Statistical Learning
Sparse Deep Learning for Time Series Data: Theory and Applications
Specifically, we lay the foundation within the Bayesian framework.
Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization Jun Chen
We establish the first generalization error bound of FedZO under the Lipschitz continuity and smoothness conditions.
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
Finding an approximate second-order stationary point (SOSP) is a well-studied and fundamental problem in stochastic nonconvex optimization with many applications in machine learning.
Decomposing Novel into Known: Part Concept Learning For 3D Novel Class Discovery
The key challenge of 3D NCD is that learned features by known class recognition are heavily biased and hinder generalization to novel classes.
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit
Finally, we show, through simulations, that the SDE provides a surprisingly good description of the corresponding finite-size model. We coin the name shaped Transformer for these architectural modifications.
Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization
Most existing algorithms rely on one-sided information, such as the convexity (resp.
