(Tentative - Best viewed with laptops)
Day 1
Day 2
Breakfast: 8:00 a.m.– 8:45 a.m.
Breakfast 8:00 a.m.– 9:00 a.m.
Introduction: 8:45 a.m.– 9:00 a.m.
9:00 a.m.– 10:00 a.m.
Safe learning for resource allocation over networks: models and regret guarantees
9:00 a.m.– 10:00 a.m.
10:00 a.m.– 10:30 a.m.
Provable Accelerated Convergence of Nesterov’s Momentum for Deep ReLU Neural Networks
10:00 a.m.– 10:30 a.m.
On Graphs with Finite-Time Consensus
Coffee break until 11:00 a.m.
11:00 a.m.– 12:00 p.m.
Agent Learning by Generative Optimization with Trace
11:00 a.m.– 12:00 p.m.
On Reinforcement Learning and Gradient Descent
Lunch break until 2:00 p.m.
2:00 p.m.– 3:00 p.m.
Towards Geo-Distributed Large Language Model Training
2:00 p.m.– 3:00 p.m.
Test-Time Thinking for Trust: Enhancing Generative AI Agents
3:00 p.m.– 4:00 p.m.
Algorithms for Distributed and Collaborative Deep Learning
3:00 p.m.– 4:00 p.m.
Hardware-Efficient Learning Algorithms for Large Language Models
4:00 p.m.– 4:30 p.m.
4:00 p.m.– 4:30 p.m.
4:30 p.m.– 6:00 p.m.
4:30 p.m.–