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Found 16 papers, 6 papers with code
Defining Expertise: Applications to Treatment Effect Estimation
Actions of an expert thus naturally encode part of their domain knowledge, and can help make inferences within the same domain: Knowing doctors try to prescribe the best treatment for their patients, we can tell treatments prescribed more frequently are likely to be more effective.
Adaptive Experiment Design with Synthetic Controls
Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients.
When is Off-Policy Evaluation Useful? A Data-Centric Perspective
On the one hand, it brings opportunities for safe policy improvement under high-stakes scenarios like clinical guidelines.
Online Decision Mediation
Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior: At each time, the algorithm observes an action chosen by a fallible agent, and decides whether to *accept* that agent's decision, *intervene* with an alternative, or *request* the expert's opinion.
Inverse Decision Modeling: Learning Interpretable Representations of Behavior
Decision analysis deals with modeling and enhancing decision processes.
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
Understanding human behavior from observed data is critical for transparency and accountability in decision-making.
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL
We identify a previously overlooked objective of query dependency in such optimization and elucidate two ensuing challenges that impede the successful and economical design of prompt optimization techniques.
Neural Laplace Control for Continuous-time Delayed Systems
Many real-world offline reinforcement learning (RL) problems involve continuous-time environments with delays.
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial.
Inferring Lexicographically-Ordered Rewards from Preferences
Modeling the preferences of agents over a set of alternatives is a principal concern in many areas.
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Significant effort has been placed on developing decision support tools to improve patient care.
Inverse Contextual Bandits: Learning How Behavior Evolves over Time
Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes, such as in healthcare.
Learning "What-if" Explanations for Sequential Decision-Making
Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior -- i. e. trajectories of observations and actions made by an expert maximizing some unknown reward function -- is essential for introspecting and auditing policies in different institutions.
Lexicographic Multiarmed Bandit
The algorithm we propose for the second setting also attains bounded regret for the multiarmed bandit with satisficing objectives.
Thompson Sampling for Combinatorial Network Optimization in Unknown Environments
Influence maximization, adaptive routing, and dynamic spectrum allocation all require choosing the right action from a large set of alternatives.
Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms
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.
