| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 702 | 2024 |
| Representation learning via invariant causal mechanisms J Mitrovic, B McWilliams, J Walker, L Buesing, C Blundell arXiv preprint arXiv:2010.07922, 2020 | 269 | 2020 |
| Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging S Azizi, L Culp, J Freyberg, B Mustafa, S Baur, S Kornblith, T Chen, ... Nature Biomedical Engineering 7 (6), 756-779, 2023 | 141 | 2023 |
| Causal reasoning from meta-reinforcement learning I Dasgupta, J Wang, S Chiappa, J Mitrovic, P Ortega, D Raposo, ... arXiv preprint arXiv:1901.08162, 2019 | 132 | 2019 |
| Pushing the limits of self-supervised resnets: Can we outperform supervised learning without labels on imagenet? N Tomasev, I Bica, B McWilliams, L Buesing, R Pascanu, C Blundell, ... arXiv preprint arXiv:2201.05119, 2022 | 95 | 2022 |
| Causal inference via kernel deviance measures J Mitrovic, D Sejdinovic, YW Teh Advances in neural information processing systems 31, 2018 | 68 | 2018 |
| Coberl: Contrastive bert for reinforcement learning A Banino, AP Badia, J Walker, T Scholtes, J Mitrovic, C Blundell arXiv preprint arXiv:2107.05431, 2021 | 42 | 2021 |
| DR-ABC: Approximate Bayesian computation with kernel-based distribution regression J Mitrovic, D Sejdinovic, YW Teh International Conference on Machine Learning, 1482-1491, 2016 | 38 | 2016 |
| Causally correct partial models for reinforcement learning DJ Rezende, I Danihelka, G Papamakarios, NR Ke, R Jiang, T Weber, ... arXiv preprint arXiv:2002.02836, 2020 | 35 | 2020 |
| Neural algorithmic reasoning with causal regularisation B Bevilacqua, K Nikiforou, B Ibarz, I Bica, M Paganini, C Blundell, ... International Conference on Machine Learning, 2272-2288, 2023 | 27 | 2023 |
| Hierarchical adversarially learned inference MI Belghazi, S Rajeswar, O Mastropietro, N Rostamzadeh, J Mitrovic, ... arXiv preprint arXiv:1802.01071, 2018 | 24 | 2018 |
| Encoders and ensembles for task-free continual learning M Shanahan, C Kaplanis, J Mitrović arXiv preprint arXiv:2105.13327, 2021 | 22 | 2021 |
| Less can be more in contrastive learning J Mitrovic, B McWilliams, M Rey PMLR, 2020 | 22 | 2020 |
| Amortized learning of neural causal representations NR Ke, J Wang, J Mitrovic, M Szummer, DJ Rezende arXiv preprint arXiv:2008.09301, 2020 | 20 | 2020 |
| Semppl: Predicting pseudo-labels for better contrastive representations M Bošnjak, PH Richemond, N Tomasev, F Strub, JC Walker, F Hill, ... arXiv preprint arXiv:2301.05158, 2023 | 10 | 2023 |
| Improving fine-grained understanding in image-text pre-training I Bica, A Ilić, M Bauer, G Erdogan, M Bošnjak, C Kaplanis, AA Gritsenko, ... arXiv preprint arXiv:2401.09865, 2024 | 9 | 2024 |
| Continually learning representations at scale A Galashov, J Mitrovic, D Tirumala, YW Teh, T Nguyen, A Chaudhry, ... Conference on Lifelong Learning Agents, 534-547, 2023 | 6 | 2023 |
| Synth: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings S Sharifzadeh, C Kaplanis, S Pathak, D Kumaran, A Ilic, J Mitrovic, ... arXiv preprint arXiv:2403.07750, 2024 | 4 | 2024 |
| Deep Kernel Machines via the Kernel Reparametrization Trick J Mitrovic, D Sejdinovic, YW Teh | 2 | 2017 |
| Meta-reinforcement learning of causal strategies I Dasgupta, Z Kurth-Nelson, S Chiappa, J Mitrovic, P Ortega, E Hughes, ... The Meta-Learning Workshop at the Neural Information Processing Systems …, 2019 | 1 | 2019 |
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
