Weighted Tensor Decomposition for Learning Latent Variables with Partial Data. - Omer Gottesman; Weiwei Pan; Finale Doshi-Velez (AISTATS, 2018)

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. - Xuefeng Peng; Yi Ding; David Wihl; Omer Gottesman; Matthieu Komorowski; Li-Wei H. Lehman; Andrew Slavin Ross; Aldo Faisal; Finale Doshi-Velez (AMIA, 2018)

Representation Balancing MDPs for Off-policy Policy Evaluation. - Yao Liu 009; Omer Gottesman; Aniruddh Raghu; Matthieu Komorowski; Aldo A. Faisal; Finale Doshi-Velez; Emma Brunskill (NeurIPS, 2018)

Evaluating Reinforcement Learning Algorithms in Observational Health Settings. - 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; et al. (CoRR, 2018)

Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters. - Aniruddh Raghu; Omer Gottesman; Yao Liu 009; Matthieu Komorowski; Aldo Faisal; Finale Doshi-Velez; Emma Brunskill (CoRR, 2018)

Combining parametric and nonparametric models for off-policy evaluation. - Omer Gottesman; Yao Liu 009; Scott Sussex; Emma Brunskill; Finale Doshi-Velez (ICML, 2019)

A general method for regularizing tensor decomposition methods via pseudo-data. - Omer Gottesman; Weiwei Pan; Finale Doshi-Velez (CoRR, 2019)

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. - Omer Gottesman; Joseph Futoma; Yao Liu 009; Sonali Parbhoo; Leo A. Celi; Emma Brunskill; Finale Doshi-Velez (ICML, 2020)

Learning to search efficiently for causally near-optimal treatments. - Samuel Håkansson; Viktor Lindblom; Omer Gottesman; Fredrik D. Johansson (NeurIPS, 2020)

State Relevance for Off-Policy Evaluation. - Simon P. Shen; Yecheng Jason Ma 001; Omer Gottesman; Finale Doshi-Velez (ICML, 2021)

Learning Markov State Abstractions for Deep Reinforcement Learning. - Cameron Allen; Neev Parikh; Omer Gottesman; George Konidaris 001 (NeurIPS, 2021)

Coarse-Grained Smoothness for RL in Metric Spaces. - Omer Gottesman; Kavosh Asadi; Cameron Allen; Sam Lobel; George Konidaris 001; Michael Littman 002 (CoRR, 2021)

Deep Q-Network with Proximal Iteration. - Kavosh Asadi; Rasool Fakoor; Omer Gottesman; Michael L. Littman; Alexander J. Smola (CoRR, 2021)

Optimistic Initialization for Exploration in Continuous Control. - Sam Lobel; Omer Gottesman; Cameron Allen; Akhil Bagaria; George Konidaris 001 (AAAI, 2022)

Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. - Ramtin Keramati; Omer Gottesman; Leo Anthony Celi; Finale Doshi-Velez; Emma Brunskill (CHIL, 2022)

Faster Deep Reinforcement Learning with Slower Online Network. - Kavosh Asadi; Rasool Fakoor; Omer Gottesman; Taesup Kim; Michael L. Littman; Alexander J. Smola (NeurIPS, 2022)

A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes. - Kelly W. Zhang; Omer Gottesman; Finale Doshi-Velez (CoRR, 2022)

Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces. - Omer Gottesman; Kavosh Asadi; Cameron S. Allen; Samuel Lobel; George Konidaris 001; Michael Littman 002 (AISTATS, 2023)

Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs. - Haotian Fu; Jiayu Yao; Omer Gottesman; Finale Doshi-Velez; George Konidaris 001 (ICLR, 2023)

TD Convergence: An Optimization Perspective. - Kavosh Asadi; Shoham Sabach; Yao Liu 009; Omer Gottesman; Rasool Fakoor (NeurIPS, 2023)

Effectively Learning Initiation Sets in Hierarchical Reinforcement Learning. - Akhil Bagaria; Ben Abbatematteo; Omer Gottesman; Matt Corsaro; Sreehari Rammohan; George Dimitri Konidaris (NeurIPS, 2023)

Robust Decision-Focused Learning for Reward Transfer. - Abhishek Sharma; Sonali Parbhoo; Omer Gottesman; Finale Doshi-Velez (CoRR, 2023)

iCVS - Inferring Cardio-Vascular hidden States from physiological signals available at the bedside. - Neta Ravid Tannenbaum; Omer Gottesman; Azadeh Assadi; Mjaye Mazwi; Uri Shalit; Danny Eytan (PLoS Comput. Biol., 2023)

Decision-Focused Model-based Reinforcement Learning for Reward Transfer. - Abhishek Sharma; Sonali Parbhoo; Omer Gottesman; Finale Doshi-Velez (MLHC, 2024)

Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. - Cameron Allen; Aaron Kirtland; Ruo Yu Tao; Sam Lobel; Daniel Scott; Nicholas Petrocelli; Omer Gottesman; Ronald Parr; Michael L. Littman; George Konidaris 001 (NeurIPS, 2024)

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces. - Saket Tiwari; Omer Gottesman; George Konidaris 001 (ICLR, 2025)

The Role of the Marketplace Operator in Inducing Competition. - Tiffany Ding; Dominique Perrault-Joncas; Orit Ronen; Michael I. Jordan; Dirk Bergemann; Dean P. Foster; Omer Gottesman (CoRR, 2025)

Efficient RL for optimizing conversation level outcomes with an LLM-based tutor. - Hyunji Nam; Omer Gottesman; Amy Zhang; Dean P. Foster; Emma Brunskill; Lyle H. Ungar (CoRR, 2025)

Structure-Informed Deep Reinforcement Learning for Inventory Management. - Alvaro Maggiar; Sohrab Andaz; Akhil Bagaria; Carson Eisenach; Dean P. Foster; Omer Gottesman; Dominique Perrault-Joncas (CoRR, 2025)