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Number of items: 23.
Article
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Proceedings paper
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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
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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
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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.
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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.
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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.
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Thesis
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Matthey-De-L'Endroit, Loïc;
(2019)
Palimpsest Working Memory.
Doctoral thesis (Ph.D), UCL (University College London).
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