IFDS

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

MLOpt:
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
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, 2023-05-03 14:00, 2023-05-10 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, 2023-05-08 12:30 - 13:30 (America/Chicago)
Current research

IFDS Ideas Forum
2023-05-01 12:30 - 13:30 (America/Chicago)

IFDS Annual Meeting
2023-08-01 - 2023-08-02 | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Program Booklet
August 1

StartEndSpeakerTitle/Activity

9:309:45Welcome and Introduction

Faculty 30 Minute Talks

9:4510:15Ilias DiakonikolasProvably learning neural networks

10:1510:45Qin LiNN-training and ODE-constrained optimization

10:4511:00Break

11:0011:30Kevin JamiesonTowards Instance-Optimal Algorithms for Reinforcement Learning

11:3012:00Sebastien RochInverting random trees

12:001:45Lunch

1:452:15Ramya VinayakLearning from Diverse Data in Metric and Preference Learning

2:152:45Rina Foygel-Barber

2:453:00Break

3:003:30Ludwig SchmidtDataComp: In search of the next generation of multimodal datasets

3:304:00Break

4:006:00Poster Session

August 2

StartEndSpeakerTitle/Activity

Faculty 30 Minute Talks

9:009:30Rebecca WillettMathematical foundations of machine learning

9:3010:00Abel RodriguezSparsity and model selection:a unified perspective

10:0010:15 Break

10:1510:45Lise GetoorFoundations for Neuro-Symbolic Systems

10:4511:15Dmitriy DrusvyatskiyAiming towards minimizers: fast convergence of SGD for overparameterized problems

11:1511:30 Break

11:30 1:30Lunch

Postdoc 15 Minute Talks

1:301:45 Ahmet AlacaogluImproving the complexity of primal-dual coordinate algorithms

1:452:00Jeongyeol Kwon RL in latent environments via moment-exploration

2:002:15 Break

2:152:30Lijun DingFlat minima generalize for low-rank matrix recovery

2:302:45Jake SoloffBagging provides assumption-free stability

2:453:00Break

3:003:15David ClancyReconstructing mutation rates on binary trees

3:153:30Jasper LeeBeyond Worst-Case Optimality in Mean Estimation

3:303:45Break

3:454:00Steve MussmannExploratory set cover for data subset selection

4:004:15Ruhui JinOptimal experimental design via gradient flow

4:154:30Break

4:305:30Steve, Becca, Lise, MaryamPanel Discussion

SILO: Scalable Gaussian-Process Inference via Sparse Inverse Cholesky Factorization
2023-09-06 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Matthias Katzfuss

IFDS Ideas Forum
2023-09-11 12:30 (America/Chicago)
The Deceptive Green Flamingos: a thought experiment on the complexities of AI alignment
Speakers: Dimitris Papailiopoulos / Nayoung Lee

SILO: A Statistical Framework for Private Personalized Federated Learning and Estimation
2023-09-13 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Suhas Diggavi, UCLA

IFDS Ideas Forum
2023-09-18 12:30 (America/Chicago)
Peer diffusions over uncertain networks
Speaker: Alex Hayes
