1 citations · 1 across the 6 of their papers we have counts for
6 papers
Asymptotic Bounds and Online Algorithms for Average-Case Matrix Discrepancy
Dmitriy Kunisky, Timm Oertel, Nicola Wengiel +1
We study the matrix discrepancy problem in the average-case setting. Given a sequence of symmetric matrices , its discrepancy is defined as the minimal…
Average-Case Matrix Discrepancy: Asymptotics and Online Algorithms
Dmitriy Kunisky, Peiyuan Zhang
We study the operator norm discrepancy of i.i.d. random matrices, initiating the matrix-valued analog of a long line of work on the norm discrepancy of i.i.d. rando…
Lower Generalization Bounds for GD and SGD in Smooth Stochastic Convex Optimization
Peiyuan Zhang, Jiaye Teng, Jingzhao Zhang
This work studies the generalization error of gradient methods. More specifically, we focus on how training steps and step-size might affect generalization in smooth stocha…
Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm
Peiyuan Zhang, Jingzhao Zhang, Suvrit Sra
Deciding whether saddle points exist or are approximable for nonconvex-nonconcave problems is usually intractable. This paper takes a step towards understanding a broad class of no…
Rethinking the Variational Interpretation of Nesterov's Accelerated Method
Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand
The continuous-time model of Nesterov's momentum provides a thought-provoking perspective for understanding the nature of the acceleration phenomenon in convex optimization. One of…
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization
Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand +2
Viewing optimization methods as numerical integrators for ordinary differential equations (ODEs) provides a thought-provoking modern framework for studying accelerated first-order…