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Number of items: 19.
B
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).
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C
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Carrasco-Davis, Rodrigo;
(2025)
Principles of Optimal Learning Control in Biological
and Artificial Agents.
Doctoral thesis (Ph.D), UCL (University College London).
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D
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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
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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).
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Dorrell, william;
Hsu, Kyle;
Hollingsworth, Luke;
Lee, Jin Hwa;
Wu, Jiajun;
Finn, Chelsea;
Latham, Peter;
... Whittington, James CR; + view all
(2025)
Range, not Independence, Drives Modularity in Biologically Inspired Representations.
In:
Proceedings 13th International Conference on Learning Representations ICLR 2025.
ICLR: Singapore, Singapore.
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G
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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.
(In press).
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H
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Huang, Kevin Han;
(2025)
Universality beyond the classical asymptotic regime.
Doctoral thesis (Ph.D), UCL (University College London).
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J
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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.
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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
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K
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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.
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M
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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).
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Moskovitz, Theodore Harris;
(2025)
Structure, Learning, & Composition: Multitask Reinforcement Learning in Brains and Machines.
Doctoral thesis (Ph.D), UCL (University College London).
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S
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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.
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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.
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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.
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Soulat, Hugo;
(2025)
Probabilistic Modeling
and Sensory Representations.
Doctoral thesis (Ph.D), UCL (University College London).
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Y
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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.
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Z
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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.
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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
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