14 citations · 51 across the 12 of their papers we have counts for
14 papers
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…
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…
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…
Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping
Umut Şimşekli, 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…
Distributionally Robust Learning with Weakly Convex Losses: Convergence Rates and Finite-Sample Guarantees
Landi Zhu, Mert Gürbüzbalaban, Andrzej Ruszczyński
We consider a distributionally robust stochastic optimization problem and formulate it as a stochastic two-level composition optimization problem with the use of the mean--semidevi…
Heavy-Tail Phenomenon in Decentralized SGD
Mert Gurbuzbalaban, Yuanhan Hu, Umut Simsekli +2
Recent theoretical studies have shown that heavy-tails can emerge in stochastic optimization due to `multiplicative noise', even under surprisingly simple settings, such as linear…