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Number of items at this level: 375.

A

Abbott, LF; Angelaki, DE; Carandini, M; Churchland, AK; Dan, Y; Dayan, P; Deneve, S; ... Zador, AM; + view all Abbott, LF; Angelaki, DE; Carandini, M; Churchland, AK; Dan, Y; Dayan, P; Deneve, S; Fiete, I; Ganguli, S; Harris, KD; Häusser, M; Hofer, S; Latham, PE; Mainen, ZF; Mrsic-Flogel, T; Paninski, L; Pillow, JW; Pouget, A; Svoboda, K; Witten, IB; Zador, AM; - view fewer (2017) An International Laboratory for Systems and Computational Neuroscience. Neuron , 96 (6) pp. 1213-1218. 10.1016/j.neuron.2017.12.013.

Adam, V; Hensman, J; Sahani, M; (2016) Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference. In: Proceedings of MLSP2016. IEEE

Ahilan, Sanjeevan; (2021) Structures for Sophisticated Behaviour: Feudal Hierarchies and World Models. Doctoral thesis (Ph.D), UCL (University College London).

Ahrens, M.B.; (2009) Nonlinear encoding of sounds in the auditory cortex. Doctoral thesis , UCL (University College London).

Aitchison, L; Jegminat, J; Menendez, JA; Pfister, J-P; Pouget, A; Latham, PE; (2021) Synaptic plasticity as Bayesian inference. Nature Neuroscience , 24 pp. 565-571. 10.1038/s41593-021-00809-5.

Aitchison, L; Bang, D; Bahrami, B; Latham, PE; (2015) Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making. PLoS Computational Biology , 11 (10) , Article e1004519. 10.1371/journal.pcbi.1004519.

Aitchison, L; Corradi, N; Latham, PE; (2016) Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables. PLoS Comput Biol , 12 (12) , Article e1005110. 10.1371/journal.pcbi.1005110.

Aitchison, L; Lengyel, M; (2016) The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics. PLOS Computational Biology , 12 (12) , Article e1005186. 10.1371/journal.pcbi.1005186.

Alabdulmohsin, Ibrahim; Chiou, Nicole; D’Amour, Alexander; Gretton, Arthur; Koyejo, Sanmi; Kusner, Matt J; Pfohl, Stephen R; ... Tsai, Katherine; + view all Alabdulmohsin, Ibrahim; Chiou, Nicole; D’Amour, Alexander; Gretton, Arthur; Koyejo, Sanmi; Kusner, Matt J; Pfohl, Stephen R; Salaudeen, Olawale; Schrouff, Jessica; Tsai, Katherine; - view fewer (2023) Adapting to Latent Subgroup Shifts via Concepts and Proxies. In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. (pp. pp. 9637-9661).

Amjad, Jaweria; Lyu, Zhaoyan; Rodrigues, Miguel RD; (2021) Regression with Deep Neural Networks: Generalization Error Guarantees, Learning Algorithms, and Regularizers. In: 2021 29th European Signal Processing Conference (EUSIPCO). (pp. pp. 1481-1485). IEEE: Dublin, Ireland.

Arbel, M; Zhou, L; Gretton, A; (2021) Generalized Energy Based Models. In: Proceedings of the 9th International Conference on Learning Representations: ICLR 2021. ICLR

Arbel, M; Gretton, AL; (2018) Kernel Conditional Exponential Family. In: Proceedings of the 21st International Conference on Artifi- cial Intelligence and Statistics (AISTATS) 2018. (pp. pp. 1337-1346). PMLR

Arbel, M; Korba, A; Salim, A; Gretton, A; (2019) Maximum Mean Discrepancy Gradient Flow. In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alché-Buc, F and Fox, E and Garnett, R, (eds.) Advances in Neural Information Processing Systems 32 (NIPS 2019). NIPS Proceedingsβ: Vancouver, Canada.

Arbel, M; Sutherland, DJ; Bińkowski, M; Gretton, A; (2018) On gradient regularizers for MMD GANs. In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and Cesa-Bianchi, N and Garnett, R, (eds.) Advances in Neural Information Processing Systems 31 (NIPS 2018). NIPS Proceedings: Montreal, Canada.

Asabuki, T; Hiratani, N; Fukai, T; (2018) Interactive reservoir computing for chunking information streams. PLoS Computational Biology , 14 (10) , Article e1006400. 10.1371/journal.pcbi.1006400.

Austern, Morgane; Orbanz, Peter; (2022) Limit theorems for distributions invariant under groups of transformations. Annals of Statistics , 50 (4) pp. 1960-1991. 10.1214/21-AOS2165.

B

Bang, D; Aitchison, L; Moran, R; Herce Castanon, S; Rafiee, B; Mahmoodi, A; Lau, JYF; ... Summerfield, C; + view all Bang, D; Aitchison, L; Moran, R; Herce Castanon, S; Rafiee, B; Mahmoodi, A; Lau, JYF; Latham, PE; Bahrami, B; Summerfield, C; - view fewer (2017) Confidence matching in group decision-making. [Letter]. Nature Human Behaviour , 1 , Article 0117. 10.1038/s41562-017-0117.

Bang, D; Fusaroli, R; Tylén, K; Olsen, K; Latham, PE; Lau, JY; Roepstorff, A; ... Bahrami, B; + view all Bang, D; Fusaroli, R; Tylén, K; Olsen, K; Latham, PE; Lau, JY; Roepstorff, A; Rees, G; Frith, CD; Bahrami, B; - view fewer (2014) Does interaction matter? Testing whether a confidence heuristic can replace interaction in collective decision-making. Conscious Cogn , 26C 13 - 23. 10.1016/j.concog.2014.02.002.

Barrett, D; (2012) Computation in Balanced Networks. Doctoral thesis , UCL (University College London).

Baume, Jerome; Kanagawa, Heishiro; Gretton, Arthur; (2023) A Kernel Stein Test of Goodness of Fit for Sequential Models. In: Proceedings of the International Conference on Machine Learning. ICML Proceedings (In press).

Beiran, M; Dubreuil, AM; Valente, A; Mastrogiuseppe, F; Ostojic, S; (2021) Shaping Dynamics With Multiple Populations in Low-Rank Recurrent Networks. Neural Computation , 33 (6) pp. 1572-1615. 10.1162/neco_a_01381.

Belitski, A; Gretton, A; Magri, C; Murayama, Y; Montemurro, M; Logothetis, N; Panzeri, S; (2008) Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information. Journal of Neuroscience , 28 , Article 22.

Bennett, Tom James; (2006) Temporal cognition as a feature of working memory. Masters thesis , UCL (University College London).

Berkes, P; Turner, RE; Sahani, M; (2009) A Structured Model of Video Reproduces Primary Visual Cortical Organisation. PLOS COMPUT BIOL , 5 (9) , Article e1000495. 10.1371/journal.pcbi.1000495.

Biderman, Dan; Whiteway, Matthew R; Hurwitz, Cole; Greenspan, Nicholas; Lee, Robert S; Vishnubhotla, Ankit; Warren, Richard; ... Paninski, Liam; + view all Biderman, Dan; Whiteway, Matthew R; Hurwitz, Cole; Greenspan, Nicholas; Lee, Robert S; Vishnubhotla, Ankit; Warren, Richard; Pedraja, Federico; Noone, Dillon; Schartner, Michael M; Huntenburg, Julia M; Khanal, Anup; Meijer, Guido T; Noel, Jean-Paul; Pan-Vazquez, Alejandro; Socha, Karolina Z; Urai, Anne E; Cunningham, John P; Sawtell, Nathaniel B; Paninski, Liam; - view fewer (2024) Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools. Nature Methods , 21 (7) pp. 1316-1328. 10.1038/s41592-024-02319-1.

Biggs, Felix; Schrab, antonin; Gretton, Arthur; (2023) MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting. In: Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). (pp. pp. 1-38). NeurIPS (In press).

Bińkowski, M; Sutherland, DJ; Arbel, M; Gretton, A; (2018) Demystifying MMD GANs. In: Bengio, Yoshua and LeCun, Yann, (eds.) Proceedings of ICLR 2018 : International Conference on Learning Representations. ICLR: Vancouver, BC, Canada,.

Blank, M; Danly, B; Levush, B; Latham, P; Pershing, D; (1997) Experimental demonstration of a W-band gyroklystron amplifier. Physical Review Letters , 79 (22) 4485 - 4488. 10.1103/PhysRevLett.79.4485.

Boboeva, Vezha; Pezzotta, Alberto; Clopath, Claudia; (2021) Free recall scaling laws and short-term memory effects in a latching attractor network. Proceedings of the National Academy of Sciences of USA , 118 (49) , Article e2026092118. 10.1073/pnas.2026092118.

Bohner, G; Sahani, M; (2016) Convolutional higher order matching pursuit. In: Proceedings of MLSP2016. IEEE

Borges, Beatriz; Foroutan, Negar; Bayazit, Deniz; Sotnikova, Anna; Montariol, Syrielle; Nazaretzky, Tanya; Banaei, Mohammadreza; ... Bosselut, Antoine; + view all Borges, Beatriz; Foroutan, Negar; Bayazit, Deniz; Sotnikova, Anna; Montariol, Syrielle; Nazaretzky, Tanya; Banaei, Mohammadreza; Sakhaeirad, Alireza; Servant, Philippe; Neshaei, Seyed Parsa; Frej, Jibril; Romanou, Angelika; Weiss, Gail; Mamooler, Sepideh; Chen, Zeming; Fan, Simin; Gao, Silin; Ismayilzada, Mete; Paul, Debjit; Schwaller, Philippe; Friedli, Sacha; Jermann, Patrick; Käser, Tanja; Bosselut, Antoine; - view fewer (2024) Could ChatGPT get an engineering degree? Evaluating higher education vulnerability to AI assistants. Proceedings of the National Academy of Sciences , 121 (49) , Article e2414955121. 10.1073/pnas.2414955121.

Bozkurt, Bariscan; Deaner, Ben; Meunier, Dimitri; Xu, Liyuan; Gretton, Arthur; (2025) Density Ratio-based Proxy Causal Learning Without Density Ratios. In: Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS) 2025. PMLR: Mai Khao, Thailand. (In press).

Braun, Lukas; Dominé, Clémentine Carla Juliette; Fitzgerald, James E; Saxe, Andrew M; (2022) Exact learning dynamics of deep linear networks with prior knowledge. In: Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). NeurIPS

Broeker, Franziska; (2022) Semi-supervised categorisation: the role of feedback in human learning. Doctoral thesis (Ph.D), UCL (University College London).

Bröker, F; Marshall, L; Bestmann, S; Dayan, P; (2018) Forget-me-some: General versus special purpose models in a hierarchical probabilistic task. PLoS One , 13 (10) , Article e0205974. 10.1371/journal.pone.0205974.

C

Carrasco-Davis, Rodrigo; (2025) Principles of Optimal Learning Control in Biological and Artificial Agents. Doctoral thesis (Ph.D), UCL (University College London).

Castillo, I; Orbanz, P; (2022) Uniform estimation in stochastic block models is slow. Electronic Journal of Statistics , 16 (1) pp. 2947-3000. 10.1214/22-EJS2014.

Celikkanat, H; Moriya, H; Ogawa, T; Kauppi, J-P; Kawanabe, M; Hyvarinen, AJ; (2017) Decoding Emotional Valence from Electroencephalographic Rhythmic Activity. In: Park, KS and Kim, Y and Weiland, J, (eds.) 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - 2017. (pp. pp. 4143-4146). IEEE Press: Jeju Island, Korea.

Chadwick, Angus; Khan, Adil G; Poort, Jasper; Blot, Antonin; Hofer, Sonja B; Mrsic-Flogel, Thomas D; Sahani, Maneesh; (2022) Learning shapes cortical dynamics to enhance integration of relevant sensory input. Neuron 10.1016/j.neuron.2022.10.001. (In press).

Chambers, C; Akram, S; Adam, V; Pelofi, C; Sahani, M; Shamma, S; Pressnitzer, D; (2017) Prior context in audition informs binding and shapes simple features. Nature Communications , 8 , Article 15027. 10.1038/ncomms15027.

Chen, Y; Xu, L; Gulcehre, C; Le Paine, T; Gretton, A; de Freitas, N; Doucet, A; (2022) On Instrumental Variable Regression for Deep Offline Policy Evaluation. Journal of Machine Learning Research , 23 (302) pp. 1-40.

Chen, D.; Zhaoping, L.; (1998) A psychophysical experiment to test the efficient stereo coding theory. In: Wong, K.-Y.M and King, I. and Yeung, D.Y., (eds.) Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective. (pp. 225-235). Springer Verlag: New York, US.

Chung, AW; Pesce, E; Monti, RP; Montana, G; (2016) Classifying HCP task-fMRI networks using heat kernels. In: Proceedings of the 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE: Trento, Italy.

Chwialkowski, K; Strathmann, H; Gretton, A; (2016) A Kernel Test of Goodness of Fit. In: ICML ’16: Proceedings of the 32nd International Conference on Machine Learning. (pp. pp. 2606-2615). JMLR: Workshop and Conference Proceedings

Currin, CB; Khoza, PN; Antrobus, AD; Latham, PE; Vogels, TP; Raimondo, JV; (2019) Think: Theory for Africa. [Editorial comment]. PLoS Computational Biology , 15 (7) , Article e1007049. 10.1371/journal.pcbi.1007049.

D

Dai, B; Dai, H; Gretton, A; Song, L; Schuurmans, D; He, N; (2019) Kernel Exponential Family Estimation via Doubly Dual Embedding. In: Chaudhuri, K and Sugiyama, M, (eds.) Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. (pp. pp. 2321-2330). Proceedings of Machine Learning Research: Naha, Okinawa, Japan.

Dai, B; Liu, Z; Dai, H; He, N; Gretton, A; Le, S; Schurmaans, D; (2019) Exponential Family Estimation via Adversarial Dynamics Embedding. In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alché-Buc, F and Fox, E and Garnett, R, (eds.) Advances in Neural Information Processing Systems 32 (NIPS 2019). NIPS Proceedingsβ: Vancouver, Canada.

Domine, Clementine; Anguita, Nicolas; Proca, Alexandra; Braun, Lukas; Mediano, Pedro; Saxe, Andrew; (2025) From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks. In: Proceedings of the ICLR 2025 Conference. (pp. pp. 1-52). ICLR

Domine, Clementine C; Braun, Lukas; Fitzgerald, James E; Saxe, Andrew M; (2023) Exact learning dynamics of deep linear networks with prior knowledge. Journal of Statistical Mechanics: Theory and Experiment , 2023 (11) , Article ARTN 114004. 10.1088/1742-5468/ad01b8.

Dominé, Clémentine Carla Juliette; (2025) Balancing Learning Regimes: The Impact of Prior Knowledge on the Dynamics of Neural Representations. Doctoral thesis (Ph.D), UCL (University College London).

Dorrell, Will; Latham, Peter; Behrens, Timothy EJ; Whittington, James CR; (2023) Actionable Neural Representations: Grid Cells from Minimal Constraints. In: Proceedings of the Eleventh International Conference on Learning Representations. (pp. pp. 1-47). ICLR (In press).

Dorrell, william; Hsu, Kyle; Hollingsworth, Luke; Lee, Jin Hwa; Wu, Jiajun; Finn, Chelsea; Latham, Peter; ... Whittington, James CR; + view all Dorrell, william; Hsu, Kyle; Hollingsworth, Luke; Lee, Jin Hwa; Wu, Jiajun; Finn, Chelsea; Latham, Peter; Behrens, Timothy Edward John; Whittington, James CR; - view fewer (2025) Range, not Independence, Drives Modularity in Biologically Inspired Representations. In: Proceedings 13th International Conference on Learning Representations ICLR 2025. ICLR: Singapore, Singapore.

Dorrell, William; Hsu, Kyle; Whittington, James; Wu, Jiajun; Finn, Chelsea; (2023) Disentanglement via Latent Quantization. In: Proceedings - Advances in Neural Information Processing Systems 37 (NeurIPS 2023). NeurIPS: New Orleans, USA.

Dorrell, William; Yuffa, Maria; Latham, Peter; (2023) Meta-Learning the Inductive Bias of Simple Neural Circuits. In: Proceedings of the International Conference on Machine Learning. ICML Proceedings (In press).

Douglas, L; Zarov, I; Gourgoulias, K; Lucas, C; Hart, C; Baker, A; Sahani, M; ... Johri, S; + view all Douglas, L; Zarov, I; Gourgoulias, K; Lucas, C; Hart, C; Baker, A; Sahani, M; Perov, Y; Johri, S; - view fewer (2017) A Universal Marginalizer for Amortized Inference in Generative Models. In: Proceedings of 31st Conference on Neural Information Processing Systems (NIPS 2017),. NIPS: Long Beach, CA, USA.

Duncker, L; Sahani, M; (2021) Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings. Current Opinion in Neurobiology , 70 pp. 163-170. 10.1016/j.conb.2021.10.014.

Duncker, Lea; (2021) Dynamical structure in neural population activity. Doctoral thesis (Ph.D), UCL (University College London).

Duncker, L; Bohner, G; Boussard, J; Sahani, M; (2019) Learning interpretable continuous-time models of latent stochastic dynamical systems. In: Salakhutdinov, Ruslan and Chaudhuri, Kamalika, (eds.) Proceedings of the 36th International Conference on Machine Learning (ICML 2019). PMLR (Proceedings of Machine Learning Research): Long Beach, CA, USA.

Duncker, L; Driscoll, L; Shenoy, K; Sahani, M; Susillo, D; (2021) Organizing recurrent network dynamics by task-computation to enable continual learning. In: Advances in Neural Information Processing Systems 33. NeurIPS

Duncker, L; Sahani, M; (2018) Temporal alignment and latent Gaussian process factor inference in population spike trains. In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.) Proceedings of Conference on Neural Information Processing Systems 31 (NIPS 2018). Neural Information Processing Systems (NIPS): Montreal, Canada.

E

Elliott, LT; (2016) Bayesian nonparametric models of genetic variation. Doctoral thesis , UCL (University College London).

F

Fernandez, T; Gretton, A; Rindt, D; Sejdinovic, D; (2021) A Kernel Log-Rank Test of Independence for Right-Censored Data. Journal of the American Statistical Association 10.1080/01621459.2021.1961784.

Fernández, T; Rivera, N; (2021) A reproducing kernel Hilbert space log-rank test for the two-sample problem. Scandinavian Journal of Statistics: Theory and Applications , 48 (4) pp. 1384-1432. 10.1111/sjos.12496.

Fernández, T; Xu, W; Ditzhaus, M; Gretton, A; (2020) A kernel test for quasi-independence. In: Larochelle, H. and Ranzato, M. and Hadsell, R. and Balcan, M.F. and Lin, H., (eds.) NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems. Neural Information Processing Systems Conference: Vancouver, Canada.

Fernández, T; Gretton, A; (2019) A maximum-mean-discrepancy goodness-of-fit test for censored data. In: Chaudhuri, K and Sugiyama, M, (eds.) Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. (pp. pp. 2966-2975). Proceedings of Machine Learning Research: Naha, Okinawa, Japan.

Fernández, T; Rivera, N; (2020) Kaplan-Meier V- and U-statistics. Electronic Journal of Statistics , 14 (1) pp. 1872-1916. 10.1214/20-ejs1704.

Fernandez Aguilar, T; Gretton, A; Rivera, N; XU, W; (2020) Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data. In: Proceedings of the 37th International Conference on Machine Learning. (pp. pp. 3112-3122). PMLR: Vienna, Austria.

Fernandez Aguilar, T; Rivera, N; Teh, YW; (2016) Gaussian processes for survival analysis. In: (Proceedings) Advances in Neural Information Processing Systems 29 (NIPS 2016). NIPS Proceedings

Ferrè, ER; Sahani, M; Haggard, P; (2016) Subliminal stimulation and somatosensory signal detection. Acta Psychologica , 170 pp. 103-111. 10.1016/j.actpsy.2016.06.009.

Flesch, T; Juechems, K; Dumbalska, T; Saxe, A; Summerfield, C; (2022) Orthogonal representations for robust context-dependent task performance in brains and neural networks. [Corrigendum]. Neuron , 110 (24) pp. 4212-4219. 10.1016/j.neuron.2022.12.004.

Flesch, T; Nagy, DG; Saxe, A; Summerfield, C; (2023) Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals. PLoS Computational Biology , 19 (1) , Article e1010808. 10.1371/journal.pcbi.1010808.

Flesch, Timo; Juechems, Keno; Dumbalska, Tsvetomira; Saxe, Andrew; Summerfield, Christopher; (2022) Orthogonal representations for robust context-dependent task performance in brains and neural networks. Neuron , 110 (7) pp. 1258-1270. 10.1016/j.neuron.2022.01.005.

Flesch, Timo; Saxe, Andrew; Summerfield, Christopher; (2023) Continual task learning in natural and artificial agents. Trends in Neurosciences 10.1016/j.tins.2022.12.006. (In press).

Franz, Silvio; Sclocchi, Antonio; Urbani, Pierfrancesco; (2021) Surfing on minima of isostatic landscapes: avalanches and unjamming transition. Journal of Statistical Mechanics: Theory and Experiment , 2021 (2) , Article 023208. 10.1088/1742-5468/abdc16.

Franz, Silvio; Sclocchi, Antonio; Urbani, Pierfrancesco; (2020) Critical energy landscape of linear soft spheres. SciPost Physics , 9 (1) , Article 012. 10.21468/scipostphys.9.1.012.

G

Galashov, A; Titsias, MK; György, A; Lyle, C; Pascanu, R; Teh, YW; Sahani, M; (2024) Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. In: Proceedings of the Advances in Neural Information Processing Systems 37 (NeurIPS 2024). NeurIPS

Galashov, Alexandre; De Bortoli, Valentin; Gretton, Arthur; (2025) Deep MMD Gradient Flow without adversarial training. In: Proceedings 13th International Conference on Learning Representations ICLR 2025. ICLR: Singapore, Singapore.

Galgali, AR; Sahani, M; Mante, V; (2023) Residual dynamics resolves recurrent contributions to neural computation. Nature Neuroscience , 26 pp. 326-338. 10.1038/s41593-022-01230-2.

Garrido, MI; Sahani, M; Dolan, RJ; (2013) Outlier responses reflect sensitivity to statistical structure in the human brain. PLoS Computational Biology , 9 (3) , Article e1002999. 10.1371/journal.pcbi.1002999.

Gerace, Federica; Saglietti, Luca; Mannelli, Stefano Sarao; Saxe, Andrew; Zdeborova, Lenka; (2022) Probing transfer learning with a model of synthetic correlated datasets. Machine Learning: Science and Technology , 3 (1) , Article 015030. 10.1088/2632-2153/ac4f3f.

Glaser, P; Arbel, M; Gretton, A; (2021) KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. In: Ranzato, M and Beygelzimer, A and Dauphin, Y and Liang, PS and Wortman Vaughan, J, (eds.) Advances in Neural Information Processing Systems 34 (NeurIPS 2021). (pp. pp. 8018-8031). NeurIPS

Glaser, P; Widmann, D; Lindsten, F; Gretton, A; (2023) Fast and Scalable Score-Based Kernel Calibration Tests. In: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence. (pp. pp. 691-700). PMLR: Pittsburgh, PA, USA.

Glaser, Pierre; Huang, Kevin Han; Gretton, Arthur; (2024) Near-Optimality of Contrastive Divergence Algorithms. In: Globerson, A and Mackey, L and Belgrave, D and Fan, A and Paquet, U and Tomczak, J and Zhang, C, (eds.) Advances in Neural Information Processing Systems 37 (NeurIPS 2024). (pp. pp. 1-55). NeurIPS: Vancouver, BC, Canada.

Gonçalves, PJ; Arrenberg, AB; Hablitzel, B; Baier, H; Machens, CK; (2014) Optogenetic perturbations reveal the dynamics of an oculomotor integrator. Frontiers in Neural Circuits , 8 , Article 10. 10.3389/fncir.2014.00010.

Gonzalez Troncoso, X.; (2006) The role of corner angle in visual physiology and brightness perception. Doctoral thesis , University of London.

Görür, D; (2007) Nonparametric Bayesian Discrete Latent Variable Models for Unsupervised Learning. Doctoral thesis , UNSPECIFIED.

Gothner, T; Gonçalves, PJ; Sahani, M; Linden, JF; Hildebrandt, KJ; (2021) Sustained Activation of PV+ Interneurons in Core Auditory Cortex Enables Robust Divisive Gain Control for Complex and Naturalistic Stimuli. Cerebral Cortex , 31 (5) pp. 2364-2381. 10.1093/cercor/bhaa347.

Grabska-Barwińska, A; Barthelmé, S; Beck, J; Mainen, ZF; Pouget, A; Latham, PE; (2017) A probabilistic approach to demixing odors. Nature Neuroscience , 20 (1) pp. 98-106. 10.1038/nn.4444.

Grabska-Barwińska, A; Latham, PE; (2014) How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes? Journal of Computational Neuroscience , 36 (3) pp. 469-481. 10.1007/s10827-013-0481-5.

Gretton, A; (2003) Kernel Methods for Classification and Signal Separation (PhD thesis). Doctoral thesis , UNSPECIFIED.

Gretton, A; Borgwardt, K; Rasch, M; Schoelkopf, B; Smola, A; (2012) A Kernel Two-Sample Test. Journal of Machine Learning Research , 13 pp. 723-773.

Gunderson, lee M; Bravo-Hermsdorff, Gecia; Orbanz, Peter; (2023) The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models. In: Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). (pp. pp. 1-11). NeurIPS (In press).

H

Hehrmann, Phillipp; (2018) Pitch perception as probabilistic inference. Doctoral thesis (Ph.D), UCL (University College London).

Heller, K.A.; (2008) Efficient Bayesian methods for clustering. Doctoral thesis , University of London.

Higgins, I; Sonnerat, N; Matthey, L; Pal, A; Burgess, CP; Bošnjak, M; Shanahan, M; ... Lerchner, A; + view all Higgins, I; Sonnerat, N; Matthey, L; Pal, A; Burgess, CP; Bošnjak, M; Shanahan, M; Botvinick, M; Hassabis, D; Lerchner, A; - view fewer (2018) SCAN: Learning Hierarchical Compositional Visual Concepts. In: Bengio, Y and LeCun, Y, (eds.) Proceedings of the Sixth International Conference on Learning Representations (ICLR 2018). International Conference on Learning Representations (ICLR): Vancouver, Canada.

Hildebrandt, KJ; Sahani, M; Linden, JF; (2017) The Impact of Anesthetic State on Spike-Sorting Success in the Cortex: A Comparison of Ketamine and Urethane Anesthesia. Frontiers in Neural Circuits , 11 , Article 95. 10.3389/fncir.2017.00095.

Hiratani, Naoki; Latham, Peter E; (2022) Developmental and evolutionary constraints on olfactory circuit selection. Proceedings of the National Academy of Sciences of the United States of America , 119 (11) , Article e2100600119. 10.1073/pnas.2100600119.

Hiratani, naoki; Mehta, Yash; Lillicrap, Timothy; Latham, Peter; (2023) On the Stability and Scalability of Node Perturbation Learning. In: Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). Neural Information Processing Systems (NeurIPS) (In press).

Hiratani, N; Fukai, T; (2018) Redundancy in synaptic connections enables neurons to learn optimally. Proceedings of the National Academy of Sciences , 115 (29) E6871-E6879. 10.1073/pnas.1803274115.

Hiratani, N; Fukai, T; (2017) Detailed dendritic excitatory/inhibitory balance through heterosynaptic spike-timing-dependent plasticity. Journal of Neuroscience , 37 (50) pp. 12106-12122. 10.1523/JNEUROSCI.0027-17.2017.

Hiratani, N; Fukai, T; (2016) Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity. Frontiers in Neural Circuits , 10 p. 41. 10.3389/fncir.2016.00041.

Hiratani, N; Latham, PE; (2020) Rapid Bayesian learning in the mammalian olfactory system. Nature Communications , 11 (1) , Article 3845. 10.1038/s41467-020-17490-0.

Hromadka, Samo; Sahani, Maneesh; (2024) Modelling Latent Dynamical Systems with Recognition-Parametrised Models. In: Proceedings of the Workshop: Structured Probabilistic Inference and Generative Modeling. ICML (In press).

Huang, Kevin Han; (2025) Universality beyond the classical asymptotic regime. Doctoral thesis (Ph.D), UCL (University College London).

Huang, Kevin Han; Liu, Xing; Duncan, Andrew B; Gandy, Axel; (2023) A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing. In: Neu, Gergely and Rosasco, Lorenzo, (eds.) Proceedings of Thirty Sixth Conference on Learning Theory. (pp. pp. 3827-3918). Proceedings of Machine Learning Research (PMLR): Bangalore, India.

Hyvarinen, AJ; Morioka, H; (2017) Nonlinear ICA of temporally dependent stationary sources. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research: Fort Lauderdale, FL, USA.

I

International Brain Laboratory; Bonacchi, Niccolò; Chapuis, Gaelle A; Churchland, Anne K; DeWitt, Eric EJ; Faulkner, Mayo; Harris, Kenneth D; ... Wells, Miles J; + view all International Brain Laboratory; Bonacchi, Niccolò; Chapuis, Gaelle A; Churchland, Anne K; DeWitt, Eric EJ; Faulkner, Mayo; Harris, Kenneth D; Huntenburg, Julia M; Hunter, Max; Laranjeira, Inês C; Rossant, Cyrille; Sasaki, Maho; Schartner, Michael M; Shen, Shan; Steinmetz, Nicholas A; Walker, Edgar Y; West, Steven J; Winter, Olivier; Wells, Miles J; - view fewer (2023) A modular architecture for organizing, processing and sharing neurophysiology data. Nature Methods , 20 pp. 403-407. 10.1038/s41592-022-01742-6.

J

Jarvis, Devon; Klein, Richard; Rosman, Benjamin; Saxe, Andrew; (2023) On The Specialization of Neural Modules. In: Proceedings of the Eleventh International Conference on Learning Representations. (pp. pp. 1-31). ICLR (In press).

Jarvis, Devon; Klein, Richard; Rosman, Benjamin; Saxe, Andrew M; (2025) Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks. In: Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025). (pp. pp. 1-35). OpenReview.net: Singapore, Singapore.

Jarvis, Devon; Lee, Sebastian; Domine, Clementine; Saxe, andrew; Sarao Mannelli, Stefano; (2025) A Theory of Initialisation's Impact on Specialisation. In: Proceedings of the ICLR 2025 Conference. (pp. pp. 1-29). ICLR

Jativa Vega, Sofia Alejandra; (2020) A second look at memory: Different Approaches to Understanding Diversity in Memory and Cognition. Doctoral thesis (Ph.D), UCL (University College London).

Jeni, LA; Lőrincz, A; Nagy, T; Palotai, Z; Sebők, J; Szabó, Z; Takács, D; (2012) 3D shape estimation in video sequences provides high precision evaluation of facial expressions. Image and Vision Computing , 30 (10) 785 - 795. 10.1016/j.imavis.2012.02.003.

Jerjian, SJ; Sahani, M; Kraskov, A; (2020) Movement initiation and grasp representation in premotor and primary motor cortex mirror neurons. eLife , 9 , Article e54139. 10.7554/eLife.54139. (In press).

Jitkrittum, W; Szabo, Z; Chwialkowski, K; Gretton, A; (2016) Interpretable Distribution Features with Maximum Testing Power. ArXiv

Jitkrittum, W; Gretton, A; Heess, N; Eslami, A; Lakshminarayanan, B; Sejdinovic, D; Szabo, Z; (2015) Just-In-Time Kernel Regression for Expectation Propagation. In: Proceeding of Large-Scale Kernel Learning: Challenges and New Opportunities workshop. : Lille, France. (In press).

Jitkrittum, W; Gretton, A; Heess, N; Eslami, A; Lakshminarayanan, B; Sejdinovic, D; Szabo, Z; (2015) Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. Presented at: Data, Learning and Inference workshop (DALI), La Palma (Canaries, Spain).

Jitkrittum, W; Gretton, A; Heess, N; Eslami, SMA; Lakshminarayanan, B; Sejdinovic, D; Szabó, Z; (2015) Kernel-based just-in-time learning for passing expectation propagation messages. In: Meila, Marina and Heskes, Tom, (eds.) Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI'15 ). (pp. pp. 405-414). AUAI Press: Virginia, USA.

Jitkrittum, W; Gretton, AL; Heess, N; Eslami, SMA; Lakshminarayanan, B; Sejdinovic, D; Szabó, Z; (2015) Kernel-Based Just-In-Time Learning For Passing Expectation Propagation Messages. Presented at: International Conference on Machine Learning (ICML) - Large-Scale Kernel Learning: Challenges and New Opportunities workshop, Lille, France.

Jitkrittum, W; Sangkloy, P; Schölkopf, B; Kanagawa, H; Hays, J; Gretton, A; (2018) Informative features for model comparison. In: Advances in Neural Information Processing Systems 31 (NIPS 2018). (pp. pp. 808-819). Neural Information Processing Systems Foundation, Inc.: Montréal, Canada.

Jitkrittum, W; Szabo, Z; Chwialkowski, K; Gretton, A; (2016) Distinguishing distributions with interpretable features. Presented at: International Conference on Machine Learning (ICML): Data-Efficient Machine Learning workshop, New York, USA.

Jitkrittum, W; Szabo, Z; Chwialkowski, K; Gretton, A; (2016) Distinguishing distributions with interpretable features. In: ICML 2016 Workshop on Data-Efficient Machine Learning. : New York, USA.

Jitkrittum, W; Szabo, Z; Chwialkowski, K; Gretton, A; (2016) Distinguishing distributions with interpretable features. Presented at: International Conference on Machine Learning (ICML): Data-Efficient Machine Learning workshop, New York, USA.

Jitkrittum, W; Szabo, Z; Gretton, A; (2017) An Adaptive Test of Independence with Analytic Kernel Embeddings. In: Proceedings of ICML 2017. (pp. pp. 1742-1751). JMLR: Sydney, Australia.

Jitkrittum, W; Xu, W; Szabó, Z; Fukumizu, K; Gretton, A; (2017) A linear-time kernel goodness-of-fit test. In: Guyon, I and Luxburg, U.V. and Bengio, S and Wallach and, H and Furges, R and Vishwanathan, S and Garnett., R, (eds.) Proceedings of Advances in Neural Information Processing Systems 30 (NIPS 2017). NIPS Foundation: CA, USA. (In press).

Jo (Xu), Ritsugen (Liyuan); (2024) Feature Mean Embeddings for Causal Inference. Doctoral thesis (Ph.D), UCL (University College London).

Juechems, K; Saxe, A; (2021) Inferring Actions, Intentions, and Causal Relations in a Deep Neural Network. In: Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. (pp. pp. 1056-1062).

K

Kanagawa, Heishiro; (2022) Statistical Model Evaluation Using Reproducing Kernels and Stein’s method. Doctoral thesis (Ph.D), UCL (University College London).

Kanagawa, Heishiro; Jitkrittum, Wittawat; Mackey, Lester; Fukumizu, Kenji; Gretton, Arthur; (2023) A kernel Stein test for comparing latent variable models. Journal of the Royal Statistical Society: Statistical Methodology Series B , Article qkad050. 10.1093/jrsssb/qkad050. (In press).

Khan, AG; Poort, J; Chadwick, A; Blot, A; Sahani, M; Mrsic-Flogel, TD; Hofer, SB; (2018) Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex. Nature Neuroscience , 21 (6) pp. 851-859. 10.1038/s41593-018-0143-z.

Khemakhem, Ilyes; (2022) Advances in identifiability of nonlinear probabilistic models. Doctoral thesis (Ph.D), UCL (University College London).

Kim, Juno; Meunier, Dimitri; Gretton, Arthur; Suzuki, Taiji; Li, Zhu; (2025) Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression. In: Proceedings 13th International Conference on Learning Representations ICLR 2025. ICLR: Singapore, Singapore.

Koch, LM; Schürch, CM; Gretton, A; Berens, P; (2022) Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection. In: Proceedings of The 5th International Conference on Medical Imaging with Deep Learning. (pp. pp. 726-740). Proceedings of Machine Learning Research (PMLR)

Korba, A; Salim, A; Arbel, M; Luise, G; Gretton, A; (2020) A Non-Asymptotic Analysis for Stein Variational Gradient Descent. In: Larochelle, H. and Ranzato, M. and Hadsell, R. and Balcan, M.F. and Lin, H., (eds.) NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems. Neural Information Processing Systems Conference: Vancouver, Canada.

Korshunova, I; Degrave, J; Huszár, F; Gal, Y; Gretton, A; Dambre, J; (2018) Bruno: A deep recurrent model for exchangeable data. In: Advances in Neural Information Processing Systems 31 (NIPS 2018). (pp. pp. 7190-7198). Neural Information Processing Systems Foundation, Inc.: Montréal, Canada.

Korshunova, I; Gal, Y; Gretton, A; Dambre, J; (2020) Conditional BRUNO: A neural process for exchangeable labelled data. Neurocomputing , 416 pp. 305-309. 10.1016/j.neucom.2019.11.108.

Korshunova, I; Gal, Y; Gretton, A; Dambre, J; (2019) Conditional BRUNO: A neural process for exchangeable labelled data. In: ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. European Symposium on Artificial Neural Networks (ESANN): Bruges, Belgium.

Krueger, K.A.; (2011) Sequential learning in the form of shaping as a source of cognitive flexibility. Doctoral thesis , UCL (University College London).

Kunin, Daniel; Raventos, Allan; Domine, Clementine; Chen, Feng; Klindt, David; Saxe, Andrew; Ganguli, Surya; (2024) Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning. In: Advances in Neural Information Processing Systems 37 (NeurIPS 2024). NeurIPS Proceedings

L

Lakshminarayanan, B; (2016) Decision trees and forests: a probabilistic perspective. Doctoral thesis , UCL (University College London).

Latham, P; Lawson, W; Irwin, V; Hogan, B; Nusinovich, G; Matthews, H; Flaherty, M; (1994) High power operation of an X-band gyrotwistron. Physical Review Letters , 72 (23) 3730 - 3733. 10.1103/PhysRevLett.72.3730.

Latham, P; Levush, B; Antonsen, T; Metzler, N; (1991) Harmonic operation of a free-electron laser. Physical Review Letters , 66 (11) 1442 - 1445. 10.1103/PhysRevLett.66.1442.

Latham, PE; (2017) Correlations demystified. Nature Neuroscience , 20 (1) pp. 6-8. 10.1038/nn.4455.

Lawson, W; Calame, J; Hogan, B; Latham, P; Read, M; Granatstein, V; Reiser, M; Lawson, W; Calame, J; Hogan, B; Latham, P; Read, M; Granatstein, V; Reiser, M; Striffler, C; - view fewer (1991) Efficient operation of a high-power X-band gyroklystron. Physical Review Letters , 67 (4) 520 - 523. 10.1103/PhysRevLett.67.520.

Lawson, W; Matthews, H; Lee, M; Calame, J; Hogan, B; Cheng, J; Latham, P; ... Reiser, M; + view all Lawson, W; Matthews, H; Lee, M; Calame, J; Hogan, B; Cheng, J; Latham, P; Granatstein, V; Reiser, M; - view fewer (1993) High-power operation of a K-band second harmonic gyroklystron. Physical Review Letters , 71 (3) 456 - 459. 10.1103/PhysRevLett.71.456.

Lee, S; Goldt, S; Saxe, A; (2021) Continual Learning in the Teacher-Student Setup: Impact of Task Similarity. In: Meila, M and Zhang, T, (eds.) Proceedings of the 38th International Conference on Machine Learning. (pp. pp. 6109-6119). MLResearch Press

Lee, S; Mannelli, SS; Clopath, C; Goldt, S; Saxe, A; (2022) Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation. In: Proceedings of the 39th International Conference on Machine Learning. (pp. pp. 12455-12477). PMLR

Lee, S; Mannelli, SS; Clopath, C; Goldt, S; Saxe, AM; (2022) Maslow’s Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation. In: Proceedings of the 39th International Conference on Machine Learning, PMLR. Proceedings of Machine Learning Research (PMLR)

Li, Y; Pogodin, R; Sutherland, DJ; Gretton, A; (2021) Self-Supervised Learning with Kernel Dependence Maximization. In: Advances in Neural Information Processing Systems 34 (NeurIPS 2021). (pp. pp. 15543-15556). NeurIPS

Li, Zhu; Meunier, D; Mollenhauer, Mattes; Gretton, A; (2022) Optimal Rates for Regularized Conditional Mean Embedding Learning. In: NeurIPS Proceedings: Advances in Neural Information Processing Systems 35 (NeurIPS 2022). NeurIPS

Li, Zhu; Meunier, Dimitri; Mollenhauer, Mattes; Gretton, Arthur; (2024) Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm. Journal of Machine Learning Research (JMLR) , 25 (181) pp. 1-51.

Li, W; Sahani, M; (2019) A neurally plausible model for online recognition and postdiction. In: (Proceedings) Advances in Neural Information Processing Systems 32 (NIPS 2019). NIPS Proceedings

Li, Z.; (1998) Pre-attentive segmentation in the primary visual cortex. (CBCL Papers 153 ). Center for Biological & Computational Learning (CBCL), Massachusetts Institute of Technology: Cambridge, US.

Li, Z.; (1997) Visual segmentation without classification in a model of the primary visual cortex. (CBCL Papers 153 ). Center for Biological & Computational Learning (CBCL), Massachusetts Institute of Technology: Cambridge, US.

Li, ZP; Dayan, P; (1999) Computational differences between asymmetrical and symmetrical networks. NETWORK-COMP NEURAL , 10 (1) 59 - 77.

Lieder, I; Adam, V; Frenkel, O; Jaffe-Dax, S; Sahani, M; Ahissar, M; (2019) Perceptual bias reveals slow-updating in autism and fast-forgetting in dyslexia. Nature Neuroscience , 22 (2) pp. 256-264. 10.1038/s41593-018-0308-9.

Lin, I-Chun; Okun, Michael; Carandini, Matteo; Harris, Kenneth D; (2020) Equations governing dynamics of excitation and inhibition in the mouse corticothalamic network. BioRxiv: Cold Spring Harbor, NY, USA.

Liu, F; Xu, W; Lu, J; Zhang, G; Gretton, A; Sutherland, DJ; (2020) Learning deep kernels for non-parametric two-sample tests. In: Proceedings of the 37th International Conference on Machine Learning. (pp. pp. 6272-6282).

Lomelí, M; Rowland, M; Gretton, A; Ghahramani, Z; (2019) Antithetic and Monte Carlo kernel estimators for partial rankings. Statistics and Computing , 29 pp. 1127-1147. 10.1007/s11222-019-09859-z.

Lomelí-García, MD; (2017) General Bayesian inference schemes in infinite mixture models. Doctoral thesis , UCL (University College London).

Lorenz, R; Monti, RP; Hampshire, A; Koush, Y; Anagnostopoulos, C; Faisal, AA; Sharp, D; ... Violante, IR; + view all Lorenz, R; Monti, RP; Hampshire, A; Koush, Y; Anagnostopoulos, C; Faisal, AA; Sharp, D; Montana, G; Leech, R; Violante, IR; - view fewer (2016) Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization. In: Proceedings of the 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE: Trento, Italy.

Lorenz, R; Monti, RP; Violante, IR; Anagnostopoulos, C; Faisal, AA; Montana, G; Leech, R; (2016) The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI. NeuroImage , 129 pp. 320-334. 10.1016/j.neuroimage.2016.01.032.

Lorenz, R; Simmons, LE; Monti, RP; Arthur, JL; Limal, S; Laakso, I; Leech, R; Lorenz, R; Simmons, LE; Monti, RP; Arthur, JL; Limal, S; Laakso, I; Leech, R; Violante, IR; - view fewer (2019) Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization. Brain Stimulation , 12 (6) pp. 1484-1489. 10.1016/j.brs.2019.07.003.

Lorenz, R; Violante, IR; Monti, RP; Montana, G; Hampshire, A; Leech, R; (2018) Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization. Nature Communications , 9 (1) , Article 1227. 10.1038/s41467-018-03657-3.

Lőrincz, A; Jeni, L; Szabo, Z; Cohn, J; Kanade, T; (2013) Emotional expression classification using time-series kernels. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). (pp. 889 - 895). IEEE

Lőrincz, A; Szabo, Z; (2007) Neurally Plausible, Non-combinatorial Iterative Independent Process Analysis. Neurocomputing - Letters , 70 (7-9) 1569 - 1573. 10.1016/j.neucom.2006.10.145.

Löwe, Anika T; Touzo, Léo; Muhle-Karbe, Paul S; Saxe, Andrew M; Summerfield, Christopher; Schuck, Nicolas W; (2024) Abrupt and spontaneous strategy switches emerge in simple regularised neural networks. PLoS Computational Biology , 20 (10) , Article e1012505. 10.1371/journal.pcbi.1012505.

M

Maei, H.; (2005) How can realistic networks process time-varying signals? Masters thesis , University of London.

Manto, Mario; Argyropoulos, Georgios PD; Bocci, Tommaso; Celnik, Pablo A; Corben, Louise A; Guidetti, Matteo; Koch, Giacomo; ... Ferrucci, Roberta; + view all Manto, Mario; Argyropoulos, Georgios PD; Bocci, Tommaso; Celnik, Pablo A; Corben, Louise A; Guidetti, Matteo; Koch, Giacomo; Priori, Alberto; Rothwell, John C; Sadnicka, Anna; Spampinato, Danny; Ugawa, Yoshikazu; Wessel, Maximilian J; Ferrucci, Roberta; - view fewer (2022) Consensus Paper: Novel Directions and Next Steps of Non-invasive Brain Stimulation of the Cerebellum in Health and Disease. Cerebellum , 21 (6) pp. 1092-1122. 10.1007/s12311-021-01344-6.

Marx, A; Gretton, A; Mooij, JM; (2021) A Weaker Faithfulness Assumption based on Triple Interactions. In: Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. (pp. pp. 451-460). Proceedings of Machine Learning Research: Online conference.

Masís, J; Chapman, T; Rhee, JY; Cox, DD; Saxe, AM; (2023) Strategically managing learning during perceptual decision making. eLife , 12 , Article e64978. 10.7554/eLife.64978.

Mastouri, Afsaneh; Zhu, Yuchen; Gultchin, Limor; Korba, Anna; Silva, Ricardo; Kusner, Matt J; Gretton, Arthur; Mastouri, Afsaneh; Zhu, Yuchen; Gultchin, Limor; Korba, Anna; Silva, Ricardo; Kusner, Matt J; Gretton, Arthur; Muandet, Krikamol; - view fewer (2021) Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. In: Meila, M and Zhang, T, (eds.) Proceedings of the 38 th International Conference on Machine Learning. (pp. pp. 1-12). PMLR

Mastrogiuseppe, Francesca; Hiratani, Naoki; Latham, Peter; (2023) Evolution of neural activity in circuits bridging sensory and abstract knowledge. eLife , 12 , Article e79908. 10.7554/eLife.79908.

Mastrogiuseppe, F; Ostojic, S; (2019) A Geometrical Analysis of Global Stability in Trained Feedback Networks. Neural Computation , 31 (6) pp. 1139-1182. 10.1162/neco_a_01187.

Mastrogiuseppe, F; Ostojic, S; (2017) Intrinsically-generated fluctuating activity in excitatory-inhibitory networks. PLoS Computational Biology , 13 (4) , Article e1005498. 10.1371/journal.pcbi.1005498.

Matsuo, Yutaka; LeCun, Yann; Sahani, Maneesh; Precup, Doina; Silver, David; Sugiyama, Masashi; Uchibe, Eiji; Matsuo, Yutaka; LeCun, Yann; Sahani, Maneesh; Precup, Doina; Silver, David; Sugiyama, Masashi; Uchibe, Eiji; Morimoto, Jun; - view fewer (2022) Deep learning, reinforcement learning, and world models. Neural Networks , 152 pp. 267-275. 10.1016/j.neunet.2022.03.037.

Matthey-De-L'Endroit, Loïc; (2019) Palimpsest Working Memory. Doctoral thesis (Ph.D), UCL (University College London).

Meunier, Dimitri; Alquier, Pierre; (2021) Meta-Strategy for Learning Tuning Parameters with Guarantees. Entropy , 23 (10) , Article 1257. 10.3390/e23101257.

Meunier, Dimitri; Li, Zhu; Gretton, Arthur; Kpotufe, Samory; (2025) Nonlinear Meta-learning Can Guarantee Faster Rates. SIAM Journal on Mathematics of Data Science , 7 (4) pp. 1594-1615. 10.1137/24m1662977.

Meunier, Dimitri; Pontil, Massimiliano; Ciliberto, Carlo; (2022) Distribution Regression with Sliced Wasserstein Kernels. In: Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan, (eds.) Proceedings of the 39th International Conference on Machine Learning. (pp. pp. 15501-15523). Proceedings of Machine Learning Research (PMLR): Baltimore, Maryland, USA.

Meunier, Dimitri; Shen, Zikai; Mollenhauer, Mattes; Gretton, Arthur; Li, Zhu; (2024) Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms. In: Globerson, A and Mackey, L and Belgrave, D and Fan, A and Paquet, U and Tomczak, J and Zhang, C, (eds.) Advances in Neural Information Processing Systems 37 (NeurIPS 2024). (pp. pp. 1-46). NeurIPS: Vancouver, BC, Canada.

Meyer, AF; Poort, J; O'Keefe, J; Sahani, M; Linden, JF; (2018) A Head-Mounted Camera System Integrates Detailed Behavioral Monitoring with Multichannel Electrophysiology in Freely Moving Mice. Neuron , 100 (1) pp. 46-60. 10.1016/j.neuron.2018.09.020.

Meyer, AF; Williamson, RS; Linden, JF; Sahani, M; (2017) Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation. Front Syst Neurosci , 10 , Article 109. 10.3389/fnsys.2016.00109.

Mirramezani, Mehran; Meeussen, Anne; Bertoldi, Katia; Orbanz, Peter; Adam, Ryan P; (2025) Designing Mechanical Meta-Materials by Learning Equivariant Flows. In: 13th International Conference on Learning Representations ICLR 2025. (pp. p. 7548). ICLR: Singapore, Singapore. (In press).

Moazzezi, R.; (2011) Change-based population coding. Doctoral thesis , UCL (University College London).

Momennejad, I; Russek, EM; Cheong, JH; Botvinick, MM; Daw, ND; Gershman, SJ; (2017) The successor representation in human reinforcement learning. Nature Human Behaviour , 1 (9) pp. 680-692. 10.1038/s41562-017-0180-8.

Monti, R; Lorenz, R; Leech, R; Anagnostopoulos, C; Montana, G; (2016) Text-mining the neurosynth corpus using deep boltzmann machines. In: Proceedings of the 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI) 2016. IEEE: Trento, Italy.

Monti, RP; Anagnostopoulos, C; Montana, G; (2018) Adaptive regularization for Lasso models in the context of non-stationary data streams. Statistical Analysis and Data Mining , 11 (5) , Article 11390. 10.1002/sam.11390.

Monti, RP; Anagnostopoulos, C; Montana, G; (2017) Learning population and subject-specific brain connectivity networks via mixed neighborhood selection. Annals of Applied Statistics , 11 (4) pp. 2142-2164. 10.1214/17-AOAS1067.

Monti, RP; Hyvärinen, A; (2018) A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data. In: Proceedings of Conference on Uncertainty in Artificial Intelligence - UAI 2018. Uncertainty in Artificial Intelligence (UAI): USA.

Monti, RP; Lorenz, R; Braga, RM; Anagnostopoulos, C; Leech, R; Montana, G; (2017) Real-time estimation of dynamic functional connectivity networks. Human Brain Mapping , 38 (1) pp. 202-220. 10.1002/hbm.23355.

Monti, RP; Lorenz, R; Hellyer, P; Leech, R; Anagnostopoulos, C; Montana, G; (2017) Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods. Frontiers in Computational Neuroscience , 11 , Article 14. 10.3389/fncom.2017.00014.

Monti, RP; Tootoonian, S; Cao, R; (2018) Avoiding degradation in deep feed-forward networks by phasing out skip-connections. In: Proceedings of the International Conference on Artificial Neural Networks and Machine Learning : ICANN 2018. (pp. pp. 447-456). Springer: Rhodes, Greece.

Monti, RP; Zhang, K; Hyvärinen, A; (2019) Causal discovery with general non-linear relationships using non-linear ICA. In: Proceedings of the Thirty-Fifth Conference (2019), Uncertainty in Artificial Intelligence. (pp. p. 45). AUAI: Tel Aviv, Israel.

Moskovitz, T; Arbel, M; Huszar, F; Gretton, A; (2021) EFFICIENT WASSERSTEIN NATURAL GRADIENTS FOR REINFORCEMENT LEARNING. In: Proceedings of the 9th International Conference on Learning Representations: ICLR 2021. ICLR: Virtual conference.

Moskovitz, T; Kao, C; Sahani, M; Botvinick, MM; (2023) Minimum Description Length Control. In: 11th International Conference on Learning Representations, ICLR 2023. (pp. pp. 1-31). ICLR (International Conference on Learning Representations)

Moskovitz, T; Wilson, SR; Sahani, M; (2022) A FIRST-OCCUPANCY REPRESENTATION FOR REINFORCEMENT LEARNING. In: Proceedings of the The Tenth International Conference on Learning Representations ICLR 2022. ICLR

Moskovitz, Ted; Arbel, Michael; Huszar, Ferenc; Gretton, Arthur; (2021) Efficient Wasserstein Natural Gradients for Reinforcement Learning. In: ICLR 2021 - 9th International Conference on Learning Representations. ICLR

Moskovitz, Ted; Hromadka, Samo; Touati, Ahmed; Borsa, Diana; Sahani, Maneesh; (2023) A State Representation for Diminishing Rewards. In: Proceedings of the Thirty-seventh Annual Conference on Neural Information Processing Systems. (pp. pp. 1-38). NeurIPS: San Diego, CA, USA.

Moskovitz, Theodore; O'Donoghue, Brendan; Veeriah, Vivek; Flennerhag, Sebastian; Singh, Satinder; Zahavy, Tom; (2023) ReLOAD: reinforcement learning with optimistic ascent-descent for last-iterate convergence in constrained MDPs. In: Proceedings of the 40 th International Conference on Machine Learning. (pp. pp. 25303-25336). PMLR 202: Honolulu, Hawaii, USA.

Moskovitz, Theodore Harris; (2025) Structure, Learning, & Composition: Multitask Reinforcement Learning in Brains and Machines. Doctoral thesis (Ph.D), UCL (University College London).

Muandet, K; Sriperumbudur, B; Fukumizu, K; Gretton, A; Schölkopf, B; (2016) Kernel Mean Shrinkage Estimators. Journal of Machine Learning Research , 17 , Article 48.

N

Najafi, F; Elsayed, GF; Cao, R; Pnevmatikakis, E; Latham, PE; Cunningham, JP; Churchland, AK; (2020) Excitatory and Inhibitory Subnetworks Are Equally Selective during Decision-Making and Emerge Simultaneously during Learning. Neuron , 105 (1) 165-179.e8. 10.1016/j.neuron.2019.09.045.

Navajas, J; Bahrami, B; Latham, PE; (2016) Post-decisional accounts of biases in confidence. Current Opinion in Behavioral Sciences , 11 pp. 55-60. 10.1016/j.cobeha.2016.05.005.

Navajas, J; Hindocha, C; Foda, H; Keramati, M; Latham, PE; Bahrami, B; (2017) The idiosyncratic nature of confidence. Nature Human Behaviour , 1 pp. 810-818. 10.1038/s41562-017-0215-1.

Nelli, Stephanie; Braun, Lukas; Dumbalska, Tsvetomira; Saxe, Andrew; Summerfield, Christopher; (2023) Neural knowledge assembly in humans and neural networks. Neuron 10.1016/j.neuron.2023.02.014. (In press).

Nishiyama, Y; Kanagawa, M; Gretton, A; Fukumizu, K; (2022) Model-based kernel sum rule: kernel Bayesian inference with probabilistic model. Machine Learning , 109 (5) pp. 939-972. 10.1007/s10994-019-05852-9.

O

O'Shea, DJ; Trautmann, E; Chandrasekaran, C; Stavisky, S; Kao, JC; Sahani, M; Ryu, S; ... Shenoy, KV; + view all O'Shea, DJ; Trautmann, E; Chandrasekaran, C; Stavisky, S; Kao, JC; Sahani, M; Ryu, S; Deisseroth, K; Shenoy, KV; - view fewer (2017) The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces. Experimental Neurology , 287 (Pt 4) pp. 437-451. 10.1016/j.expneurol.2016.08.003.

P

Pachitariu, M; Lyamzin, DR; Sahani, M; Lesica, NA; (2015) State-dependent population coding in primary auditory cortex. J Neurosci , 35 (5) pp. 2058-2073. 10.1523/JNEUROSCI.3318-14.2015.

Panzeri, S; Harvey, CD; Piasini, E; Latham, PE; Fellin, T; (2017) Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior. Neuron , 93 (3) pp. 491-507. 10.1016/j.neuron.2016.12.036.

Park, M; Jitkrittum, W; Qamar, A; Szabo, Z; Buesing, L; Sahani, M; (2015) Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM). Presented at: Quinquennial Review Symposium, London, United Kingdom.

Park, M; Jitkrittum, W; Qamar, A; Szabo, Z; Buesing, L; Sahani, M; (2014) Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). In: Advances in Neural Information Processing Systems 28 (NIPS 2015). Neural Information Processing Systems Foundation: Montreal, Canada.

Pegoraro, Marco; Domine, Clementine; Rodola, Emanuele; Velickovic, Petar; Deac, Andreea; (2024) Geometric epitope and paratope prediction. Bioinformatics , 40 (7) , Article btae405. 10.1093/bioinformatics/btae405.

Pehlevan, Cengiz; Erdogan, Alper T; Bozkurt, Bariscan; (2023) Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry. In: Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). (pp. pp. 1-37). NeurIPS (In press).

Pellegrino, Arthur; (2023) Low tensor rank learning of neural dynamics. In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Massachusetts Institute of Technology Press: NeurIPS.

Pezzotta, Alberto; Briscoe, James; (2023) Optimal control of gene regulatory networks for morphogen-driven tissue patterning. Cell Systems , 14 (11) 940-952.e11. 10.1016/j.cels.2023.10.004.

Pintér, B; Vörös, G; Szabo, Z; Lőrincz, A; (2013) Explaining unintelligible words by means of their context. In: (Proceedings) International Conference on Pattern Recognition Applications and Methods (ICPRAM). (pp. 382 - 387).

Pintér, B; Vörös, G; Szabo, Z; Lőrincz, A; (2012) Automated Word Puzzle Generation via Topic Dictionaries. Presented at: International Conference on Machine Learning (ICML) - Sparsity, Dictionaries and Projections in Machine Learning and Signal Processing Workshop, Edinburgh, Scotland.

Pintér, B; Vörös, G; Szabo, Z; Lőrincz, A; (2012) Automated Word Puzzle Generation Using Topic Models and Semantic Relatedness Measures. Presented at: Joint Conference on Mathematics and Computer Science (MACS), Siófok, Hungary.

Pintér, B; Vörös, G; Szabo, Z; Lőrincz, A; (2012) Automated Word Puzzle Generation Using Topic Models and Semantic Relatedness Measures. Annales Universitatis Scientiarum Budapestinensis de Rolando Eötvös Nominatae, Sectio Computatorica , 36 299 - 322.

Póczos, B; Szabo, Z; Schneider, J; (2011) Nonparametric divergence estimators for Independent Subspace Analysis. In: (Proceedings) European Signal Processing Conference (EUSIPCO) - Special Session on Dependent Component Analysis. (pp. 1849 - 1853).

Pogodin, R; Mehta, Y; Lillicrap, TP; Latham, PE; (2021) Towards Biologically Plausible Convolutional Networks. In: Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021). NeurIPs (In press).

Pogodin, Roman; (2023) Deep Learning Models of Learning in the Brain. Doctoral thesis (Ph.D), UCL (University College London).

Pogodin, Roman; Deka, Namrata; Li, Yazhe; Sutherland, Danica J; Veitch, Victor; Gretton, Arthur; (2023) Efficient Conditionally Invariant Representation Learning. In: Proceedings of the Eleventh International Conference on Learning Representations. (pp. p. 4723). International Conference on Learning Representations: Kigali, Rwanda. (In press).

Pogodin, R; Latham, PE; (2020) Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks. In: Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020). NeurIPS: Vancouver, Canada.

Poort, J; Wilmes, KA; Blot, A; Chadwick, A; Sahani, M; Clopath, C; Mrsic-Flogel, TD; ... Khan, AG; + view all Poort, J; Wilmes, KA; Blot, A; Chadwick, A; Sahani, M; Clopath, C; Mrsic-Flogel, TD; Hofer, SB; Khan, AG; - view fewer (2021) Learning and attention increase visual response selectivity through distinct mechanisms. Neuron 10.1016/j.neuron.2021.11.016. (In press).

Poort, J; Khan, AG; Pachitariu, M; Nemri, A; Orsolic, I; Krupic, J; Bauza, M; ... Hofer, SB; + view all Poort, J; Khan, AG; Pachitariu, M; Nemri, A; Orsolic, I; Krupic, J; Bauza, M; Sahani, M; Keller, GB; Mrsic-Flogel, TD; Hofer, SB; - view fewer (2015) Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex. Neuron , 86 (6) pp. 1478-1490. 10.1016/j.neuron.2015.05.037.

R

Rao, VAP; (2012) Markov chain Monte Carlo for continuous-time discrete-state systems. Doctoral thesis , UCL (University College London).

Richards, BA; Lillicrap, TP; Beaudoin, P; Bengio, Y; Bogacz, R; Christensen, A; Clopath, C; ... Kording, KP; + view all Richards, BA; Lillicrap, TP; Beaudoin, P; Bengio, Y; Bogacz, R; Christensen, A; Clopath, C; Costa, RP; de Berker, A; Ganguli, S; Gillon, CJ; Hafner, D; Kepecs, A; Kriegeskorte, N; Latham, P; Lindsay, GW; Miller, KD; Naud, R; Pack, CC; Poirazi, P; Roelfsema, P; Sacramento, J; Saxe, A; Scellier, B; Schapiro, AC; Senn, W; Wayne, G; Yamins, D; Zenke, F; Zylberberg, J; Therien, D; Kording, KP; - view fewer (2019) A deep learning framework for neuroscience. Nature Neuroscience , 22 (11) pp. 1761-1770. 10.1038/s41593-019-0520-2.

Riveland, Reidar; Pouget, Alexandre; (2024) Natural language instructions induce compositional generalization in networks of neurons. Nature Neuroscience , 27 pp. 988-999. 10.1038/s41593-024-01607-5.

Roudi, Y; Latham, PE; (2007) A balanced memory network. PLOS COMPUT BIOL , 3 (9) , Article e141. 10.1371/journal.pcbi.0030141.eor.

Roudi, Y; Nirenberg, S; Latham, PE; (2009) Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't. UNSPECIFIED, US.

Roudi, YAT; (2008) Representing Where along with What Information in a Model of a Cortical Patch. PLoS Computational Biology , 4 (3) 10.1371/journal.pcbi.1000012.

Rubenstein, PK; Chwialkowski, KP; Gretton, A; (2016) A Kernel Test for Three-Variable Interactions with Random Processes. In: UAI ’16: Proceedings of the 32nd International Conference on Uncertainty in Artificial Intelligence. (pp. pp. 637-646). AUAI Press

Ruetten, Virginia Marie Sophie; (2024) Whole body-brain functional imaging and interrogation platform: technology development, analysis methods and applications. Doctoral thesis (Ph.D), UCL (University College London).

Rusakov, DA; Savtchenko, LP; Latham, PE; (2020) Noisy Synaptic Conductance: Bug or a Feature? Trends in Neurosciences , 43 (6) pp. 363-372. 10.1016/j.tins.2020.03.009.

Russek, EM; Momennejad, I; Botvinick, MM; Gershman, SJ; Daw, ND; (2017) Predictive representations can link model-based reinforcement learning to model-free mechanisms. PLoS Computer Biology , 13 (9) , Article e1005768. 10.1371/journal.pcbi.1005768.

Rutten, V; Bernacchia, A; Sahani, M; Hennequin, G; (2020) Non-reversible Gaussian processes for identifying latent dynamical structure in neural data. In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020). NeurIPS

S

Sadnicka, Anna; Edwards, Mark John; (2023) Between Nothing and Everything: Phenomenology in Movement Disorders. Movement Disorders 10.1002/mds.29584.

Sadnicka, Anna; Latorre, Anna; (2023) A Caudal Twist in the Tale: The Spinal Cord and Dystonia. Movement Disorders , 38 (11) pp. 1992-1993. 10.1002/mds.29635.

Sadnicka, Anna; Wiestler, Tobias; Butler, Katherine; Altenmuller, Eckart; Edwards, Mark J; Ejaz, Naveed; Diedrichsen, Jörn; (2023) Intact finger representation within primary sensorimotor cortex of musician’s dystonia. Brain , 146 (4) pp. 1511-1522. 10.1093/brain/awac356.

Saglietti, L; Mannelli, SS; Saxe, A; (2022) An analytical theory of curriculum learning in teacher–student networks. Journal of Statistical Mechanics: Theory and Experiment , 2022 , Article 114014. 10.1088/1742-5468/ac9b3c.

Saglietti, Luca; Mannelli, Stefano Sarao; Saxe, Andrew M; (2022) An Analytical Theory of Curriculum Learning in Teacher-Student Networks. In: Koyejo, S and Mohamed, S and Agarwal, A and Belgrave, D and Cho, K and Oh, A, (eds.) Advances in Neural Information Processing Systems 35 (NeurIPS 2022). NeurIPS

Sahani, M; Bohner, G; Meyer, A; (2016) Score-matching estimators for continuous-time point-process regression models. In: Proceedings of MLSP2016. IEEE

Salmasi, M; Sahani, M; (2022) Learning neural codes for perceptual uncertainty. In: 2022 IEEE International Symposium on Information Theory (ISIT). (pp. pp. 2463-2468). IEEE: Espoo, Finland.

Sandbrink, Kai; Bauer, Jan; Proca, Alexandra M; Saxe, Andrew; Summerfield, Christopher; Hummos, Ali; (2024) Flexible task abstractions emerge in linear networks with fast and bounded units. In: Advances in Neural Information Processing Systems 37 (NeurIPS 2024). NeurIPS: Vancouver, Canada.

Saraiva, AC; Schüür, F; Bestmann, S; (2013) Emotional valence and contextual affordances flexibly shape approach-avoidance movements. Front Psychol , 4 , Article 933. 10.3389/fpsyg.2013.00933.

Sarao Mannelli, Stefano; Biroli, Giulio; Cammarota, Chiara; Krzakala, Florent; Urbani, Pierfrancesco; Zdeborova, Lenka; (2020) Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval. In: NeurIPS Proceedings. NeurIPS

Sarao Mannelli, Stefano; Ivashynka, Yaraslau; Saxe, Andrew; Saglietti, Luca; (2024) Tilting the odds at the lottery: the interplay of overparameterisation and curricula in neural networks. Journal of Statistical Mechanics: Theory and Experiment , 2024 (11) , Article 114001. 10.1088/1742-5468/ad864b.

Sarao Mannelli, Stefano; Urbani, Pierfrancesco; (2021) Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems. In: NeurIPS Proceedings. NeurIPS

Sarao Mannelli, Stefano; Vanden-Eijnden, Eric; Zdeborova, Lenka; (2020) Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions. In: NeurIPS Proceedings. NeurIPS

Saxe, AM; Sodhani, S; Lewallen, S; (2022) The Neural Race Reduction: Dynamics of Abstraction in Gated Networks. In: Proceedings of the 39th International Conference on Machine Learning (ICML 2022).

Saxe, Andrew; Juechems, Keno; (2021) Inferring Actions, Intentions, and Causal Relations in a Deep Neural Network. In: Proceedings of the Annual Meeting of the Cognitive Science Society. (pp. pp. 1056-1062). Cognitive Science Society: Philadelphia USA.

Schrab, Antonin; Guedj, Benjamin; Gretton, Arthur; (2023) KSD Aggregated Goodness-of-fit Test. In: Proceedings of the Advances in Neural Information Processing Systems 35 (NeurIPS 2022). NeurIPS

Schrab, Antonin; Kim, Ilmun; Albert, Mélisande; Laurent, Béatrice; Guedj, Benjamin; Gretton, Arthur; (2023) MMD Aggregated Two-Sample Test. Journal of Machine Learning Research (JMLR) , 24 , Article 194.

Schrab, Antonin; Kim, Ilmun; Guedj, Benjamin; Gretton, Arthur; (2022) Efficient Aggregated Kernel Tests using Incomplete U-statistics. In: NeurIPS Proceedings: Advances in Neural Information Processing Systems 35 (NeurIPS 2022). NeurIPS

Schrouff, Jessica; Bellot, Alexis; Rannen-Triki, Amal; Malek, Alan; Albuquerque, Isabela; Gretton, Arthur; D'Amour, Alexander; Schrouff, Jessica; Bellot, Alexis; Rannen-Triki, Amal; Malek, Alan; Albuquerque, Isabela; Gretton, Arthur; D'Amour, Alexander; Chiappa, Silvia; - view fewer (2024) Mind the Graph When Balancing Data for Fairness or Robustness. In: Globerson, A and Mackey, L and Belgrave, D and Fan, A and Paquet, U and Tomczak, J and Zhang, C, (eds.) Advances in Neural Information Processing Systems 37 (NeurIPS 2024). (pp. pp. 1-35). NeurIPS: Vancouver, BC, Canada.

Schuessler, F; Mastrogiuseppe, F; Dubreuil, A; Ostojic, S; Barak, O; (2020) The interplay between randomness and structure during learning in RNNs. In: Larochelle, Hugo and Ranzato, Marc'Aurelio and Hadsell, Raia and Balcan, Maria-Florina and Lin, Hsuan-Tien, (eds.) Advances in Neural Information Processing Systems 33 (NeurIPS 2020). NeurIPS

Schuessler, F; Dubreuil, A; Mastrogiuseppe, F; Ostojic, S; Barak, O; (2019) Dynamics of random recurrent networks with correlated low-rank structure. Physical Review Research , 2 (1) , Article 013111. 10.1103/PhysRevResearch.2.013111.

Schwarz, J; Jayakumar, SM; Pascanu, R; Latham, PE; Teh, YW; (2021) Powerpropagation: A sparsity inducing weight reparameterisation. In: Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems. NeurIPS (In press).

Sclocchi, Antonio; Favero, Alessandro; Levi, Noam Itzhak; Wyart, Matthieu; (2025) Probing the latent hierarchical structure of data via diffusion models. Journal of Statistical Mechanics: Theory and Experiment , 2025 (8) , Article 084005. 10.1088/1742-5468/aded6c.

Sclocchi, Antonio; Favero, Alessandro; Wyart, Matthieu; (2025) A phase transition in diffusion models reveals the hierarchical nature of data. Proceedings of the National Academy of Sciences (PNAS) , 122 (1) , Article e2408799121. 10.1073/pnas.2408799121.

Sclocchi, Antonio; Urbani, Pierfrancesco; (2022) High-dimensional optimization under nonconvex excluded volume constraints. Physical Review E , 105 (2-1) , Article 024134. 10.1103/PhysRevE.105.024134.

Sclocchi, Antonio; Urbani, Pierfrancesco; (2021) Proliferation of non-linear excitations in the piecewise-linear perceptron. SciPost Physics , 10 (1) , Article 013. 10.21468/scipostphys.10.1.013.

Sclocchi, Antonio; Wyart, Matthieu; (2024) On the different regimes of stochastic gradient descent. Proceedings of the National Academy of Sciences (PNAS) , 121 (9) , Article e2316301121. 10.1073/pnas.2316301121.

Shamash, Philip; Lee, Sebastian; Saxe, Andrew M; Branco, Tiago; (2023) Mice identify subgoal locations through an action-driven mapping process. Neuron 10.1016/j.neuron.2023.03.034. (In press).

Shelton, JA; Gasthaus, J; Dai, Z; Lücke, J; Gretton, A; (2017) GP-Select: Accelerating EM using adaptive subspace preselection. Neural Computation , 29 (8) pp. 2177-2202. 10.1162/NECO_a_00982.

Silva Simões, Lucas; (2023) Normative studies of single-event memories and multitask decision-making. Doctoral thesis (Ph.D), UCL (University College London).

Simoes Matos Saraiva, AC; (2017) Motion and Emotion: How emotional stimuli influence the motor system. Doctoral thesis , UCL (University College London).

Singh, aaditya; Chan, Stephanie CY; Moskovitz, Ted; Grant, erin; Saxe, Andrew; Hill, Felix; (2023) The Transient Nature of Emergent In-context Learning in Transformers. In: Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). NeurIPS (In press).

Singh, Aaditya K; Ding, David; Saxe, Andrew; Hill, Felix; Lampinen, Andrew Kyle; (2023) Know your audience: specializing grounded language models with listener subtraction. In: EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. (pp. pp. 3884-3911). Association for Computational Linguistics: Dubrovnik, Croatia.

Singh, R; Xu, L; Gretton, A; (2025) Sequential kernel embedding for mediated and time-varying dose response curves. Bernoulli , 31 (4) pp. 3013-3033. 10.3150/24-BEJ1836.

Singh, R; Sahani, M; Gretton, A; (2019) Kernel Instrumental Variable Regression. In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alché-Buc, F and Fox, E and Garnett., R, (eds.) Proceedings of Advances in Neural Information Processing Systems 32 (NIPS 2019). NIPS Proceedings

Soldado Magraner, Joana; (2018) Linear Dynamics of Evidence Integration in Contextual Decision Making. Doctoral thesis (Ph.D), UCL (University College London).

Soulat, H; Keshavarzi, S; Margrie, TW; Sahani, M; (2021) Probabilistic Tensor Decomposition of Neural Population Spiking Activity. In: Ranzato, M and Beygelzimer, A and Dauphin, Y and Liang, PS and Wortman Vaughan, J, (eds.) Advances in Neural Information Processing Systems 34 (NeurIPS 2021). (pp. pp. 15969-15980). NeurIPS

Soulat, Hugo; (2025) Probabilistic Modeling and Sensory Representations. Doctoral thesis (Ph.D), UCL (University College London).

Sriperumbudur, B; Fukumizu, K; Gretton, A; Hyvärinen, A; Kumar, R; (2017) Density Estimation in Infinite Dimensional Exponential Families. Journal of Machine Learning Research , 18 , Article 57.

Sriperumbudur, B; Szabo, Z; (2016) Optimal Uniform and Lp Rates for Random Fourier Features. Presented at: Theory of Big Data Workshop, London, United Kingdom.

Sriperumbudur, B; Szabo, Z; (2015) Optimal Uniform and Lp Rates for Random Fourier Features. Presented at: Talk at Pennsylvania State University, Pennsylvania State University, USA.

Sriperumbudur, B; Szabo, Z; (2015) Optimal Rates for Random Fourier Feature Approximations. Presented at: Talk at University of Alberta, Edmonton, Alberta, Canada.

Sriperumbudur, B; Szabo, Z; (2015) Optimal Rates for Random Fourier Feature Kernel Approximations. Presented at: Talk at UC Berkeley: AMPLab, Berkeley, California.

Sriperumbudur, B; Szabo, Z; (2015) Optimal Uniform and Lp Rates for Random Fourier Features. Presented at: Quinquennial Review Symposium, Gatsby Unit, London, Unite Kingdom.

Sriperumbudur, B; Szabo, Z; (2015) Optimal Rates for Random Fourier Features. Presented at: Neural Information Processing Systems (NIPS-2015), Montréal, Canada. (In press).

Sriperumbudur, B; Szabo, Z; (2015) Performance Guarantees for Random Fourier Features - Limitations and Merits. Presented at: ML@SITraN, University of Sheffield, Sheffield, Unite Kingdom.

Strathmann, H; Sejdinovic, D; Livingston, S; Schuster, I; Lomeli Garcia, M; Szabo, Z; Andrieu, C; Strathmann, H; Sejdinovic, D; Livingston, S; Schuster, I; Lomeli Garcia, M; Szabo, Z; Andrieu, C; Gretton, A; - view fewer (2016) Kernel techniques for adaptive Monte Carlo methods. Presented at: Greek Stochastics Workshop on Big Data and Big Models, Tinos, Greek.

Strathmann, H; Sejdinovic, D; Livingstone, S; Szabo, Z; Gretton, A; (2015) Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. In: Cortes, C and Lawrence, ND and Lee, DD and Sugiyama, M and Garnett, R, (eds.) Advances in Neural Information Processing Systems 28 (NIPS 2015). NIPS Proceedings

Strathmann, Heiko; (2018) Kernel methods for Monte Carlo. Doctoral thesis (Ph.D), UCL (University College London).

Stringer, C; Pachitariu, M; Steinmetz, NA; Okun, M; Bartho, P; Harris, KD; Sahani, M; Stringer, C; Pachitariu, M; Steinmetz, NA; Okun, M; Bartho, P; Harris, KD; Sahani, M; Lesica, NA; - view fewer (2016) Inhibitory control of correlated intrinsic variability in cortical networks. Elife , 5 , Article e19695. 10.7554/eLife.19695.

Sun, Weinan; Advani, Madhu; Spruston, Nelson; Saxe, Andrew; Fitzgerald, James E; (2023) Organizing memories for generalization in complementary learning systems. Nature Neuroscience 10.1038/s41593-023-01382-9. (In press).

Susman, L; Mastrogiuseppe, F; Brenner, N; Barak, O; (2021) Quality of internal representation shapes learning performance in feedback neural networks. Physical Review Research , 3 , Article 013176. 10.1103/PhysRevResearch.3.013176.

Sutherland, DJ; Strathmann, H; Arbel, M; Gretton, A; Efficient and principled score estimation with Nyström kernel exponential families. In: Lawrence, Neil and Reid, Mark, (eds.) Proceedings International Conference on Artificial Intelligence and Statistics - 2018. Proceedings of Machine Learning Research: Playa Blanca, Lanzarote, Canary Islands.

Sutherland, DJ; Tung, H-Y; Strathmann, H; De, S; Ramdas, A; Smola, A; Gretton, A; Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. In: Proceedings of the 5th International Conference on Learning Representations (ICLR 2017). International Conference on Learning Representations: Toulon, France.

Szabó, Z; Sriperumbudur, BK; (2015) Optimal Rates for the Random Fourier Feature Method. Presented at: Talk at Carnegie Mellon University: Statistical ML Reading Group, Pittsburgh, PA, USA.

Szabo, Zolt´an; Sriperumbudur, Bharath K; Poczos, Barnab´as; Gretton, Arthur; (2016) Learning Theory for Distribution Regression. Journal of Machine Learning Research , 17

Szabo, Z; (2016) Hypothesis Testing with Kernels. Presented at: International Workshop on Pattern Recognition in Neuroimaging (PRNI), Trento, Italy.

Szabo, Z; (2016) Kernel-based learning on probability distributions. Presented at: UNSPECIFIED, San Diego, California, USA.

Szabo, Z; (2016) Performance guarantees for kernel-based learning on probability distributions. Presented at: Talk at Special Symposium on Intelligent Systems, MPI Tübingen, Germany.

Szabo, Z; (2016) Optimal Rates for the Random Fourier Feature Technique. Presented at: invited talk at École Polytechnique, Palaiseau, France.

Szabo, Z; (2016) Learning from Features of Sets and Probabilities. Presented at: Talk at Imperial College London, Department of Computing, London, United Kingdom.

Szabo, Z; (2014) Information Theoretical Estimators Toolbox. Journal of Machine Learning Research , 15 283 - 287.

Szabo, Z; (2013) Information Theoretical Estimators (ITE) Toolbox. Presented at: Neural Information Processing Systems (NIPS) - Workshop on Machine Learning Open Source Software, Harrahs and Harveys, Lake Tahoe, Nevada, United States.

Szabo, Z; (2012) Group-Structured and Independent Subspace Based Dictionary Learning. Doctoral thesis , Eötvös Loránd University.

Szabo, Z; (2010) Autoregressive Independent Process Analysis with Missing Observations. In: Proceedings of ESANN 2010: 18th European Symposium on Artificial Neural Networks. (pp. 159 - 164). D-Side Publications

Szabo, Z; (2009) Separation Principles in Independent Process Analysis. Doctoral thesis , Eötvös Loránd University, Budapest.

Szabo, Z; (2003) Retina based sampling in face component recognition. Masters thesis , Eötvös Loránd University, Budapest.

Szabo, Z; Gretton, A; Póczos, B; Sriperumbudur, B; (2015) Consistent Vector-valued Distribution Regression. Presented at: UCL Workshop on the Theory of Big Data, London, UK.

Szabo, Z; Gretton, A; Poczos, B; Sriperumbudur, B; (2015) Two-stage Sampled Learning Theory on Distributions. In: Lebanon, G and Vishwanathan, SVN, (eds.) Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics. (pp. pp. 948-957). Journal of Machine Learning Research: San Diego, CA, USA.

Szabo, Z; Gretton, A; Poczos, B; Sriperumbudur, B; (2014) Vector-valued distribution regression: a simple and consistent approach. Presented at: Statistical Science Seminars, London, UK.

Szabo, Z; Gretton, A; Póczos, B; Sriperumbudur, B; (2014) Simple consistent distribution regression on compact metric domains. Presented at: UCL-Duke Workshop on Sensing and Analysis of High-Dimensional Data (SAHD-2014), London, UK.

Szabo, Z; Gretton, A; Póczos, B; Sriperumbudur, B; (2014) Distribution Regression - the Set Kernel Heuristic is Consistent. Presented at: CSML Lunch Talk Series, London, UK.

Szabo, Z; Gretton, A; Póczos, B; Sriperumbudur, B; (2014) Learning on Distributions. Presented at: Kernel methods for big data workshop, Lille, France.

Szabo, Z; Gretton, A; Póczos, B; Sriperumbudur, B; (2014) Consistent Distribution Regression via Mean Embedding. Presented at: University of Hertfordshire, Computer Science Research Colloquium, Hatfield, UK.

Szabo, Z; Lőrincz, A; (2009) Complex Independent Process Analysis. Acta Cybernetica , 19 177 - 190.

Szabo, Z; Lőrincz, A; (2008) Towards Independent Subspace Analysis in Controlled Dynamical Systems. Presented at: ICA Research Network International Workshop (ICARN), Liverpool, U.K..

Szabo, Z; Lőrincz, A; (2007) Multilayer Kerceptron. Journal of Applied Mathematics , 24 209 - 222.

Szabo, Z; Póczos, A; Lőrincz, A; (2012) Collaborative Filtering via Group-Structured Dictionary Learning. In: (Proceedings) International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA). (pp. 247 - 254). Springer-Verlag, Berlin Heidelberg

Szabo, Z; Póczos, B; (2011) Nonparametric Independent Process Analysis. Presented at: European Signal Processing Conference (EUSIPCO), Barcelona, Spain.

Szabo, Z; Póczos, B; Lőrincz, A; (2012) Separation theorem for independent subspace analysis and its consequences. Pattern Recognition , 45 (4) 1782 - 1791. 10.1016/j.patcog.2011.09.007.

Szabo, Z; Póczos, B; Lőrincz, A; (2011) Online Dictionary Learning with Group Structure Inducing Norms. Presented at: International Conference on Machine Learning (ICML) - Structured Sparsity: Learning and Inference Workshop, Bellevue, Washington, USA.

Szabo, Z; Póczos, B; Lőrincz, A; (2005) Separation Theorem for Independent Subspace Analysis. : Eötvös Loránd University, Budapest.

Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2016) Optimal Regression on Sets. Presented at: eResearch Domain launch event, London, United Kingdom.

Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2016) Distribution Regression with Minimax-Optimal Guarantee. Presented at: MASCOT-NUM 2016, Toulouse, France.

Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Learning Theory for Vector-Valued Distribution Regression. Presented at: CMStatistics 2015, London, United Kingdom.

Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Distribution Regression: Computational and Statistical Tradeoffs. Presented at: CSML Lunch Talk Series, London, United Kingdom.

Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Distribution Regression: Computational and Statistical Tradeoffs. Presented at: Talk at Princeton University, Princeton, New Jersey.

Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Regression on Probability Measures: A Simple and Consistent Algorithm. Presented at: CRiSM Seminars, Department of Statistics, University of Warwick, Coventry, United Kingdom.

Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Vector-valued Distribution Regression - Keep It Simple and Consistent. Presented at: CSML reading group, Department of Statistics, University of Oxford, Oxford, United Kingdom.

Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Distribution Regression - Make It Simple and Consistent. Presented at: Data, Learning and Inference workshop (DALI), La Palma (Canaries, Spain).

Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) A Simple and Consistent Technique for Vector-valued Distribution Regression. Presented at: Invited talk at the Artificial Intelligence and Natural Computation seminars, University of Birmingham, UK.

Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Consistent Vector-valued Regression on Probability Measures. Presented at: Invited talk at Prof. Bernhard Schölkopf's lab, Tübingen.

T

Tacchetti, A; Francis Song, H; Mediano, PAM; Zambaldi, V; Kramár, J; Rabinowitz, NC; Graepel, T; ... Battaglia, PW; + view all Tacchetti, A; Francis Song, H; Mediano, PAM; Zambaldi, V; Kramár, J; Rabinowitz, NC; Graepel, T; Botvinick, M; Battaglia, PW; - view fewer (2019) Relational forward models for multi-agent learning. In: Proceedings of the 7th International Conference on Learning Representations, ICLR 2019. ICLR

Tilley, Matthew; Miller, Michelle; Freedman, David J; (2023) Artificial Neuronal Ensembles with Learned Context Dependent Gating. In: Proceedings of the Eleventh International Conference on Learning Representations, ICLR 2023. (pp. pp. 1-13). International Conference on Learning Representations

Tootoonian, Sina; Schaefer, Andreas T; Latham, Peter E; (2022) Sparse connectivity for MAP inference in linear models using sister mitral cells. PLOS Computational Biology , 18 (1) , Article e1009808. 10.1371/journal.pcbi.1009808. (In press).

Trautmann, EM; O'Shea, DJ; Sun, X; Marshel, JH; Crow, A; Hsueh, B; Vesuna, S; ... Shenoy, KV; + view all Trautmann, EM; O'Shea, DJ; Sun, X; Marshel, JH; Crow, A; Hsueh, B; Vesuna, S; Cofer, L; Bohner, G; Allen, W; Kauvar, I; Quirin, S; MacDougall, M; Chen, Y; Whitmire, MP; Ramakrishnan, C; Sahani, M; Seidemann, E; Ryu, SI; Deisseroth, K; Shenoy, KV; - view fewer (2021) Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Nature Communications , 12 (1) , Article 3689. 10.1038/s41467-021-23884-5.

Turner, R.E.; (2010) Statistical models for natural sounds. Doctoral thesis , UCL (University College London).

Tuttle, E; (2003) Bayesian Inference Methods for Solving Sequential Decision Problems. Masters thesis , UNSPECIFIED.

V

van Rossem, L; Saxe, AM; (2024) When Representations Align: Universality in Representation Learning Dynamics. In: Proceedings of the 41st International Conference on Machine Learning. (pp. pp. 49098-49121). Proceedings of Machine Learning Research: Vienna, Austria.

Van Rossem, Loek; (2025) Algorithm Development in Neural Networks: Insights from the Streaming Parity Task. In: Proceedings of the 42nd International Conference on Machine Learning. PMLR: Vancouver, Canada.

Vertes, E; Sahani, M; (2019) A neurally plausible model learns successor representations in partially observable environments. In: Proceedings of 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). NIPS Proceedings: Vancouver, Canada.

Vertes, E; Sahani, M; (2018) Flexible and accurate inference and learning for deep generative models. In: Proceedings of 32nd Conference on Neural Information Processing Systems (NeurIPS 2018),. Neural Information Processing Systems (NIPS): Montréal, Canada..

W

Wager, TD; Atlas, LY; Botvinick, MM; Chang, LJ; Coghill, RC; Davis, KD; Iannetti, GD; ... Yarkoni, T; + view all Wager, TD; Atlas, LY; Botvinick, MM; Chang, LJ; Coghill, RC; Davis, KD; Iannetti, GD; Poldrack, RA; Shackman, AJ; Yarkoni, T; - view fewer (2016) Pain in the ACC? Proceedings of The National Academy of Sciences of The United States of America (PNAS) , 113 (18) E2474-E2475. 10.1073/pnas.1600282113.

Walker, William; (2024) Probabilistic Unsupervised Learning using Recognition Parameterized Models. Doctoral thesis (Ph.D), UCL (University College London).

Walker, william; Soulat, hugo; Yu, changmin; Sahani, Maneesh; (2023) Unsupervised representation learning with recognition-parametrised probabilistic models. In: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research: Valencia, Spain Proceedings of Machine Learning Research. (In press).

Weichwald, S; Gretton, A; Schölkopf, B; Grosse-Wentrup, M; (2016) Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data. In: PRNI 2016: 6th International Workshop on Pattern Recognition in Neuroimaging. Institute of Electrical and Electronic Engineers (IEEE)

Weichwald, S; Grosse-Wentrup, M; Gretton, A; (2016) MERLiN: Mixture Effect Recovery in Linear Networks. IEEE Journal of Selected Topics in Signal Processing , 10 (7) pp. 1254-1266. 10.1109/JSTSP.2016.2601144.

Wenliang, LK; Moskovitz, T; Kanagawa, H; Sahani, M; (2020) Amortised learning by wake-sleep. In: Proceedings of the 37th International Conference on Machine Learning,. (pp. pp. 10167-10178). PMLR: Proceedings of Machine Learning Research

Wenliang, LK; Sutherland, DJ; Strathmann, H; Gretton, A; (2019) Learning deep kernels for exponential family densities. In: Proceedings of the 36th International Conference on Machine Learning. (pp. pp. 11693-11710). Proceedings of Machine Learning Research (PMLR): Long Beach, CA, USA.

Williamson, RS; Sahani, M; Pillow, JW; (2019) Correction: The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction. PLoS Computational Biology , 15 (6) , Article e1007139. 10.1371/journal.pcbi.1007139.

Williamson, RS; Ahrens, MB; Linden, JF; Sahani, M; (2016) Input-Specific Gain Modulation by Local Sensory Context Shapes Cortical and Thalamic Responses to Complex Sounds. Neuron , 91 (2) pp. 467-481. 10.1016/j.neuron.2016.05.041.

Williamson, RS; Sahani, M; Pillow, JW; (2015) The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction. PLOS Computational Biology , 11 (4) , Article e1004141. 10.1371/journal.pcbi.1004141.

Wiltzer, H; Farebrother, J; Gretton, A; Rowland, M; (2024) Foundations of Multivariate Distributional Reinforcement Learning. In: Globerson, A and Mackey, L and Belgrave, D and Fan, A and Paquet, U and Tomczak, J and Zhang, C, (eds.) Advances in Neural Information Processing Systems 37. Neural Information Processing Systems Foundation, Inc. (NeurIPS): Vancouver, Canada.

Wu, C; Masoomi, A; Gretton, A; Dy, J; (2022) Deep Layer-wise Networks Have Closed-Form Weights. In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. (pp. pp. 188-225). Valencia, Spain

X

Xu, L; Chen, Y; Srinivasan, S; de Freitas, N; Doucet, A; Gretton, A; (2021) Learning Deep Features in Instrumental Variable Regression. In: Proceedings of the 9th International Conference on Learning Representations: ICLR 2021. ICLR

Xu, Liyuan; Gretton, Arthur; (2023) A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. In: Proceedings of the Eleventh International Conference on Learning Representations. (pp. p. 2756). International Conference on Learning Representations: Kigali, Rwanda. (In press).

Xu, Liyuan; Gretton, Arthur; (2022) A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. arXiv: Ithaca, NY, USA.

Xu, Wenkai; (2021) Advances in Non-parametric Hypothesis Testing with Kernels. Doctoral thesis (Ph.D), UCL (University College London).

Y

Yu, Changmin; Burgess, neil; Sahani, Maneesh; Gershman, Samuel J; (2023) Successor-Predecessor Intrinsic Exploration. OpenReview.net: Amherst, MA, United States.

Yu, Changmin; Burgess, Neil; Sahani, Maneesh; Gershman, Samuel J; (2023) Successor-Predecessor Intrinsic Exploration. In: Oh, A and Neumann, T and Globerson, A and Saenko, K and Hardt, M and Levine, S, (eds.) Advances in Neural Information Processing Systems 36 (NeurIPS 2023). (pp. pp. 1-18). NeurIPS

Yu, Changmin; Sahani, Maneesh; Lengyel, Máté; (2025) Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA. In: Proceedings 13th International Conference on Learning Representations ICLR 2025. ICLR: Singapore, Singapore.

Yu, Changmin; Soulat, hugo; Burgess, neil; Sahani, Maneesh; (2022) Structured recognition for generative models with explaining away. In: Koyejo, S and Mohamed, M and Agarwal, A and Belgrave, D and Cho, K and Oh, A, (eds.) Advances in Neural Information Processing Systems 35 (NeurIPS 2022). NeurIPS Proceedings: New Orleans, LA, USA.

Yu, Angela Jie; (2005) ACh and NE: Bayes, uncertainty, attention, and learning. UNSPECIFIED thesis , UCL (University College London).

Z

Zamfir, Elena; (2021) Investigating the complexity of interactions between attributions and beliefs: evidence from a novel task. Doctoral thesis (Ph.D), UCL (University College London).

Zboňáková, L; Monti, RP; Härdle, WK; (2019) Towards the interpretation of time-varying regularization parameters in streaming penalized regression models. Pattern Recognition Letters , 125 pp. 542-548. 10.1016/j.patrec.2019.06.021.

Zhang, Yedi; Latham, Peter E; Saxe, Andrew M; (2024) Understanding Unimodal Bias in Multimodal Deep Linear Networks. In: Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix, (eds.) Proceedings of the 41st International Conference on Machine Learning. (pp. pp. 59100-59125). Proceedings of Machine Learning Research (PMLR): Vienna, Austria.

Zhang, Yedi; Saxe, Andrew; Latham, peter; (2025) When Are Bias-Free ReLU Networks Effectively Linear Networks? Transactions on Machine Learning Research , 04 pp. 1-36.

Zhang, Yedi; Singh, Aaditya K; Latham, Peter E; Saxe, Andrew M; (2025) Training Dynamics of In-Context Learning in Linear Attention. In: Proceedings of the 42nd International Conference on Machine Learning. (pp. pp. 1-41). PMLR

Zhaoping, L.; (2000) Pre-attentive segmentation in the primary visual cortex. Spatial Vision , 13 (1) pp. 25-50. 10.1163/156856800741009.

Zhaoping, L.; (1999) A V1 model of pop out and asymmetry in visual search. In: Kearns, M.S. and Solla, S.A. and Cohn, D.A., (eds.) Advances in Neural Information Processing Systems. (pp. pp. 796-802). MIT Press: Cambridge, US.

Zhaoping, L.; (1998) Primary cortical dynamics for visual grouping. In: Wong, K.-Y.M and King, I. and Yeung, D.Y., (eds.) Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective: International Workshop (TANC'97) Hong Kong, 26-28 May 1997. (pp. pp. 155-164). Springer: New York, US.

Zhaoping, L.; (1995) Understanding ocular dominance development from binocular input statistics. In: Bower, J.M., (ed.) The Neurobiology of Computation: Proceedings of the Third Annual Computation and Neural Systems Conference. (pp. pp. 397-402). Kluwer/ Springer Verlag: Boston, US.

Zhaoping, L.; (1994) Modeling the sensory computations of the olfactory bulb. In: Domany, E. and van Hemmen, J.L. and Schulten, K., (eds.) Models of Neural Networks II: Temporal Aspects of Coding and Information Processing in Biological Systems. (pp. 221-251). Springer Verlag: New York, US.

Zhaoping, L.; Atick, J.J.; (1994) Toward a theory of the striate cortex. Neural Computation , 6 (1) pp. 127-147. 10.1162/neco.1994.6.1.127.

Zhou, Liang; Smith, Kevin A; Tenenbaum, Joshua B; Gerstenberg, Tobias; (2023) Mental jenga: A counterfactual simulation model of causal judgments about physical support. Journal of Experimental Psychology: General , 152 (8) pp. 2237-2269. 10.1037/xge0001392.

Zhou, Wenda; Veitch, Victor; Austern, Morgane; Adams, Ryan P; Orbanz, Peter; (2019) Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. In: ICLR 2019 International Conference on Learning Representations. ICLR: New Orleans, LA, United States.

Zhu, Y; Gultchin, L; Gretton, A; Kusner, M; Silva, R; (2022) Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach. In: Cussens, J and Zhang, K, (eds.) Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022. (pp. pp. 2414-2424). Proceedings of Machine Learning Research (PMLR): Eindhoven, Netherlands.

Zylberberg, J; Pouget, A; Latham, PE; Shea-Brown, E; (2017) Robust information propagation through noisy neural circuits. PLoS Computational Biology , 13 (4) , Article e1005497. 10.1371/journal.pcbi.1005497.

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