New Directions in Machine Learning Theory
Videos from BIRS Workshop
Jamie Morgenstern, University of Washington
Monday Oct 21, 2024 09:07 - 09:38
What governs predictive disparity in modern machine learning applications?
Tijana Zrnic, Stanford University
Monday Oct 21, 2024 09:39 - 10:11
Prediction-Powered Inference
Christian Ikeokwu, UC Berkeley
Monday Oct 21, 2024 10:31 - 11:11
Bursting the Filter Bubble: Disincentivizing Echo Chambers in Social Networks
Luana Ruiz, JHU
Monday Oct 21, 2024 13:03 - 13:33
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
Ellen Vitercik, Stanford University
Monday Oct 21, 2024 13:39 - 14:06
Online matching with graph neural networks
Arwen Bradley, Apple
Monday Oct 21, 2024 14:31 - 14:58
Step-by-Step Diffusion
Simina Brânzei, Purdue
Tuesday Oct 22, 2024 09:04 - 09:30
Dueling over dessert, mastering the art of repeated cake cutting
Eric Mazumdar, California Insitute of Technology
Tuesday Oct 22, 2024 10:49 - 11:12
Behavioral Economics-Inspired Multi-Agent Learning
Nika Haghtalab, University of California, Berkeley
Tuesday Oct 22, 2024 13:03 - 13:36
Theory of Multi-objective Machine Learning
Vatsal Sharan, USC
Tuesday Oct 22, 2024 13:36 - 14:03
A multigroup perspective to go beyond loss minimization in ML
Preetum Nakkiran, Apple
Tuesday Oct 22, 2024 14:32 - 15:06
Mechanisms of LLM Generalization: A Computational Approach
Mikhail Khodak, Princeton University
Wednesday Oct 23, 2024 09:04 - 09:36
Efficiently learning instance-optimal linear system solvers
Etai Littwin, Apple
Wednesday Oct 23, 2024 09:36 - 10:04
Should we predict in Latent Space in Self-Supervised Learning?
Manolis Zampetakis, Yale University
Wednesday Oct 23, 2024 10:46 - 11:15
Towards Theoretical Understanding of Extrapolation in Data Science
Sanjoy Dasgupta, UCSD
Thursday Oct 24, 2024 09:03 - 09:32
Recent progress on interpretable clustering
Eric Balkanski, Columbia
Thursday Oct 24, 2024 09:33 - 10:04
Fair Secretaries with Unfair Predictions
Bailey Flanigan, Harvard
Thursday Oct 24, 2024 11:03 - 11:34
Algorithmic tools for targeting sortition ideals
Kasper Green Larsen, Aarhus University
Thursday Oct 24, 2024 13:03 - 13:31
Majority-of-Three: The Simplest Optimal Learner?
Tosca Lechner, Vector Institute
Thursday Oct 24, 2024 13:34 - 14:03
Inherent Limitations for Characterizing Distribution Learning
Will Ma, Columbia University
Thursday Oct 24, 2024 14:06 - 14:43
VC Theory vs. Empirical DP
Sebastian Bordt, University of Tübingen
Friday Oct 25, 2024 09:04 - 09:34
Who is allowed to train on the test set?
Vasilis Kontonis, UT Austin
Friday Oct 25, 2024 09:39 - 10:10
Beyond Worst-Case Models for Learning from Noisy Labels
