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Omer Gottesman
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2020 – today
- 2024
- [i17]Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris:
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. CoRR abs/2407.07333 (2024) - 2023
- [j1]Neta Ravid Tannenbaum, Omer Gottesman, Azadeh Assadi
, Mjaye Mazwi, Uri Shalit, Danny Eytan
iCVS - Inferring Cardio-Vascular hidden States from physiological signals available at the bedside. PLoS Comput. Biol. 19(9) (2023) - [c15]Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris, Michael Littman:
Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces. AISTATS 2023: 1390-1410 - [c14]Haotian Fu, Jiayu Yao, Omer Gottesman, Finale Doshi-Velez, George Konidaris:
Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs. ICLR 2023 - [c13]Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor:
TD Convergence: An Optimization Perspective. NeurIPS 2023 - [c12]Akhil Bagaria, Ben Abbatematteo, Omer Gottesman, Matt Corsaro, Sreehari Rammohan, George Dimitri Konidaris:
Effectively Learning Initiation Sets in Hierarchical Reinforcement Learning. NeurIPS 2023 - [i16]Abhishek Sharma, Sonali Parbhoo, Omer Gottesman, Finale Doshi-Velez:
Robust Decision-Focused Learning for Reward Transfer. CoRR abs/2304.03365 (2023) - [i15]Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor:
TD Convergence: An Optimization Perspective. CoRR abs/2306.17750 (2023) - 2022
- [c11]Sam Lobel, Omer Gottesman, Cameron Allen, Akhil Bagaria, George Konidaris:
Optimistic Initialization for Exploration in Continuous Control. AAAI 2022: 7612-7619 - [c10]Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill:
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. CHIL 2022: 397-410 - [c9]Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J. Smola:
Faster Deep Reinforcement Learning with Slower Online Network. NeurIPS 2022 - [i14]Kelly W. Zhang, Omer Gottesman, Finale Doshi-Velez:
A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes. CoRR abs/2208.00250 (2022) - 2021
- [c8]Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, Finale Doshi-Velez:
State Relevance for Off-Policy Evaluation. ICML 2021: 9537-9546 - [c7]Cameron Allen, Neev Parikh, Omer Gottesman, George Konidaris:
Learning Markov State Abstractions for Deep Reinforcement Learning. NeurIPS 2021: 8229-8241 - [i13]Cameron Allen, Neev Parikh, Omer Gottesman, George Konidaris:
Learning Markov State Abstractions for Deep Reinforcement Learning. CoRR abs/2106.04379 (2021) - [i12]Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, Finale Doshi-Velez:
State Relevance for Off-Policy Evaluation. CoRR abs/2109.06310 (2021) - [i11]Omer Gottesman, Kavosh Asadi, Cameron Allen, Sam Lobel, George Konidaris, Michael Littman:
Coarse-Grained Smoothness for RL in Metric Spaces. CoRR abs/2110.12276 (2021) - [i10]Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill:
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. CoRR abs/2111.14272 (2021) - [i9]Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Michael L. Littman, Alexander J. Smola:
Deep Q-Network with Proximal Iteration. CoRR abs/2112.05848 (2021) - 2020
- [c6]Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez:
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. ICML 2020: 3658-3667 - [c5]Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson:
Learning to search efficiently for causally near-optimal treatments. NeurIPS 2020 - [i8]Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez:
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. CoRR abs/2002.03478 (2020) - [i7]Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson:
Learning to search efficiently for causally near-optimal treatments. CoRR abs/2007.00973 (2020)
2010 – 2019
- 2019
- [c4]Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez:
Combining parametric and nonparametric models for off-policy evaluation. ICML 2019: 2366-2375 - [i6]Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez:
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. CoRR abs/1901.04670 (2019) - [i5]Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez:
Combining Parametric and Nonparametric Models for Off-Policy Evaluation. CoRR abs/1905.05787 (2019) - [i4]Omer Gottesman, Weiwei Pan, Finale Doshi-Velez:
A general method for regularizing tensor decomposition methods via pseudo-data. CoRR abs/1905.10424 (2019) - 2018
- [c3]Omer Gottesman, Weiwei Pan, Finale Doshi-Velez:
Weighted Tensor Decomposition for Learning Latent Variables with Partial Data. AISTATS 2018: 1664-1672 - [c2]Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez:
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. AMIA 2018 - [c1]Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, Emma Brunskill:
Representation Balancing MDPs for Off-policy Policy Evaluation. NeurIPS 2018: 2649-2658 - [i3]Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill:
Representation Balancing MDPs for Off-Policy Policy Evaluation. CoRR abs/1805.09044 (2018) - [i2]Omer Gottesman, Fredrik D. Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li-Wei H. Lehman, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David A. Sontag, Finale Doshi-Velez:
Evaluating Reinforcement Learning Algorithms in Observational Health Settings. CoRR abs/1805.12298 (2018) - [i1]Aniruddh Raghu, Omer Gottesman, Yao Liu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill:
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters. CoRR abs/1807.01066 (2018)
Coauthor Index
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last updated on 2024-08-26 21:16 CEST by the dblp team
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