10 citations · 17 across the 9 of their papers we have counts for
9 papers
Quantifying Potential Observation Missingness in Inverse Reinforcement Learning
Leo Benac, Abhishek Sharma, Alihan Huyuk +1
Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-making behavior. Many variants o…
Strategically Linked Decisions in Long-Term Planning and Reinforcement Learning
Alihan Hüyük, Finale Doshi-Velez
Long-term planning, as in reinforcement learning (RL), involves finding strategies: actions that collectively work toward a goal rather than individually optimizing their immediate…
Disentangling Recognition and Decision Regrets in Image-Based Reinforcement Learning
Alihan Hüyük, Arndt Ryo Koblitz, Atefeh Mohajeri +1
In image-based reinforcement learning (RL), policies usually operate in two steps: first extracting lower-dimensional features from raw images (the "recognition" step), and then ta…
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL
Hao Sun, Alihan Hüyük, Mihaela van der Schaar
In this study, we aim to enhance the arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization. We identify a previously overlooked objecti…
Neural Laplace Control for Continuous-time Delayed Systems
Samuel Holt, Alihan Hüyük, Zhaozhi Qian +2
Many real-world offline reinforcement learning (RL) problems involve continuous-time environments with delays. Such environments are characterized by two distinctive features: firs…
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
Alicia Curth, Alihan Hüyük, Mihaela van der Schaar
We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial. This type of adaptive clinical trial…
Inferring Lexicographically-Ordered Rewards from Preferences
Alihan Hüyük, William R. Zame, Mihaela van der Schaar
Modeling the preferences of agents over a set of alternatives is a principal concern in many areas. The dominant approach has been to find a single reward/utility function with the…
Inverse Contextual Bandits: Learning How Behavior Evolves over Time
Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar
Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes, such as in healthcare. Though convent…
Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms
Alihan Hüyük, Cem Tekin
We analyze the regret of combinatorial Thompson sampling (CTS) for the combinatorial multi-armed bandit with probabilistically triggered arms under the semi-bandit feedback setting…