| Language modeling is compression G Delétang, A Ruoss, PA Duquenne, E Catt, T Genewein, C Mattern, ... arXiv preprint arXiv:2309.10668, 2023 | 311 | 2023 |
| Neural networks and the chomsky hierarchy G Delétang, A Ruoss, J Grau-Moya, T Genewein, LK Wenliang, E Catt, ... arXiv preprint arXiv:2207.02098, 2022 | 250 | 2022 |
| Cot-gan: Generating sequential data via causal optimal transport T Xu*, LK Wenliang*, M Munn, B Acciaio Adcances in Neural Information Processing Systems, 2020 | 173 | 2020 |
| TacticAI: an AI assistant for football tactics Z Wang, P Veličković, D Hennes, N Tomašev, L Prince, M Kaisers, ... Nature communications 15 (1), 1906, 2024 | 129 | 2024 |
| Deep neural networks for modeling visual perceptual learning LK Wenliang, AR Seitz Journal of Neuroscience 38 (27), 6028-6044, 2018 | 107 | 2018 |
| Learning deep kernels for exponential family densities LK Wenliang*, DJ Sutherland*, H Strathmann, A Gretton International Conference on Machine Learning, 6737--6746, 2019 | 102 | 2019 |
| Amortized planning with large-scale transformers: A case study on chess A Ruoss, G Delétang, S Medapati, J Grau-Moya, LK Wenliang, E Catt, ... Advances in Neural Information Processing Systems 37, 65765-65790, 2024 | 80* | 2024 |
| Grin: Generative relation and intention network for multi-agent trajectory prediction L Li, J Yao, L Wenliang, T He, T Xiao, J Yan, D Wipf, Z Zhang Advances in Neural Information Processing Systems 34, 27107-27118, 2021 | 58 | 2021 |
| Blindness of score-based methods to isolated components and mixing proportions LK Wenliang, H Kanagawa NeurIPS Workshop Your Model is Wrong: Robustness and misspecification in …, 2021 | 44 | 2021 |
| Score-based generative models learn manifold-like structures with constrained mixing LK Wenliang, B Moran NeurIPS 2022 Workshop on Score-Based Methods, 2022 | 41 | 2022 |
| Learning universal predictors J Grau-Moya, T Genewein, M Hutter, L Orseau, G Delétang, E Catt, ... arXiv preprint arXiv:2401.14953, 2024 | 27 | 2024 |
| Memory-based meta-learning on non-stationary distributions T Genewein, G Delétang, A Ruoss, LK Wenliang, E Catt, V Dutordoir, ... International conference on machine learning, 11173-11195, 2023 | 20 | 2023 |
| On the value of infinite gradients in variational autoencoder models B Dai, L Wenliang, D Wipf Advances in Neural Information Processing Systems 34, 7180-7192, 2021 | 15 | 2021 |
| A neurally plausible model for online recognition and postdiction in a dynamical environment LK Wenliang, M Sahani Advances in Neural Information Processing Systems, 2019 | 12 | 2019 |
| Grasp planning by human experience on a variety of objects with complex geometry C Liu, W Li, F Sun, J Zhang 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015 | 10 | 2015 |
| Amortised learning by wake-sleep L Wenliang, T Moskovitz, H Kanagawa, M Sahani International Conference on Machine Learning, 10236-10247, 2020 | 9* | 2020 |
| Distributional Bellman Operators over Mean Embeddings LK Wenliang, G Delétang, M Aitchison, M Hutter, A Ruoss, A Gretton, ... arXiv preprint arXiv:2312.07358, 2023 | 8 | 2023 |
| Self-Predictive Universal AI E Catt, J Grau-Moya, M Hutter, M Aitchison, T Genewein, G Deletang, ... Advances in Neural Information Processing Systems, 2023 | 8 | 2023 |
| Near-minimax-optimal distributional reinforcement learning with a generative model M Rowland, K Li, R Munos, C Lyle, Y Tang, W Dabney Advances in Neural Information Processing Systems 37, 132774-132823, 2024 | 6 | 2024 |
| Understanding prompt tuning and in-context learning via meta-learning T Genewein, LK Wenliang, J Grau-Moya, A Ruoss, L Orseau, M Hutter arXiv preprint arXiv:2505.17010, 2025 | 4 | 2025 |
Jordi Grau-MoyaResearch Scientist at Google DeepMindVerified email at deepmind.com
