| Soft actor-critic algorithms and applications T Haarnoja, A Zhou, K Hartikainen, G Tucker, S Ha, J Tan, V Kumar, ... arXiv preprint arXiv:1812.05905, 2018 | 3200 | 2018 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2453 | 2023 |
| Offline reinforcement learning: Tutorial, review, and perspectives on open problems S Levine, A Kumar, G Tucker, J Fu arXiv preprint arXiv:2005.01643, 2020 | 2349* | 2020 |
| Conservative q-learning for offline reinforcement learning A Kumar, A Zhou, G Tucker, S Levine NeurIPS 2020, 2020 | 2037 | 2020 |
| Efficient Bayesian mixed-model analysis increases association power in large cohorts PR Loh, G Tucker, BK Bulik-Sullivan, BJ Vilhjálmsson, HK Finucane, ... Nature genetics 47 (3), 284-290, 2015 | 1655 | 2015 |
| Regularizing neural networks by penalizing confident output distributions G Pereyra, G Tucker, J Chorowski, Ł Kaiser, G Hinton ICLR 2017 Workshop, 2017 | 1319 | 2017 |
| D4rl: Datasets for deep data-driven reinforcement learning J Fu, A Kumar, O Nachum, G Tucker, S Levine arXiv preprint arXiv:2004.07219, 2020 | 1263 | 2020 |
| Stabilizing off-policy q-learning via bootstrapping error reduction A Kumar, J Fu, G Tucker, S Levine NeurIPS 2019, 2019 | 1179 | 2019 |
| Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... ICLR 2020 Spotlight, 2020 | 1061 | 2020 |
| On variational bounds of mutual information B Poole, S Ozair, A Van Den Oord, A Alemi, G Tucker International Conference on Machine Learning, 5171-5180, 2019 | 992 | 2019 |
| 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 | 958 | 2024 |
| Gemma: Open models based on gemini research and technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 902 | 2024 |
| Behavior regularized offline reinforcement learning Y Wu, G Tucker, O Nachum arXiv preprint arXiv:1911.11361, 2019 | 800 | 2019 |
| Widespread macromolecular interaction perturbations in human genetic disorders N Sahni, S Yi, M Taipale, JIF Bass, J Coulombe-Huntington, F Yang, ... Cell 161 (3), 647-660, 2015 | 601 | 2015 |
| Learning to walk via deep reinforcement learning T Haarnoja, S Ha, A Zhou, J Tan, G Tucker, S Levine RSS 2019, 2019 | 589 | 2019 |
| Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling C Riquelme, G Tucker, J Snoek ICLR 2018, 2018 | 471* | 2018 |
| A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways M Taipale, G Tucker, J Peng, I Krykbaeva, ZY Lin, B Larsen, H Choi, ... Cell 158 (2), 434-448, 2014 | 463 | 2014 |
| Sample-efficient reinforcement learning with stochastic ensemble value expansion J Buckman, D Hafner, G Tucker, E Brevdo, H Lee NeurIPS 2018 Oral, 2018 | 414 | 2018 |
| Don't blame the elbo! a linear vae perspective on posterior collapse J Lucas, G Tucker, RB Grosse, M Norouzi Advances in Neural Information Processing Systems 32, 2019 | 369* | 2019 |
| Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models G Tucker, A Mnih, CJ Maddison, D Lawson, J Sohl-Dickstein NIPS 2017 Oral, 2017 | 360 | 2017 |
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu