Susan A. Murphy

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Action Centered Contextual Bandits

Kristjan H. Greenewald, Ambuj Tewari, Susan A. Murphy, 2017, NIPS.

Inferring bounds on the performance of a control policy from a sample of one-step system transitions

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2009 .

Addressing the Computational Challenges of Personalized Medicine (Dagstuhl Seminar 17472)

Holger Fröhlich, Susan A. Murphy, Niko Beerenwinkel, 2017, Dagstuhl Reports.

Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K).

Deborah Estrin, James M. Rehg, Gregory D. Abowd, 2017, IEEE pervasive computing.

Using data to inform sequential, individualized clinical decision making

Susan A. Murphy, Eric B. Laber, Inbal Nahum-Shani, 2011 .

qlaci R package for using q-learning to construct adaptive interventions using data from a SMART (Version 1.0)

Susan A. Murphy, Kun Deng, Aaron T. Wagner, 2014 .

Computing bounds for kernel-based policy evaluation in reinforcement learning

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2010 .

Three Lectures on Design of Experiments and Data Analysis in Sequential Decision Making

Susan A. Murphy, 2015 .

Generating Informative Trajectories by using Bounds on the Return of Control Policies

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2010 .

Maximum likelihood estimation of the structural nested mean model using SAS PROC NLP: With application to the study of time-varying moderators of the effect of weight change on quality of life

Susan A. Murphy, Daniel Almirall, Cynthia J. Coffman, 2010 .

Budgeted Learning for Developing Personalized Treatment

Russ Greiner, Susan A. Murphy, Kun Deng, 2014, 2014 13th International Conference on Machine Learning and Applications.

Inferring bounds on the performance of a control policy from a sample of trajectories

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2009, 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning.

Causal inference for longitudinal data

Susan A. Murphy, 2003 .

On SMART and factorial designs: Application to mHealth research

Susan A. Murphy, Inbal Nahum-Shani, Peng Liao, 2014 .

The Latent Curriculum: Breaking Conceptual Barriers to Information Architecture

Susan A. Murphy, Catherine Boden, 2012 .

Monographs on statistics and applied probability

Susan A. Murphy, Niels Keiding, Richard L. Smith, 1990 .

Introduction to Part IV: Predictors to mHealth Interventions

James M. Rehg, Susan A. Murphy, Santosh Kumar, 2017, Mobile Health - Sensors, Analytic Methods, and Applications.

Estimation Monte Carlo sans modèle de politiques de décision

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2011, Rev. d'Intelligence Artif..

Personalizing Intervention Probabilities By Pooling pdf

Susan A. Murphy, Predrag V. Klasnja, Sabina Tomkins, 2018, ArXiv.

Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials

Susan A. Murphy, Predrag Klasnja, Andy Jinseok Lee, 2018, Statistics in biosciences.

Linear fitted-Q iteration with multiple reward functions

Susan A. Murphy, Michael H. Bowling, Daniel J. Lizotte, 2013, J. Mach. Learn. Res..

Artificial intelligence decision-making in mobile health

Susan A. Murphy, Predrag Klasnja, Mashfiqui Rabbi, 2019 .

Piloting and sizing sequential, multiple assignment, randomized trials in dynamic treatment regime development

Susan A. Murphy, 2012 .

Introduction to Part I: mHealth Applications and Tools

James M. Rehg, Susan A. Murphy, Santosh Kumar, 2017, Mobile Health - Sensors, Analytic Methods, and Applications.

Chapter 11: Imputing missing data from sequential multiple assignment randomized trials

Joelle Pineau, Susan A. Murphy, Eric B. Laber, 2015 .

A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Reward pdf

Susan A. Murphy, Richard S. Sutton, Hamid Reza Maei, 2016, ArXiv.

Just-in-Time but Not Too Much: Determining Treatment Timing in Mobile Health

Susan A. Murphy, Predrag V. Klasnja, Mustafa al'Absi, 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

Linear mixed models under endogeneity: modeling sequential treatment effects with application to a mobile health study

Susan A. Murphy, Predrag Klasnja, Tianchen Qian, 2019, 1902.10861.

Experimental design, data analysis,methods for mobile interventions

Susan A. Murphy, S. Murphy, 2015 .

The purpose of the Institute is to foster the development and dissemination of the theory and applications of statistics and probability.

Susan A. Murphy, Bradley Efron, Thomas G. Kurtz, 2016 .

Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

Ambuj Tewari, Susan A. Murphy, Inbal Nahum-Shani, 2017, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study.

Susan A. Murphy, Predrag Klasnja, Tianchen Qian, 2019, Statistical science : a review journal of the Institute of Mathematical Statistics.

An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions pdf

Ambuj Tewari, Susan A. Murphy, Huitian Lei, 2017, ArXiv.

Model-Free Monte Carlo-like Policy Evaluation

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2010, AISTATS.

SMART designs to improve health

Susan A. Murphy, 2013 .

6. Discrete-Time Multilevel Hazard Analysis

Susan A. Murphy, William G. Axinn, Jennifer S. Barber, 2000 .

Informing sequential clinical decision-making through reinforcement learning: an empirical study

Joelle Pineau, Susan A. Murphy, Daniel J. Lizotte, 2010, Machine Learning.

Exploring innovative methods to evaluate the efficacy and safety of mobile health

Vasant Honavar, Susan A. Murphy, Vladimir Shusterman, 2013 .

Design Lessons from a Micro-Randomized Pilot Study in Mobile Health

Susan A. Murphy, Predrag V. Klasnja, Andy Jinseok Lee, 2017, Mobile Health - Sensors, Analytic Methods, and Applications.

Estimating Time-Varying Causal Excursion Effect in Mobile Health with Binary Outcomes

Susan A. Murphy, Daniel Almirall, Predrag Klasnja, 2019 .

A smartphone-based behavioural activation application using recommender system

Susan A. Murphy, Niranjan Bidargaddi, Tao Zhang, 2019, UbiComp/ISWC Adjunct.

98 Feasibility and acceptability of using a mobile phone app to assess substance use among adolescents and emerging adults

Ambuj Tewari, Susan A. Murphy, Inbal Nahum-Shani, 2017, Injury Prevention.

From Markers to Interventions: The Case of Just-in-Time Stress Intervention

Emre Ertin, Susan A. Murphy, Inbal Nahum-Shani, 2017, Mobile Health - Sensors, Analytic Methods, and Applications.

A Generalization Error for Q-Learning

Susan A. Murphy, S. Murphy, 2005, J. Mach. Learn. Res..

SARA: a mobile app to engage users in health data collection

Susan A. Murphy, Predrag V. Klasnja, Inbal Nahum-Shani, 2017, UbiComp/ISWC Adjunct.

Active learning for personalizing treatment

Joelle Pineau, Susan A. Murphy, Kun Deng, 2011, 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).

Stratégies d'échantillonnage pour l'apprentissage par renforcement batch

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2013, Rev. d'Intelligence Artif..

A Review of Some Inferential Challenges in Constructing Dynamic Treatment Regimes

Susan A. Murphy, Min Qian, S. Murphy, 2007 .

Apprentissage actif par modification de la politique de décision courante

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2011 .

The Penn State Methodology Center: Bringing the power of innovative methods to bear on drug abuse

Runze Li, Susan A. Murphy, Linda M. Collins, 2009 .

Just-in-Time but Not Too Much

Susan A. Murphy, Predrag V. Klasnja, Mustafa al'Absi, 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

iSurvive: An Interpretable, Event-time Prediction Model for mHealth

James M. Rehg, Susan A. Murphy, Michael L. Dennis, 2017, ICML.

Breaking Conceptual Barriers to Information Architecture: An Online Learning Experience

Susan A. Murphy, Catherine Boden, 2011 .

Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K)

Deborah Estrin, James M. Rehg, Gregory D. Abowd, 2015, IEEE Pervasive Computing.

From Ads to Interventions: Contextual Bandits in Mobile Health

Ambuj Tewari, Susan A. Murphy, Ambuj Tewari, 2017, Mobile Health - Sensors, Analytic Methods, and Applications.

SAS PROC QLEARN Users’ Guide (Version 1.0)

Susan A. Murphy, Daniel Almirall, John J Dziak, 2012 .

Towards Min Max Generalization in Reinforcement Learning

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2010, ICAART.

Just-in-time adaptive interventions

Susan A. Murphy, S. Murphy, 2014 .

Active exploration by searching for experiments that falsify the computed control policy

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2011, 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).

Machine learning methods for individualizing real-time treatment policies

Susan A. Murphy, 2013 .

A batch, off-policy actor-critic algorithm for optimizing mobile interventions

Susan A. Murphy, 2015 .

ewrapper: operationalizing engagement strategies in mHealth

Gregory D. Abowd, Susan A. Murphy, Inbal Nahum-Shani, 2017, UbiComp/ISWC Adjunct.

Introduction to Part II: Sensors to mHealth Markers

James M. Rehg, Susan A. Murphy, Santosh Kumar, 2017, Mobile Health - Sensors, Analytic Methods, and Applications.

Evaluation of Treatment Policies Assisted by Treatment Eects

Susan A. Murphy, Xi Lu, S. Murphy, 2011 .

E-cient A-Learning for Dynamic Treatment Regimes: A Handout

Susan A. Murphy, Lacey Gunter, Daniel Almirall, 2005 .

Determining the risk set for hazard functions when survival methods are applied to social science and prevention data

Susan A. Murphy, L. A. Malacane, 1997 .

Online Supplemental Materials to Dynamic Treatment Regimes

Susan A. Murphy, Bibhas Chakraborty, 2014 .

Intelligent Pooling in Thompson Sampling for Rapid Personalization in Mobile Health

Susan A. Murphy, Predrag Klasnja, Peng Liao, 2019 .

Variable selection for tailoring treatment

Susan A. Murphy, 2008 .

Small Sample Inference for Generalization Error in Classification Using the CUD Bound

Susan A. Murphy, Eric B. Laber, S. Murphy, 2008, UAI.

A comparison of randomized trials and the multi-phase optimization strategy for behavioral interventions

Susan A. Murphy, Linda M. Collins, Victor J. Strecher, 2005 .

Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research

Susan A. Murphy, Inbal Nahum-Shani, Daniel Almirall, 2014, Translational behavioral medicine.

Mobile Health: Sensors, Analytic Methods, and Applications

James M. Rehg, Susan A. Murphy, Santosh Kumar, 2017 .

Learning Treatment Policies in Mobile Health

Susan A. Murphy, Inbal Nahum-Shani, Andrew Raij, 2016 .

Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis

Susan A. Murphy, Michael H. Bowling, Daniel J. Lizotte, 2010, ICML.

Introduction to Part III: Markers to mHealth Predictors

James M. Rehg, Susan A. Murphy, Santosh Kumar, 2017, Mobile Health - Sensors, Analytic Methods, and Applications.

4. Adjusting for Time-Varying Confounding in Survival Analysis

Susan A. Murphy, Jennifer S. Barber, Natalya Verbitsky, 2004 .

Batch mode reinforcement learning based on the synthesis of artificial trajectories

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2013, Ann. Oper. Res..

A-Learning for approximate planning

Susan A. Murphy, Ji Zhu, D. Blatt, 2004 .

Active Learning for Developing Personalized Treatment pdf

Joelle Pineau, Susan A. Murphy, Kun Deng, 2011, UAI.

A Cautious Approach to Generalization in Reinforcement Learning

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2010, ICAART.

Mobile Health

James M. Rehg, Susan A. Murphy, Santosh Kumar, 2017, Springer International Publishing.

Statistical Inference with M-Estimators on Adaptively Collected Data

Susan A. Murphy, Lucas Janson, Kelly W. Zhang, 2021, NeurIPS.

Statistical Inference with M-Estimators on Bandit Data pdf

Susan A. Murphy, Lucas Janson, Kelly W. Zhang, 2021, ArXiv.

Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning pdf

Susan A. Murphy, Finale Doshi-Velez, Sarah Rathnam, 2021, ArXiv.

Projected partial likelihood and its application to longitudinal data

Susan A. Murphy, Bing Li, Bing Li, 1995 .

A Conceptual Framework for Adaptive Preventive Interventions

Susan A. Murphy, Linda M. Collins, S. Murphy, 2004, Prevention Science.

MLE in the proportional odds model

Susan A. Murphy, A. J. Rossini, A. W. van der Vaart, 1996 .

Adaptive treatment strategies

Susan A. Murphy, 2008 .

An Individualized, Data-Driven Digital Approach for Precision Behavior Change

Suchi Saria, Susan A. Murphy, Scott L. Zeger, 2020, American journal of lifestyle medicine.

Testing for a Time Dependent Coefficient in Cox's Regression Model

Susan A. Murphy, S. Murphy, 1993 .

Asymptotic Theory for the Frailty Model

Susan A. Murphy, S. Murphy, 1995 .

The Theil-Sen Estimator with Doubly Censored Data and Applications to Astronomy

Susan A. Murphy, Michael G. Akritas, Michael P. LaValley, 1995 .

Dynamic Treatment Regimes using Reinforcement Learning: A Cautious Generalization Approach

Louis Wehenkel, Susan A. Murphy, Damien Ernst, 2009 .

Observed information in semi-parametric models

Susan A. Murphy, Aad van der Vaart, A. V. D. Vaart, 1999 .

Semiparametric likelihood ratio inference

Susan A. Murphy, A. W. van der Vaart, A. V. D. Vaart, 1997 .

Maximum Likelihood Estimation in the Proportional Odds Model

Susan A. Murphy, A. J. Rossini, A. W. van der Vaart, 1997 .

Adaptive Treatment Strategies: an Emerging Approach for Improving Treatment Effectiveness

Susan A. Murphy, S. Murphy, 2003 .

Rejoinder of “Dynamic treatment regimes: Technical challenges and applications”

Susan A. Murphy, Daniel J. Lizotte, Eric B. Laber, 2014, Electronic Journal of Statistics.

Consistency in a Proportional Hazards Model Incorporating a Random Effect

Susan A. Murphy, S. Murphy, 1994 .

Rejoinder to discussion of ``On Profile Likelihood''.

Susan A. Murphy, A. W. van der Vaart, 2000 .

Semiparametric Mixtures in Case-Control Studies

Susan A. Murphy, A. W. van der Vaart, A. V. D. Vaart, 2001 .