IFDS

SILO: Sameer Deshpande
2022-11-16 12:30 (America/Chicago)
Sameer Deshpande

MLOpt:
2022-11-18 13:30, 2022-11-25 13:30, 2022-12-09 13:30, 2022-12-16 13:30 (America/Los_Angeles) | University of Washington, Seattle, 185 E Stevens Way NE, Seattle, WA, 98195-2350, United States

IFDS Ideas Forum: Yinglun Zhu
2022-11-21 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO: Yongyi Guo
2022-11-23 12:30 (America/Chicago)
Yongyi Guo

IFDS Ideas Forum: Hao Yan
2022-11-28 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO: Kirthevasan Kandasamy
2022-11-30 12:30 (America/Chicago)
Kirthevasan Kandasamy

IFDS All-Hands
2022-12-02 13:30 (America/Los_Angeles) | University of Washington, Seattle, 185 E Stevens Way NE, Seattle, WA, 98195-2350, United States

IFDS Ideas Forum: Jenny Wei, Ying Fan
2022-12-05 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States

SILO: Yiqiao Zhong
2022-12-07 12:30 (America/Chicago)
Yiqiao Zhong

IFDS Ideas Forum
2023-02-06 12:30 - 13:30 (America/Chicago)
Announcements, Several topics of current research

IFDS Ideas Forum
2023-02-13 12:30 - 13:30, 2023-02-20 12:30 - 13:30 (America/Chicago)
ICML Bazaar

IFDS Ideas Forum
2023-02-27 12:30 - 13:30, 2023-03-06 12:30 - 13:30 (America/Chicago)
(WID 4130)

IFDS Ideas Forum
2023-03-08 12:30 - 13:30 (America/Chicago)
JOINT WITH SILO: Orchard View 12:30-1:30

IFDS Ideas Forum
2023-03-13 12:30 - 13:30 (America/Chicago)
SPRING BREAK – NO MEETING

IFDS Ideas Forum: Misha Khodak
2023-03-20 12:30 - 13:30 (America/Chicago) | CS 1240
New Directions in Algorithms with Predictions: Learning and Privacy

Abstract: A burgeoning paradigm in algorithm design is learning-augmented algorithms, or algorithms with predictions, where methods can take advantage of a (possibly imperfect) prediction about their instance. While past work has focused on using predictions to improve competitive ratios and runtime, this talk addresses a different, salient question: how do we learn the predictions themselves? We introduce an approach for co-designing learning-augmented algorithms with their own custom learning algorithms, with the crucial step being to optimize nice surrogate losses bounding the algorithms’ costs. This leads to improved sample complexity bounds for several learning-augmented graph algorithms and the first learning-theoretic guarantees for page migration with predictions, among other contributions. We also instantiate these ideas on the new direction of learning-augmented private algorithms, where the goal is to reduce utility loss due to privacy rather than runtime. Our approach drives numerous insights on how to robustly incorporate external information to release better statistics of sensitive datasets, which we verify empirically on the task of multiple quantile release.Bio: Misha Khodak is a PhD student in computer science at Carnegie Mellon University advised by Nina Balcan and Ameet Talwalkar. He studies foundations and applications of machine learning, especially meta-learning and algorithm design. Misha is a recipient of the Facebook PhD Fellowship and has interned at Google Research – New York, Microsoft Research – New England, the Lawrence Livermore National Lab, and the Princeton Plasma Physics Lab.

SILO Seminar Series
2023-03-22 14:00, 2023-03-29 14:00, 2023-04-05 14:00, 2023-04-12 14:00, 2023-04-19 14:00, 2023-04-26 14:00 (America/Chicago)
This semester we will be holding hybrid seminars, with both in-person and virtual (Zoom) participation. Pizza will be served for the in-person attendees.

IFDS Ideas Forum
2023-04-03 12:30 - 13:30 (America/Chicago)
Several open problems

IFDS Ideas Forum
2023-04-10 12:30 - 13:30, 2023-04-17 12:30 - 13:30, 2023-04-24 12:30 - 13:30 (America/Chicago)
Current research
