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Papers, talks, and announcements, newest first.
Invited talk, “Robust and Risk-Sensitive Acceleration in Gradient Methods,” at the Financial/Actuarial Mathematics Seminar, University of Michigan, Ann Arbor (October 7). →
“RESIST: resilient decentralized learning using consensus gradient descent” (with C. Fang, R. Dixit, and W. U. Bajwa) appears in Transactions on Machine Learning Research with a Featured certification. →
“Accelerated gradient methods with biased gradient estimates: risk sensitivity, high-probability guarantees, and large deviation bounds” (with Y. Syed and N. S. Aybat) appears in the Journal of Nonlinear and Variational Analysis. →
“Mean-semideviation-based distributionally robust learning with weakly convex losses” (with L. Zhu and A. Ruszczyński) appears in Mathematical Programming 215(1). →
“Robustly stable accelerated momentum methods with a near-optimal L2 gain and H∞ performance” appears in Mathematics of Operations Research. The paper is dedicated to Michael L. Overton. →
“Entropic risk-averse generalized momentum methods” (with B. Can) appears in Optimization Methods and Software 40(6). →
Two papers appear in the Journal of Machine Learning Research — on constrained sampling with penalized Langevin Monte Carlo, and on high-probability, risk-averse guarantees for stochastic accelerated primal-dual methods — alongside “Robust accelerated primal-dual methods for computing saddle points” in the SIAM Journal on Optimization and a NeurIPS 2024 paper on stochastic non-convex minimax optimization. →