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Number of items: 31.
A
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.
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B
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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
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Broeker, Franziska;
(2022)
Semi-supervised categorisation: the role of feedback in human learning.
Doctoral thesis (Ph.D), UCL (University College London).
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C
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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.
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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).
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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.
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F
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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.
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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.
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G
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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.
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H
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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.
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K
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Kanagawa, Heishiro;
(2022)
Statistical Model Evaluation Using Reproducing Kernels and Stein’s method.
Doctoral thesis (Ph.D), UCL (University College London).
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Khemakhem, Ilyes;
(2022)
Advances in identifiability of nonlinear probabilistic models.
Doctoral thesis (Ph.D), UCL (University College London).
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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)
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L
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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
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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)
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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
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M
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Manto, Mario;
Argyropoulos, Georgios PD;
Bocci, Tommaso;
Celnik, Pablo A;
Corben, Louise A;
Guidetti, Matteo;
Koch, Giacomo;
... Ferrucci, Roberta; + view all
(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.
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Matsuo, Yutaka;
LeCun, Yann;
Sahani, Maneesh;
Precup, Doina;
Silver, David;
Sugiyama, Masashi;
Uchibe, Eiji;
(2022)
Deep learning, reinforcement learning, and world models.
Neural Networks
, 152
pp. 267-275.
10.1016/j.neunet.2022.03.037.
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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.
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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
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N
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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.
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S
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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.
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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
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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.
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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).
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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
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T
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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).
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W
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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
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X
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Xu, Liyuan;
Gretton, Arthur;
(2022)
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment.
arXiv: Ithaca, NY, USA.
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Y
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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.
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Z
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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.
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