| Meta-learning with warped gradient descent S Flennerhag, AA Rusu, R Pascanu, F Visin, H Yin, R Hadsell arXiv preprint arXiv:1909.00025, 2019 | 240 | 2019 |
| Transferring Knowledge across Learning Processes S Flennerhag, PG Moreno, ND Lawrence, A Damianou Seventh International Conference on Learning Representations, 2019 | 73 | 2019 |
| Bootstrapped meta-learning S Flennerhag, Y Schroecker, T Zahavy, H van Hasselt, D Silver, S Singh arXiv preprint arXiv:2109.04504, 2021 | 70 | 2021 |
| Discovering evolution strategies via meta-black-box optimization R Lange, T Schaul, Y Chen, T Zahavy, V Dalibard, C Lu, S Singh, ... Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023 | 37 | 2023 |
| Discovering policies with domino: Diversity optimization maintaining near optimality T Zahavy, Y Schroecker, F Behbahani, K Baumli, S Flennerhag, S Hou, ... arXiv preprint arXiv:2205.13521, 2022 | 33 | 2022 |
| Introducing symmetries to black box meta reinforcement learning L Kirsch, S Flennerhag, H Van Hasselt, A Friesen, J Oh, Y Chen Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7202-7210, 2022 | 32 | 2022 |
| Augmenting correlation structures in spatial data using deep generative models K Klemmer, A Koshiyama, S Flennerhag arXiv preprint arXiv:1905.09796, 2019 | 27 | 2019 |
| Discovering attention-based genetic algorithms via meta-black-box optimization R Lange, T Schaul, Y Chen, C Lu, T Zahavy, V Dalibard, S Flennerhag Proceedings of the Genetic and Evolutionary Computation Conference, 929-937, 2023 | 26 | 2023 |
| Discovering diverse nearly optimal policies with successor features T Zahavy, B O'Donoghue, A Barreto, V Mnih, S Flennerhag, S Singh arXiv preprint arXiv:2106.00669, 2021 | 18 | 2021 |
| Quantnet: Transferring learning across systematic trading strategies A Koshiyama, S Flennerhag, SB Blumberg, N Firoozye, P Treleaven arXiv preprint arXiv:2004.03445, 2020 | 16* | 2020 |
| Temporal difference uncertainties as a signal for exploration S Flennerhag, JX Wang, P Sprechmann, F Visin, A Galashov, ... arXiv preprint arXiv:2010.02255, 2020 | 15 | 2020 |
| Breaking the activation function bottleneck through adaptive parameterization S Flennerhag, H Yin, J Keane, M Elliot Advances in Neural Information Processing Systems 31, 2018 | 14 | 2018 |
| Reload: Reinforcement learning with optimistic ascent-descent for last-iterate convergence in constrained mdps T Moskovitz, B O’Donoghue, V Veeriah, S Flennerhag, S Singh, T Zahavy International Conference on Machine Learning, 25303-25336, 2023 | 12 | 2023 |
| Meta-gradients in non-stationary environments J Luketina, S Flennerhag, Y Schroecker, D Abel, T Zahavy, S Singh Conference on Lifelong Learning Agents, 886-901, 2022 | 8 | 2022 |
| Probing transfer in deep reinforcement learning without task engineering AA Rusu, S Flennerhag, D Rao, R Pascanu, R Hadsell Conference on Lifelong Learning Agents, 1231-1254, 2022 | 6 | 2022 |
| Optimistic meta-gradients S Flennerhag, T Zahavy, B O'Donoghue, HP van Hasselt, A György, ... Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Vision-Language Models as a Source of Rewards K Baumli, S Baveja, F Behbahani, H Chan, G Comanici, S Flennerhag, ... arXiv preprint arXiv:2312.09187, 2023 | 3 | 2023 |
| Towards Scalable Meta-Learning S Flennerhag PQDT-Global, 2021 | 2 | 2021 |
| Optimism and Adaptivity in Policy Optimization V Chelu, T Zahavy, A Guez, D Precup, S Flennerhag arXiv preprint arXiv:2306.10587, 2023 | | 2023 |
| Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization R Tjarko Lange, T Schaul, Y Chen, C Lu, T Zahavy, V Dalibard, ... arXiv e-prints, arXiv: 2304.03995, 2023 | | 2023 |
Tom ZahavyStaff Research Scientist, Google DeepMind在 deepmind.com 的电子邮件经过验证
