17 citations · 25 across the 5 of their papers we have counts for
6 papers
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation
Ramtin Keramati, Omer Gottesman, Leo Anthony Celi +2
Off-policy policy evaluation methods for sequential decision making can be used to help identify if a proposed decision policy is better than a current baseline policy. However, a…
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
Evan Zheran Liu, Ramtin Keramati, Sudarshan Seshadri +4
Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables f…
Value Driven Representation for Human-in-the-Loop Reinforcement Learning
Ramtin Keramati, Emma Brunskill
Interactive adaptive systems powered by Reinforcement Learning (RL) have many potential applications, such as intelligent tutoring systems. In such systems there is typically an ex…
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky +1
When observed decisions depend only on observed features, off-policy policy evaluation (OPE) methods for sequential decision making problems can estimate the performance of evaluat…
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy
Ramtin Keramati, Christoph Dann, Alex Tamkin +1
While maximizing expected return is the goal in most reinforcement learning approaches, risk-sensitive objectives such as conditional value at risk (CVaR) are more suitable for man…
Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
Ramtin Keramati, Jay Whang, Patrick Cho +1
Humans learn to play video games significantly faster than the state-of-the-art reinforcement learning (RL) algorithms. People seem to build simple models that are easy to learn to…