| Guidelines for reinforcement learning in healthcare O Gottesman, F Johansson, M Komorowski, A Faisal, D Sontag, ... Nature medicine 25 (1), 16-18, 2019 | 461 | 2019 |
| Science with a wide-field UV transient explorer I Sagiv, A Gal-Yam, EO Ofek, E Waxman, O Aharonson, SR Kulkarni, ... The Astronomical Journal 147 (4), 79, 2014 | 153 | 2014 |
| Evaluating reinforcement learning algorithms in observational health settings O Gottesman, F Johansson, J Meier, J Dent, D Lee, S Srinivasan, L Zhang, ... arXiv preprint arXiv:1805.12298, 2018 | 146* | 2018 |
| Nonmonotonic aging and memory retention in disordered mechanical systems Y Lahini, O Gottesman, A Amir, SM Rubinstein Physical review letters 118 (8), 085501, 2017 | 118 | 2017 |
| Improving sepsis treatment strategies by combining deep and kernel-based reinforcement learning X Peng, Y Ding, D Wihl, O Gottesman, M Komorowski, HL Li-wei, A Ross, ... AMIA Annual Symposium Proceedings 2018, 887, 2018 | 101 | 2018 |
| Representation balancing mdps for off-policy policy evaluation Y Liu, O Gottesman, A Raghu, M Komorowski, AA Faisal, F Doshi-Velez, ... Advances in neural information processing systems 31, 2018 | 79 | 2018 |
| Interpretable off-policy evaluation in reinforcement learning by highlighting influential transitions O Gottesman, J Futoma, Y Liu, S Parbhoo, L Celi, E Brunskill, ... International Conference on Machine Learning, 3658-3667, 2020 | 61 | 2020 |
| Behaviour policy estimation in off-policy policy evaluation: Calibration matters A Raghu, O Gottesman, Y Liu, M Komorowski, A Faisal, F Doshi-Velez, ... arXiv preprint arXiv:1807.01066, 2018 | 43 | 2018 |
| Learning markov state abstractions for deep reinforcement learning C Allen, N Parikh, O Gottesman, G Konidaris Advances in Neural Information Processing Systems 34, 8229-8241, 2021 | 38 | 2021 |
| Multiple extinction routes in stochastic population models O Gottesman, B Meerson Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 85 (2 …, 2012 | 37 | 2012 |
| Combining parametric and nonparametric models for off-policy evaluation O Gottesman, Y Liu, S Sussex, E Brunskill, F Doshi-Velez arXiv preprint arXiv:1905.05787, 2019 | 36 | 2019 |
| A state variable for crumpled thin sheets O Gottesman, J Andrejevic, CH Rycroft, SM Rubinstein Communications Physics 1 (1), 70, 2018 | 33 | 2018 |
| On the incompressibility of cylindrical origami patterns F Bös, M Wardetzky, E Vouga, O Gottesman Journal of Mechanical Design 139 (2), 021404, 2017 | 26 | 2017 |
| Furrows in the wake of propagating d-cones O Gottesman, E Efrati, SM Rubinstein Nature communications 6 (1), 7232, 2015 | 13 | 2015 |
| Optimistic initialization for exploration in continuous control S Lobel, O Gottesman, C Allen, A Bagaria, G Konidaris Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7612-7619, 2022 | 11 | 2022 |
| Improving counterfactual reasoning with kernelised dynamic mixing models S Parbhoo, O Gottesman, AS Ross, M Komorowski, A Faisal, I Bon, ... PloS one 13 (11), e0205839, 2018 | 10 | 2018 |
| Learning to search efficiently for causally near-optimal treatments S Håkansson, V Lindblom, O Gottesman, FD Johansson Advances in Neural Information Processing Systems 33, 1333-1344, 2020 | 8 | 2020 |
| Faster deep reinforcement learning with slower online network K Asadi, R Fakoor, O Gottesman, T Kim, M Littman, AJ Smola Advances in Neural Information Processing Systems 35, 19944-19955, 2022 | 5* | 2022 |
| Localized patterns in crushed conical shells O Gottesman, E Vouga, SM Rubinstein, L Mahadevan Europhysics Letters 124 (1), 14005, 2018 | 5 | 2018 |
| Td convergence: An optimization perspective K Asadi, S Sabach, Y Liu, O Gottesman, R Fakoor Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
Yoav LahiniTel Aviv UniversityVerified email at tauex.tau.ac.il
