Session MD-5: Relaxed Smoothness and Convexity Assumptions in Optimization for Machine Learning in stream Optimization for machine learning
Monday, 16:30-18:30Room: B100/4013
| Session chair(s): |
|
| 104. Loss Landscape Characterization of Neural Networks without Over-Parametrization |
Rustem Islamov
[R] - Switzerland | accepted | ||
| 81. Methods for Convex (L0,L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity |
Eduard Gorbunov
[R] - United Arab Emirates | accepted | ||
| 399. Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods |
Anton Rodomanov
[R] - Germany | accepted | ||
| Daniil Vankov
[] - United States | ||||
| Angelia Nedich
[] - United States | ||||
| Lalitha Sankar
[] - United States | ||||
| Sebastian Stich
[R] - Germany | ||||
| 368. A Third-Order Perspective on Newton’s Method and its Application in Federated Learning |
Slavomír Hanzely
[R] - United Arab Emirates | accepted | ||