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Number of items: 21.

A

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. Green open access
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C

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. Green open access
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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. Green open access
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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. Green open access
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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. Green open access
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F

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. Green open access
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K

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. Green open access
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L

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 Green open access
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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. Green open access
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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. Green open access
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Lorenz, R; Simmons, LE; Monti, RP; Arthur, JL; Limal, S; Laakso, I; Leech, R; (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. Green open access
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M

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

Richards, BA; Lillicrap, TP; Beaudoin, P; Bengio, Y; Bogacz, R; Christensen, A; Clopath, C; ... Kording, KP; + view all (2019) A deep learning framework for neuroscience. Nature Neuroscience , 22 (11) pp. 1761-1770. 10.1038/s41593-019-0520-2. Green open access
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S

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. Green open access
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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 Green open access
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T

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

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. Green open access
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W

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. Green open access
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Z

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. Green open access
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This list was generated on Wed Oct 13 00:40:57 2021 BST.