45 citations · 46 across the 4 of their papers we have counts for
10 papers
Coarse-Grained Smoothness for RL in Metric Spaces
Omer Gottesman, Kavosh Asadi, Cameron Allen +3
Principled decision-making in continuous state--action spaces is impossible without some assumptions. A common approach is to assume Lipschitz continuity of the Q-function. We show…
State Relevance for Off-Policy Evaluation
Simon P. Shen, Yecheng Jason Ma, Omer Gottesman +1
Importance sampling-based estimators for off-policy evaluation (OPE) are valued for their simplicity, unbiasedness, and reliance on relatively few assumptions. However, the varianc…
Learning to search efficiently for causally near-optimal treatments
Samuel Håkansson, Viktor Lindblom, Omer Gottesman +1
Finding an effective medical treatment often requires a search by trial and error. Making this search more efficient by minimizing the number of unnecessary trials could lower both…
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Omer Gottesman, Joseph Futoma, Yao Liu +4
Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deplo…
A general method for regularizing tensor decomposition methods via pseudo-data
Omer Gottesman, Weiwei Pan, Finale Doshi-Velez
Tensor decomposition methods allow us to learn the parameters of latent variable models through decomposition of low-order moments of data. A significant limitation of these algori…
Combining Parametric and Nonparametric Models for Off-Policy Evaluation
Omer Gottesman, Yao Liu, Scott Sussex +2
We consider a model-based approach to perform batch off-policy evaluation in reinforcement learning. Our method takes a mixture-of-experts approach to combine parametric and non-pa…