A neurally plausible model for online recognition and postdiction in a dynamical environment. - Li Kevin Wenliang; Maneesh Sahani (NeurIPS, 2019)

Amortised Learning by Wake-Sleep. - Li K. Wenliang; Theodore H. Moskovitz; Heishiro Kanagawa; Maneesh Sahani (ICML, 2020)

COT-GAN: Generating Sequential Data via Causal Optimal Transport. - Tianlin Xu; Li Kevin Wenliang; Michael Munn; Beatrice Acciaio (NeurIPS, 2020)

Blindness of score-based methods to isolated components and mixing proportions. - Li K. Wenliang (CoRR, 2020)

GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction. - Longyuan Li; Jian Yao 004; Li K. Wenliang; Tong He 002; Tianjun Xiao; Junchi Yan; David Wipf; Zheng Zhang 001 (NeurIPS, 2021)

Nonparametric enrichment in computational and biological representations of distributions. - Li Kevin Wenliang (2021)

Neural Networks and the Chomsky Hierarchy. - Grégoire Delétang; Anian Ruoss; Jordi Grau-Moya; Tim Genewein; Li Kevin Wenliang; Elliot Catt; Marcus Hutter; Shane Legg; Pedro A. Ortega (CoRR, 2022)

On the failure of variational score matching for VAE models. - Li Kevin Wenliang (CoRR, 2022)

Neural Networks and the Chomsky Hierarchy. - Grégoire Delétang; Anian Ruoss; Jordi Grau-Moya; Tim Genewein; Li Kevin Wenliang; Elliot Catt; Chris Cundy; Marcus Hutter; Shane Legg; Joel Veness; Pedro A. Ortega (ICLR, 2023)

Memory-Based Meta-Learning on Non-Stationary Distributions. - Tim Genewein; Grégoire Delétang; Anian Ruoss; Li Kevin Wenliang; Elliot Catt; Vincent Dutordoir; Jordi Grau-Moya; Laurent Orseau; Marcus Hutter; Joel Veness (ICML, 2023)

TacticAI: an AI assistant for football tactics. - Zhe Wang 055; Petar Velickovic; Daniel Hennes; Nenad Tomasev; Laurel Prince; Michael Kaisers; Yoram Bachrach; Romuald Elie; Li Kevin Wenliang; Federico Piccinini; William Spearman; Ian Graham; et al. (CoRR, 2023)

Score-based generative models learn manifold-like structures with constrained mixing. - Li Kevin Wenliang; Ben Moran (CoRR, 2023)

Language Modeling Is Compression. - Grégoire Delétang; Anian Ruoss; Paul-Ambroise Duquenne; Elliot Catt; Tim Genewein; Christopher Mattern; Jordi Grau-Moya; Li Kevin Wenliang; Matthew Aitchison; Laurent Orseau; Marcus Hutter; Joel Veness (ICLR, 2024)

Learning Universal Predictors. - Jordi Grau-Moya; Tim Genewein; Marcus Hutter; Laurent Orseau; Grégoire Delétang; Elliot Catt; Anian Ruoss; Li Kevin Wenliang; Christopher Mattern; Matthew Aitchison; Joel Veness (ICML, 2024)

Distributional Bellman Operators over Mean Embeddings. - Li Kevin Wenliang; Grégoire Delétang; Matthew Aitchison; Marcus Hutter; Anian Ruoss; Arthur Gretton; Mark Rowland 001 (ICML, 2024)

Grandmaster-Level Chess Without Search. - Anian Ruoss; Grégoire Delétang; Sourabh Medapati; Jordi Grau-Moya; Li Kevin Wenliang; Elliot Catt; John Reid; Tim Genewein (CoRR, 2024)

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. - Mark Rowland 001; Li Kevin Wenliang; Rémi Munos; Clare Lyle; Yunhao Tang; Will Dabney (CoRR, 2024)

Why is prompting hard? Understanding prompts on binary sequence predictors. - Li Kevin Wenliang; Anian Ruoss; Jordi Grau-Moya; Marcus Hutter; Tim Genewein (CoRR, 2025)

Understanding Prompt Tuning and In-Context Learning via Meta-Learning. - Tim Genewein; Li Kevin Wenliang; Jordi Grau-Moya; Anian Ruoss; Laurent Orseau; Marcus Hutter (CoRR, 2025)