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Learning from Heterogeneous Sources
Program
Federated and Collaborative Learning
Location
Calvin Lab auditorium
Date
Monday, Feb. 23
–
Friday, Feb. 27, 2026
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Workshop & Symposia
Schedule | Learning From Heterogeneous Sources
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The Workshop
Schedule
Videos
All talks listed in Pacific Time. Schedule subject to change.
Monday, Feb. 23, 2026
9
–
9:30 a.m.
Coffee and Check-In
9:30
–
10:15 a.m.
From the Ball-proximal (Broximal) Point Method to Efficient Training of LLM
Peter Richtarik (KAUST)
Video
10:15
–
11 a.m.
Scale Learning and Reasoning Across Heterogeneous Gradients and Semantics
Tian Li (University of Chicago)
Video
11 a.m.
–
1 p.m.
Lunch (on your own)
1
–
1:45 p.m.
"Federated Reinforcement Learning: Statistical and Communication Trade-offs
Yuejie Chi (Yale University)
Video
1:45
–
2:15 p.m.
Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration
Ahmed Khaled (Princeton University)
Video
2:15
–
2:45 p.m.
The Many Faces of Heterogeneity: Federated, Continual, and Modular Learning
Marco Ciccone (Vector Institute)
Video
2:45
–
3:15 p.m.
Break
3:15
–
4 p.m.
Symbiotic Relations between Decoupled Training, Optimization, and Federated Learning
Zachary Charles (Google Research)
Video
4
–
5 p.m.
Reception
Tuesday, Feb. 24, 2026
9
–
9:30 a.m.
Coffee and Check-In
9:30
–
10:15 a.m.
Privacy Amplification from Structured Algorithmic Randomness
Ayfer Ozgur (Stanford University)
Video
10:15
–
10:45 a.m.
Break
10:45
–
11:30 a.m.
Federated, Synthetic, Personalized: Heterogeneity Here or There?
Zheng Xu (Meta)
Video
11:30 a.m.
–
1:30 p.m.
Lunch (on your own)
1:30
–
2 p.m.
Privacy of Decentralized Machine Learning
Edwige Cyffers (CNRS)
Video
2
–
2:30 p.m.
Exact Unlearning of Finetuning Data via Model Merging at Scale
Kevin Kuo (Carnegie Mellon University)
Video
2:30
–
3 p.m.
Privacy versus Robustness in Federated Learning: Limits and Algorithms
Youssef Allouah (Stanford University)
Video
3
–
3:30 p.m.
Break
3:30
–
4:30 p.m.
Richard M. Karp Distinguished Lecture
Brendan McMahan (Google)
Wednesday, Feb. 25, 2026
9
–
9:30 a.m.
Coffee and Check-In
9:30
–
10:15 a.m.
Exploiting Similarity in Federated Learning
Sebastian Stich (CISPA)
Video
10:15
–
11 a.m.
Federated Learning in the Generative AI Era
Gauri Joshi (Carnegie Mellon University)
Video
11 a.m.
–
1 p.m.
Lunch (on your own)
1
–
1:45 p.m.
FlexOlmo: Open Language Models for Flexible Data Use
Sewon Min (UC Berkeley)
Video
1:45
–
2:30 p.m.
On continual learning with gradient descent for neural networks
Arya Mazumdar (UC San Diego)
Video
2:30
–
3 p.m.
Break
3
–
3:45 p.m.
Not all data are created Equal: Robust Mean Estimation from Heterogeneous and Unreliable Users
Maryam Aliakbarpour (Rice)
Video
3:45
–
5 p.m.
Group Hike
Thursday, Feb. 26, 2026
9
–
9:30 a.m.
Coffee and Check-In
9:30
–
10:15 a.m.
A Complex Picture of Multi-task Learning
Samory Kpotufe (Columbia University)
Video
10:15
–
10:45 a.m.
Break
10:45
–
11:30 a.m.
Learning from multiple modalities, Predicting on unseen tasks
Zaid Harchaoui (University of Washington)
Video
11:30 a.m.
–
1:30 p.m.
Lunch
1:30
–
2:15 p.m.
Personalized Collaborative Learning with Affinity-Based Variance Reduction
Chenyu Zhang (MIT)
Video
2:15
–
3 p.m.
The Statistical Fairness-Accuracy Frontier
Alireza Fallah (Rice University)
Video
3
–
3:30 p.m.
Break
3:30
–
5:30 p.m.
Jane Street Estimathon
Friday, Feb. 27, 2026
9
–
9:30 a.m.
Coffee and Check-In
9:30
–
10:15 a.m.
Are We Measuring the Right Thing? Distribution Shift Lessons for Federated Learning
Sanmi Koyejo (Stanford University)
Video
10:15
–
11 a.m.
Open Problems Session #1
11 a.m.
–
1 p.m.
Lunch (on your own)
1
–
1:45 p.m.
Tight analyses of first-order methods with error feedback, in homogeneous and heterogeneous setups
Aymeric Dieuleveut (CMAP)
Video
1:45
–
2:30 p.m.
Open Problems Session #2
2:30
–
2:45 p.m.
Break
2:45
–
3:30 p.m.
Group Discussion
3:30
–
3:45 p.m.
Concluding Remarks
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