12 citations · 25 across the 13 of their papers we have counts for
6 papers · 1 filter
Power Constrained Nonstationary Bandits with Habituation and Recovery Dynamics
Fengxu Li, Stephanie M. Carpenter, Matthew P. Buman +1
A common challenge for decision makers is selecting actions whose rewards are unknown and evolve over time based on prior policies. For instance, repeated use may reduce an action'…
Finite-Time Guarantees for Multi-Agent Combinatorial Bandits with Nonstationary Rewards
Katherine B. Adams, Justin J. Boutilier, Qinyang He +1
We study a sequential resource allocation problem where a decision maker selects subsets of agents at each period to maximize overall outcomes without prior knowledge of individual…
An Adaptive Optimization Approach to Personalized Financial Incentives in Mobile Behavioral Weight Loss Interventions
Qiaomei Li, Kara L. Gavin, Corrine I. Voils +1
Obesity is a critical healthcare issue affecting the United States. The least risky treatments available for obesity are behavioral interventions meant to promote diet and exercise…
The Game of Hidden Rules: A New Kind of Benchmark Challenge for Machine Learning
Eric Pulick, Shubham Bharti, Yiding Chen +4
As machine learning (ML) is more tightly woven into society, it is imperative that we better characterize ML's strengths and limitations if we are to employ it responsibly. Existin…
A Mixed-Integer Programming Approach to Training Dense Neural Networks
Vrishabh Patil, Yonatan Mintz
Artificial Neural Networks (ANNs) are prevalent machine learning models that are applied across various real-world classification tasks. However, training ANNs is time-consuming an…
Optimal Local Explainer Aggregation for Interpretable Prediction
Qiaomei Li, Rachel Cummings, Yonatan Mintz
A key challenge for decision makers when incorporating black box machine learned models into practice is being able to understand the predictions provided by these models. One prop…