Omer Gottesman

41 papers A* 13A 2Misc 1Journal 23Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers. CoRR Sikata Bela Sengupta, Guangyi Liu, Omer Gottesman, Joseph W. Durham, Michael Kearns, Aaron Roth, Michael Caldara
2025	J	`jnl`	Efficient RL for optimizing conversation level outcomes with an LLM-based tutor. CoRR Hyunji Nam, Omer Gottesman, Amy Zhang, Dean P. Foster, Emma Brunskill, Lyle H. Ungar
2025	A*	`conf`	Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces. ICLR Saket Tiwari, Omer Gottesman, George Konidaris
2025	J	`jnl`	Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces. CoRR Saket Tiwari, Omer Gottesman, George Konidaris
2025	J	`jnl`	Structure-Informed Deep Reinforcement Learning for Inventory Management. CoRR Alvaro Maggiar, Sohrab Andaz, Akhil Bagaria, Carson Eisenach, Dean P. Foster, Omer Gottesman, Dominique Perrault-Joncas
2025	J	`jnl`	The Role of the Marketplace Operator in Inducing Competition. CoRR Tiffany Ding, Dominique Perrault-Joncas, Orit Ronen, Michael I. Jordan, Dirk Bergemann, Dean P. Foster, Omer Gottesman
2024	—	`conf`	Decision-Focused Model-based Reinforcement Learning for Reward Transfer. MLHC Abhishek Sharma, Sonali Parbhoo, Omer Gottesman, Finale Doshi-Velez
2024	A*	`conf`	Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. NeurIPS Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris
2024	J	`jnl`	Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. CoRR Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris
2023	A	`conf`	Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces. AISTATS Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris, Michael Littman
2023	A*	`conf`	Effectively Learning Initiation Sets in Hierarchical Reinforcement Learning. NeurIPS Akhil Bagaria, Ben Abbatematteo, Omer Gottesman, Matt Corsaro, Sreehari Rammohan, George Dimitri Konidaris
2023	A*	`conf`	Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs. ICLR Haotian Fu, Jiayu Yao, Omer Gottesman, Finale Doshi-Velez, George Konidaris
2023	J	`jnl`	Robust Decision-Focused Learning for Reward Transfer. CoRR Abhishek Sharma, Sonali Parbhoo, Omer Gottesman, Finale Doshi-Velez
2023	A*	`conf`	TD Convergence: An Optimization Perspective. NeurIPS Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor
2023	J	`jnl`	TD Convergence: An Optimization Perspective. CoRR Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor
2023	J	`jnl`	iCVS - Inferring Cardio-Vascular hidden States from physiological signals available at the bedside. PLoS Comput. Biol. Neta Ravid Tannenbaum, Omer Gottesman, Azadeh Assadi, Mjaye Mazwi, Uri Shalit, Danny Eytan
2022	J	`jnl`	A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes. CoRR Kelly W. Zhang, Omer Gottesman, Finale Doshi-Velez
2022	A*	`conf`	Faster Deep Reinforcement Learning with Slower Online Network. NeurIPS Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J. Smola
2022	—	`conf`	Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. CHIL Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill
2022	A*	`conf`	Optimistic Initialization for Exploration in Continuous Control. AAAI Sam Lobel, Omer Gottesman, Cameron Allen, Akhil Bagaria, George Konidaris
2021	J	`jnl`	Coarse-Grained Smoothness for RL in Metric Spaces. CoRR Omer Gottesman, Kavosh Asadi, Cameron Allen, Sam Lobel, George Konidaris, Michael Littman
2021	J	`jnl`	Deep Q-Network with Proximal Iteration. CoRR Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Michael L. Littman, Alexander J. Smola
2021	J	`jnl`	Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. CoRR Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill
2021	A*	`conf`	Learning Markov State Abstractions for Deep Reinforcement Learning. NeurIPS Cameron Allen, Neev Parikh, Omer Gottesman, George Konidaris
2021	J	`jnl`	Learning Markov State Abstractions for Deep Reinforcement Learning. CoRR Cameron Allen, Neev Parikh, Omer Gottesman, George Konidaris
2021	A*	`conf`	State Relevance for Off-Policy Evaluation. ICML Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, Finale Doshi-Velez
2021	J	`jnl`	State Relevance for Off-Policy Evaluation. CoRR Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, Finale Doshi-Velez
2020	A*	`conf`	Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. ICML Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez
2020	J	`jnl`	Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. CoRR Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez
2020	A*	`conf`	Learning to search efficiently for causally near-optimal treatments. NeurIPS Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson
2020	J	`jnl`	Learning to search efficiently for causally near-optimal treatments. CoRR Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson
2019	J	`jnl`	A general method for regularizing tensor decomposition methods via pseudo-data. CoRR Omer Gottesman, Weiwei Pan, Finale Doshi-Velez
2019	J	`jnl`	Combining Parametric and Nonparametric Models for Off-Policy Evaluation. CoRR Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez
2019	A*	`conf`	Combining parametric and nonparametric models for off-policy evaluation. ICML Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez
2019	J	`jnl`	Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. CoRR Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez
2018	J	`jnl`	Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters. CoRR Aniruddh Raghu, Omer Gottesman, Yao Liu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill
2018	J	`jnl`	Evaluating Reinforcement Learning Algorithms in Observational Health Settings. CoRR 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
2018	Misc	`conf`	Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. AMIA Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez
2018	J	`jnl`	Representation Balancing MDPs for Off-Policy Policy Evaluation. CoRR Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill
2018	A*	`conf`	Representation Balancing MDPs for Off-policy Policy Evaluation. NeurIPS Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, Emma Brunskill
2018	A	`conf`	Weighted Tensor Decomposition for Learning Latent Variables with Partial Data. AISTATS Omer Gottesman, Weiwei Pan, Finale Doshi-Velez