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Jan20
EVENT DETAILSmore info
lessTitle: Applications of Power Constrained Nonstationary Multi-Armed Bandits in Personalized Healthcare.
Abstract: 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’s effectiveness (habituation), while inactivity may restore it (recovery). These nonstationarities are captured by the Reducing or Gaining Unknown Efficacy (ROGUE) bandit framework, which models real-world settings such as behavioral health interventions. While existing algorithms can compute sublinear regret policies to optimize these settings, they may not provide sufficient exploration due to overemphasis on exploitation, limiting the ability to estimate population-level effects. This is a challenge of particular interest in micro-randomized trials (MRTs) that aid researchers in developing just-in-time adaptive interventions that have population-level effects while still providing personalized recommendations to individuals. We first develop ROGUE-TS, a Thompson Sampling algorithm tailored to the ROGUE framework, and provide theoretical guarantees of sublinear regret. We then introduce a probability clipping procedure to balance personalization and population-level learning, with quantified trade-off that balances regret and minimum exploration probability. Validation on an MRT dataset concerning physical activity promotion shows that our methods both achieve lower regret than existing approaches and maintain high statistical power through the clipping procedure without significantly increasing regret. This enables reliable detection of treatment effects while accounting for individual behavioral dynamics. For researchers designing MRTs, our framework offers practical guidance on balancing personalization with statistical validity. We demonstrate how prior data, such as from pilot studies, can be leveraged to inform adaptive intervention delivery. In cases where interventions differ in burden or risk, tailored strategies can be used to prioritize either participant safety or learning objectives. Importantly, we show that statistical power can still be achieved with reduced exploration, enabling the design of more efficient and participant-friendly trials.
Bio: Yonatan Mintz is an assistant professor in the Industrial and Systems Engineering department at the University of Wisconsin, Madison. His research focuses on the application of machine learning and automated decision making to human sensitive contexts. One application of his research has been on using patient level data, to create precision interventions . Yonatan is also interested in the sociotechnical implications of machine learning algorithms and has done work on fairness, accountability, and transparency in automated decision making. In terms of methodology his research explores topics in machine learning theory, stochastic control, reinforcement learning, and nonconvex optimization. Yonatan's work has been recognized as a finalist in the INFORMS Health Applications Society Pierskalla Paper competition, a best poster award from the NeurIPS joint workshop on AI for Social Good, and he has been actively invited to publicly speak about his work in both print and televised media including PBS. His research has been funded by multiple awards from the National Institutes of Health (NIH) and American Family Insurance. Prior to joining UW--Madison, Yonatan was a postdoctoral research fellow at the department of Industrial and Systems Engineering at the Georgia Institute of Technology. Yonatan received his B.S. in Industrial and Systems Engineering with a concentration in Operations Research from Georgia Tech in 2012, and his Ph.D. in Industrial Engineering and Operations Research from the University of California, Berkeley in 2018.
TIME Tuesday, January 20, 2026 at 11:00 AM - 12:00 PM
LOCATION A230, Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Jan27
TIME Tuesday, January 27, 2026 at 11:00 AM - 12:00 PM
LOCATION TBD, Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Feb3
TIME Tuesday, February 3, 2026 at 11:00 AM - 12:00 PM
LOCATION TBD, Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Feb10
TIME Tuesday, February 10, 2026 at 11:00 AM - 12:00 PM
LOCATION TBD, Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Feb17
EVENT DETAILS
lessNemhauser Best Paper Award: Chutong Gao, Redundancy or Flexibility? Interplay between their Values in Designing Fork-Join Systems
Coauthors: Seyed Iravani, Ohad Perry
Citation: We consider fork-join systems with redundancy and flexibility. Fork-join systems empower numerous modern online service platforms, such as AI agent workflows, among other real-world applications. In a 𝑘-requirement fork-join (FJ𝑘, 𝑘 ≥ 2) system, each job “forks” into multiple tasks processed in parallel, among which 𝑘 tasks are required to “join” for the completion of the job. To minimize the mean job sojourn times in FJ𝑘 systems, two strategies—redundancy and flexibility—are often employed. Redundancy deploys 𝑛 (> 𝑘) stations and sends one task from a job to each, but only 𝑘 out of 𝑛 tasks are needed to complete the job (rendering the remaining 𝑛 − 𝑘 tasks redundant). Flexibility, on the other hand, enables dynamic service capacity allocation. To understand the impact on the mean sojourn time of each strategy and their interplay, we propose a unified modeling framework that simultaneously considers: (i) how many redundant stations should be deployed, and (ii) how the service capacities should be allocated among all stations. We primarily focus on exponentially distributed task sizes. When flexibility is allowed, we prove a class of longest-queue policies are global-optimal, regardless of having redundancy or lack thereof—so redundancy has no marginal value in the existence of flexibility. In the absence of flexibility, we prove in FJ2 systems that “a little redundancy goes a long way”, in a twofold sense: To achieve heavy traffic optimality relative to the global-optimal system, it suffices to (i) employ only one extra station, and (ii) allocate an arbitrarily small capacity 𝜖 > 0 to that extra station. This suggests that (even a little) redundancy is as attractive as (full) flexibility in reducing sojourn times. Numerical studies further support the above insights in FJ𝑘 systems with 𝑘 > 2. We further consider a family of Weibull task-size distributions, where exponential becomes a special case. We show that when the variability of task sizes is low, redundancy can reduce the stability region (and thus harm both throughput and the sojourn time), so it should not be implemented. For highly variable task sizes, redundancy alone can expand the stability region, and flexibility on top of that further expands the stability region. But the flexible control policies need to be selected with extra caution—otherwise, implementing flexibility can harm the system’s stability.
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Nelson Best Paper Award: Tong Xu, Integer programming for learning directed acyclic graphs from nonidentifiable Gaussian models
Coauthors: Armeen Taeb, Simge Küçükyavuz, Ali Shojaie
Citation: We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: (i) they cannot provide optimality guarantees and can suffer from learning suboptimal models; (ii) they rely on the stringent assumption that the noise is homoscedastic, and hence the underlying model is fully identifiable. We overcome these shortcomings and develop a computationally efficient mixed-integer programming framework for learning medium-sized problems that accounts for arbitrary heteroscedastic noise. We present an early stopping criterion under which we can terminate the branch-and-bound procedure to achieve an asymptotically optimal solution and establish the consistency of this approximate solution. In addition, we show via numerical experiments that our method outperforms state-of-the-art algorithms and is robust to noise heteroscedasticity, whereas the performance of some competing methods deteriorates under strong violations of the identifiability assumption. The software implementation of our method is available as the Python package micodag.
TIME Tuesday, February 17, 2026 at 11:00 AM - 12:00 PM
LOCATION Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Mar3
TIME Tuesday, March 3, 2026 at 11:00 AM - 12:00 PM
LOCATION TBD, Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Mar10
TIME Tuesday, March 10, 2026 at 11:00 AM - 12:00 PM
LOCATION TBD, Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)