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SUMMARY:CS Seminar Series: Machine Learning for Faster Optimization
DESCRIPTION:Refreshments are available starting at 10:30 a.m. The seminar will begin at 10:45 a.m. \nAbstract\nThis talk will discuss the area of algorithms with predictions\, also known as learning-augmented algorithms. These methods parameterize algorithms with machine-learned predictions\, enabling the algorithms to tailor their decisions to input distributions and to allow for improved performance on runtime\, space\, or solution quality. This talk will discuss recent developments on how to leverage machine-learned predictions to improve the runtime efficiency of algorithms for optimization and data structures. The talk will also discuss how to achieve “instance-optimal” algorithms when the predictions are accurate and the algorithm’s performance gracefully degrades when there are errors in the predicted advice. The talk will illustrate via examples such as bipartite matching the potential of the area to realize significant performance improvements for algorithm efficiency. \nSpeaker Biography\nBen Moseley is the Carnegie-Bosch Associate Professor of Operations Research at Carnegie Mellon University and is a consulting scientist at Relational AI. He obtained his PhD from the University of Illinois. During his career\, his papers have won best paper awards at IPDPS (2015)\, SPAA (2013)\, and SODA (2010). His papers have been recognized as top publications with honors such as Oral Presentations at NeurIPS (2021\, 2017) and NeurIPS Spotlight Papers (2023\, 2018). He has served as area chair for ICML\, ICLR\, and NeurIPS every year since 2020 and has been on many program committees\, including SODA (2022\, 2018)\, ESA (2017)\, and SPAA (2024\, 2022\, 2021\, 2016). He was an associate editor for IEEE Transactions on Knowledge and Data Engineering from 2018–2022 and has served as associate editor of Operations Research Letters since 2017. He has won an NSF CAREER Award\, two Google Research Faculty Awards\, a Yahoo ACE Award\, and an Infor faculty award. He was selected as a Top 50 Undergraduate Professor by Poets & Quants. His research interests broadly include algorithms\, machine learning\, and discrete optimization. He is currently excited about robustly incorporating machine learning into decision-making processes. \nZoom link >>
URL:https://www.cs.jhu.edu/event/cs-seminar-series-machine-learning-for-faster-optimization/
LOCATION:Hackerman B-17
CATEGORIES:Seminars and Lectures
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