All talks listed in Pacific Time. Schedule subject to change.

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Coffee and Check In
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Opening Remarks
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Learning from Dynamics

Ankur Moitra (Massachusetts Institute of Technology)
Video
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Testing Noise Assumptions of Learning Algorithms

Arsen Vasilyan (Simons Institute)
Video
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Break
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The Truth About Your Lying Calibrated Forecaster: How to Design Truthful Calibration Measures

Nika Haghtalab (UC Berkeley)
Video
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Parameter-free second-order methods for min-max optimization

Ali Kavis (UT Austin)
Video
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Lunch (on your own)
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Data curation for LLM Pre-training and Post-Training: Creating massive datasets for the AI open-source community with the Datacomp project.

Alex Dimakis (UT Austin)
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Bypassing the Impossibility of Online Learning Thresholds: Unbounded Losses and Transductive Priors

Nikita Zhivotovskiy (UC Berkeley)
Video
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Coffee and Check In
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Revisiting Scalarization in Multi-Task Learning

Han Zhao (University of Illinois Urbana-Champaign)
Video
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Understanding Contrastive Learning and Self-training

Sujay Sanghavi (UT Austin and Amazon)
Video
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Break
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Omnipredicting Single-Index Models with Multi-Index Models

Kevin Tian (UT Austin)
Video
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Talk by

Tselil Schramm (Stanford University)
Video
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Poster Session and Appetizers
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Beyond Decoding: Meta-Generation Algorithms for Large Language Models (Remote Talk)

Sean Welleck (Carnegie Mellon University)
Video
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RMK Lecture Reception (Light Refreshments)
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The Science of Modern Machine Learning: New Opportunities for Theory | Richard M. Karp Distinguished Lecture

Peter Bartlett (UC Berkeley)
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Coffee and Check In
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Robust Mixture Learning when Outliers Overwhelm Small Groups

Fanny Yang (ETH Zurich)
Video
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Learning General Gaussian Mixtures With Efficient Score Matching

Vasilis Kontonis (UT Austin)
Video
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Break
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Talk by

Mahdi Soltanolkotabi (University of Southern California)
Video
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Talk by

Zaid Harchaoui (University of Washington)