1 citations · 1 across the 2 of their papers we have counts for
4 papers
A Stochastic GDA Method With Backtracking For Solving Nonconvex Concave Minimax Problems
Necdet Serhat Aybat, Qiushui Xu, Xuan Zhang +1
We propose a stochastic GDA (gradient descent ascent) method with backtracking (SGDA-B) to solve nonconvex-concave (NCC) minimax problems of the form: $\min_{\mathbf{x}} \max_y \su…
An Accelerated Primal Dual Algorithm with Backtracking for Decentralized Constrained Optimization
Qiushui Xu, Necdet Serhat Aybat, Mert Gürbüzbalaban
We propose a distributed accelerated primal-dual method with backtracking (D-APDB) for cooperative multi-agent constrained consensus optimization problems over an undirected networ…
Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping
Umut ÅimÅekli, Mert Gürbüzbalaban, Sinan Yıldırım +1
The injection of heavy-tailed noise into the iterates of stochastic gradient descent (SGD) has garnered growing interest in recent years due to its theoretical and empirical benefi…
High-probability complexity guarantees for nonconvex minimax problems
Yassine Laguel, Yasa Syed, Necdet Serhat Aybat +1
Stochastic smooth nonconvex minimax problems are prevalent in machine learning, e.g., GAN training, fair classification, and distributionally robust learning. Stochastic gradient d…