| Tighter problem-dependent regret bounds in reinforcement learning without domain knowledge using value function bounds A Zanette, E Brunskill International Conference on Machine Learning, 2019 | 178 | 2019 |
| Learning Near Optimal Policies with Low Inherent Bellman Error A Zanette, A Lazaric, M Kochenderfer, E Brunskill International Conference on Machine Learning, 2020 | 111 | 2020 |
| Frequentist regret bounds for randomized least-squares value iteration A Zanette, D Brandfonbrener, E Brunskill, M Pirotta, A Lazaric International Conference on Artificial Intelligence and Statistics, 1954-1964, 2020 | 80 | 2020 |
| Exponential lower bounds for batch reinforcement learning: Batch rl can be exponentially harder than online rl A Zanette International Conference on Machine Learning, 12287-12297, 2021 | 38 | 2021 |
| Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration A Zanette, A Lazaric, MJ Kochenderfer, E Brunskill Advances in Neural Information Processing Systems, 2020 | 34 | 2020 |
| Provable benefits of actor-critic methods for offline reinforcement learning A Zanette, MJ Wainwright, E Brunskill Advances in neural information processing systems 34, 13626-13640, 2021 | 28 | 2021 |
| Almost horizon-free structure-aware best policy identification with a generative model A Zanette, MJ Kochenderfer, E Brunskill Advances in Neural Information Processing Systems 32, 2019 | 27 | 2019 |
| Limiting extrapolation in linear approximate value iteration A Zanette, A Lazaric, MJ Kochenderfer, E Brunskill Advances in Neural Information Processing Systems 32, 2019 | 22 | 2019 |
| Robust super-level set estimation using gaussian processes A Zanette, J Zhang, MJ Kochenderfer Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018 | 19 | 2018 |
| Cautiously optimistic policy optimization and exploration with linear function approximation A Zanette, CA Cheng, A Agarwal Conference on Learning Theory, 4473-4525, 2021 | 18 | 2021 |
| Problem dependent reinforcement learning bounds which can identify bandit structure in mdps A Zanette, E Brunskill International Conference on Machine Learning, 5747-5755, 2018 | 12 | 2018 |
| Design of experiments for stochastic contextual linear bandits A Zanette, K Dong, JN Lee, E Brunskill Advances in Neural Information Processing Systems 34, 22720-22731, 2021 | 5 | 2021 |
| Information directed reinforcement learning A Zanette, R Sarkar Tech. Rep., Technical report, Technical report, 2017 | 4 | 2017 |
| Enriching the finite element method with meshfree particles in structural mechanics A Zanette, M Ferronato, C Janna International Journal for Numerical Methods in Engineering 110 (7), 675-700, 2017 | 2 | 2017 |
| Bellman Residual Orthogonalization for Offline Reinforcement Learning A Zanette, MJ Wainwright arXiv preprint arXiv:2203.12786, 2022 | 1 | 2022 |
| Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning A Zanette, MJ Wainwright arXiv preprint arXiv:2206.00796, 2022 | | 2022 |
| Reinforcement Learning: When Can We Do Sample Efficient Exploration? A Zanette Stanford University, 2021 | | 2021 |
| Enriching the Finite Element Method with meshfree techniques in structural mechanics A Zanette, M Ferronato, C Janna PAMM 15 (1), 691-692, 2015 | | 2015 |
| Robust Super-Level Set Estimation using Gaussian Processes Download PDF Open Website A Zanette, J Zhang, M Kochenderfer | | |
Emma BrunskillAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
