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DTSTART;TZID=America/Los_Angeles:20260520T110000
DTEND;TZID=America/Los_Angeles:20260520T120000
DTSTAMP:20260928T035840
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SUMMARY:TILOS-HDSI Seminar: Inference-Time Algorithms: A Theoretical Lens on Tractability and Error Propagation
DESCRIPTION:Andrej Risteski\, Carnegie Mellon University \nAbstract: Modern AI systems are increasingly built by placing trained models inside larger computational loops. Inference-time algorithms are a basic instance of this idea: they use one or more trained models at test time to incorporate new information\, exploit pretrained models as priors\, and trade computational effort for accuracy\, sample quality\, or control. Examples include generator-verifier search for reasoning\, diffusion models for solving inverse problems\, and reward-guided generation. Theoretically\, this revisits a classical question from optimization and theoretical computer science: what can be done with access to an oracle? Here\, however\, the oracles are new and non-standard: they model the capabilities of large pretrained models\, making them powerful\, but also imperfect because they are learned. This combination leads to new questions about algorithm design and error propagation. \nThis talk studies two central aspects of this paradigm: computational efficiency and error propagation. The first vignette considers generator-verifier systems\, and shows how stochastic backtracking can trade additional computation for accuracy\, giving a principled version of test-time scaling even with imperfect learned oracles. The second vignette studies diffusion steering: when can we efficiently bias a pretrained diffusion model toward higher-reward samples while staying close to the original model? We show that tractability depends strongly on both the reward structure and the alignment objective\, and that simple primitives—such as sampling from linear tilts—can be surprisingly useful for handling richer reward classes. \nBased on https://arxiv.org/abs/2510.03149\, https://arxiv.org/abs/2602.16570\, https://arxiv.org/abs/2605.11361. \n\nAndrej Risteski is an Associate Professor at the Machine Learning Department in Carnegie Mellon University. Prior to that\, he was a Norbert Wiener Research Fellow jointly in the Applied Math department and IDSS at MIT. Dr. Risteski received his PhD in the Computer Science Department at Princeton University under the advisement of Sanjeev Arora. \nDr. Risteski’s research interests lie in the intersection of machine learning\, statistics\, and theoretical computer science\, spanning topics like (probabilistic) generative models\, algorithmic tools for learning and inference\, representation and self-supervised learning\, out-of-distribution generalization and applications of neural approaches to natural language processing and scientific domains. The broad goal of his research is principled and mathematical understanding of statistical and algorithmic problems arising in modern machine learning paradigms.
URL:https://tilos.ai/event/tilos-hdsi-seminar-inference-time-algorithms-a-theoretical-lens-on-tractability-and-error-propagation/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
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DTSTART;TZID=America/Los_Angeles:20260930T110000
DTEND;TZID=America/Los_Angeles:20260930T120000
DTSTAMP:20260928T035840
CREATED:20260923T171457Z
LAST-MODIFIED:20260923T172607Z
UID:8660-1790766000-1790769600@tilos.ai
SUMMARY:TILOS-SDSU Seminar: Distributionally Robust Optimization and Applications to Air Traffic Management
DESCRIPTION:Max Z. Li\, University of Michigan \nAbstract: Ground Delay Programs (GDPs) mitigate air traffic demand-capacity imbalances by holding flights on the ground at their origin airports when the destination airport’s arrival capacity is reduced\, thereby limiting costly airborne holding. A central challenge is that day-to-day demand-capacity balancing depends on accurate capacity predictions. In practice\, however\, these predictions are highly uncertain: forecast errors\, operational disruptions\, and climate-driven changes in weather severity can induce distributional shifts in capacity outcomes. As a result\, policies optimized for a single predicted distribution may perform poorly out of sample. We address this challenge by developing a distributionally robust framework for the single-airport ground holding problem (dr-SAGHP). We further propose a solution approach that integrates Kelly’s cutting plane method with the integer L-shaped method\, applicable more broadly to two-stage distributionally robust integer programs with relatively complete recourse and continuous second-stage decision variables. The approach includes a novel dual bisection and primal recovery procedure that exploits problem structure to efficiently generate the subgradients required by Kelly’s method. In computational experiments\, the proposed algorithm achieves up to two orders-of-magnitude speedups relative to directly solving the convex reformulation\, while maintaining negligible optimality gaps. Numerical results demonstrate that dr-SAGHP yields substantial out-of-sample improvements under moderate to severe distributional shifts\, enhancing the robustness and effectiveness of GDP decision-making under capacity uncertainty. \n\nMax is an Assistant Professor of Aerospace Engineering at the University of Michigan\, Ann Arbor. He also has courtesy appointments in Civil and Environmental Engineering as well as Industrial and Operations Engineering. Max received his PhD in Aerospace Engineering from the Massachusetts Institute of Technology in 2021. He received his MSE in Systems Engineering and BSE in Electrical Engineering and Mathematics\, both from the University of Pennsylvania\, in 2018. Max’s research and teaching interests include air transportation systems\, airport and airline operations\, Advanced Air Mobility\, networked systems\, as well as optimization and control. Max is the recipient of the NSF CAREER Award\, INFORMS Aviation Applications Section Best PhD Dissertation Prize\, the FAA RAISE Award\, as well as several best paper awards from national- and international-level conferences. \nZoom: https://SDSU.zoom.us/j/87332078173
URL:https://tilos.ai/event/tilos-sdsu-seminar-distributionally-robust-optimization-and-applications-to-air-traffic-management/
LOCATION:Lamden Hall (LH) 247\, San Diego State University\, San Diego\, CA\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
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