no code implementations • 30 Jun 2010 • Eric B. Laber, Min Qian, Dan J. Lizotte, William E. Pelham, Susan A. Murphy
We then review an interesting challenge, that of nonregularity that often arises in this area.
no code implementations • 28 Jun 2017 • Huitian Lei, Yangyi Lu, Ambuj Tewari, Susan A. Murphy
Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative and highly personalized health interventions.
no code implementations • ICML 2017 • Walter H. Dempsey, Alexander Moreno, Christy K. Scott, Michael L. Dennis, David H. Gustafson, Susan A. Murphy, James M. Rehg
We present a parameter learning method for GLM emissions and survival model fitting, and present promising results on both synthetic data and an mHealth drug use dataset.
no code implementations • NeurIPS 2020 • Kelly W. Zhang, Lucas Janson, Susan A. Murphy
As bandit algorithms are increasingly utilized in scientific studies and industrial applications, there is an associated increasing need for reliable inference methods based on the resulting adaptively-collected data.
no code implementations • 28 Mar 2020 • Marianne Menictas, Sabina Tomkins, Susan A. Murphy
For example, a mobile health (mHealth) application designed to increase physical activity must make contextually relevant suggestions to motivate users.
no code implementations • NeurIPS 2021 • Kelly W. Zhang, Lucas Janson, Susan A. Murphy
Yet there is a lack of general methods for conducting statistical inference using more complex models on data collected with (contextual) bandit algorithms; for example, current methods cannot be used for valid inference on parameters in a logistic regression model for a binary reward.
no code implementations • 16 Sep 2021 • Sarah Rathnam, Susan A. Murphy, Finale Doshi-Velez
In batch reinforcement learning, there can be poorly explored state-action pairs resulting in poorly learned, inaccurate models and poorly performing associated policies.
no code implementations • 14 Feb 2022 • Kelly W. Zhang, Lucas Janson, Susan A. Murphy
In this work, we focus on longitudinal user data collected by a large class of adaptive sampling algorithms that are designed to optimize treatment decisions online using accruing data from multiple users.
no code implementations • 28 Feb 2022 • Martin Cousineau, Vedat Verter, Susan A. Murphy, Joelle Pineau
In the absence of randomized controlled and natural experiments, it is necessary to balance the distributions of (observable) covariates of the treated and control groups in order to obtain an unbiased estimate of a causal effect of interest; otherwise, a different effect size may be estimated, and incorrect recommendations may be given.
1 code implementation • 8 Jun 2022 • Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education.
1 code implementation • 15 Aug 2022 • Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy
Dental disease is one of the most common chronic diseases despite being largely preventable.
no code implementations • 17 May 2023 • Karine Karine, Predrag Klasnja, Susan A. Murphy, Benjamin M. Marlin
Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community.
no code implementations • 20 Jun 2023 • Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez
We demonstrate that planning under a lower discount factor produces an identical optimal policy to planning using any prior on the transition matrix that has the same distribution for all states and actions.
1 code implementation • 5 Feb 2024 • Anna L. Trella, Walter Dempsey, Finale Doshi-Velez, Susan A. Murphy
We consider the stochastic multi-armed bandit problem with non-stationary rewards.
no code implementations • 26 Feb 2024 • Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Iris Yan, Finale Doshi-Velez, Susan A. Murphy
This paper proposes algorithm fidelity as a critical requirement for deploying online RL algorithms in clinical trials.
no code implementations • 9 Mar 2024 • Zana Buçinca, Siddharth Swaroop, Amanda E. Paluch, Susan A. Murphy, Krzysztof Z. Gajos
We instantiate our approach with two objectives: human-AI accuracy on the decision-making task and human learning about the task, and learn policies that optimize these two objectives from previous human-AI interaction data.
no code implementations • 16 Mar 2024 • Ziping Xu, Kelly W. Zhang, Susan A. Murphy
In the realm of Reinforcement Learning (RL), online RL is often conceptualized as an optimization problem, where an algorithm interacts with an unknown environment to minimize cumulative regret.