Search Results for author: Aishwarya Mandyam

Found 6 papers, 2 papers with code

CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation

no code implementations11 Dec 2024 Aishwarya Mandyam, Shengpu Tang, Jiayu Yao, Jenna Wiens, Barbara E. Engelhardt

Our empirical results show that when the reward model is misspecified and the annotations are imperfect, it is most beneficial to use the annotations only in the DM portion of a DR estimator.

counterfactual Off-policy evaluation

Adaptive Interventions with User-Defined Goals for Health Behavior Change

1 code implementation16 Nov 2023 Aishwarya Mandyam, Matthew Jörke, William Denton, Barbara E. Engelhardt, Emma Brunskill

Tailoring advice to a person's unique goals, preferences, and life circumstances is a critical component of health coaching that has been underutilized in adaptive algorithms for mobile health interventions.

Thompson Sampling

Kernel Density Bayesian Inverse Reinforcement Learning

1 code implementation13 Mar 2023 Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara E. Engelhardt

In this work, we incorporate existing domain-specific data to achieve better posterior concentration rates.

BIRL Density Estimation +3

Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations

no code implementations6 Oct 2021 Aishwarya Mandyam, Andrew Jones, Jiayu Yao, Krzysztof Laudanski, Barbara Engelhardt

CFQI uses a compositional $Q$-value function with separate modules for each task variant, allowing it to take advantage of shared knowledge while learning distinct policies for each variant.

Decision Making Navigate +5

Nested Policy Reinforcement Learning for Clinical Decision Support

no code implementations29 Sep 2021 Aishwarya Mandyam, Andrew Jones, Krzysztof Laudanski, Barbara Engelhardt

Off-policy reinforcement learning (RL) has proven to be a powerful framework for guiding agents' actions in environments with stochastic rewards and unknown or noisy state dynamics.

Decision Making Navigate +4

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