13 Results

Poster	Wed 14:00	Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality Ilyas Fatkhullin · Jalal Etesami · Niao He · Negar Kiyavash
Poster	Tue 14:00	EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization Laurent Condat · Kai Yi · Peter Richtarik
Poster	Tue 9:00	SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning Haibo Yang · Zhuqing Liu · Xin Zhang · Jia Liu
Poster	Thu 14:00	Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions Xufeng Cai · Chaobing Song · Cristóbal Guzmán · Jelena Diakonikolas
Poster	Thu 14:00	A framework for bilevel optimization that enables stochastic and global variance reduction algorithms Mathieu Dagréou · Pierre Ablin · Samuel Vaiter · Thomas Moreau
Poster	Thu 14:00	Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization Ali Kavis · Stratis Skoulakis · Kimon Antonakopoulos · Leello Tadesse Dadi · Volkan Cevher
Poster	Thu 14:00	Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning Grigory Malinovsky · Kai Yi · Peter Richtarik
Poster	Tue 14:00	Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization Wei Jiang · Gang Li · Yibo Wang · Lijun Zhang · Tianbao Yang
Poster	Wed 14:00	Coordinate Linear Variance Reduction for Generalized Linear Programming Chaobing Song · Cheuk Yin Lin · Stephen Wright · Jelena Diakonikolas
Workshop		Variance Reduction in Off-Policy Deep Reinforcement Learning using Spectral Normalization Payal Bawa · Rafael Oliveira · Fabio Ramos
Workshop		Reducing Communication in Nonconvex Federated Learning with a Novel Single-Loop Variance Reduction Method Kazusato Oko · Shunta Akiyama · Tomoya Murata · Taiji Suzuki
Poster	Tue 9:00	Gradient Estimation with Discrete Stein Operators Jiaxin Shi · Yuhao Zhou · Jessica Hwang · Michalis Titsias · Lester Mackey