1 citations · 1 across the 7 of their papers we have counts for
7 papers
APRIL: Annotations for Policy evaluation with Reliable Inference from LLMs
Aishwarya Mandyam, Kalyani Limaye, Barbara E. Engelhardt +1
Off-policy evaluation (OPE) estimates the value of a contextual bandit policy prior to deployment. As such, OPE plays a critical role in ensuring safety in high-stakes domains such…
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
Emily Alsentzer, Marie-Laure Charpignon, Bill Chen +90
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025…
PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data
Aishwarya Mandyam, Jason Meng, Ge Gao +4
Off-policy evaluation (OPE) methods estimate the value of a new reinforcement learning (RL) policy prior to deployment. Recent advances have shown that leveraging auxiliary dataset…
CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation
Aishwarya Mandyam, Shengpu Tang, Jiayu Yao +2
Off-policy evaluation (OPE) is critical for applying contextual bandit algorithms to high-stakes decision-making settings such as healthcare, where new treatment policies must be e…
Adaptive Interventions with User-Defined Goals for Health Behavior Change
Aishwarya Mandyam, Matthew Jörke, William Denton +2
Promoting healthy lifestyle behaviors remains a major public health concern, particularly due to their crucial role in preventing chronic conditions such as cancer, heart disease,…
Kernel Density Bayesian Inverse Reinforcement Learning
Aishwarya Mandyam, Didong Li, Jiayu Yao +3
Inverse reinforcement learning (IRL) methods infer an agent's reward function using demonstrations of expert behavior. A Bayesian IRL approach models a distribution over candidate…
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
Aishwarya Mandyam, Andrew Jones, Jiayu Yao +2
Reinforcement learning (RL) is an effective framework for solving sequential decision-making tasks. However, applying RL methods in medical care settings is challenging in part due…