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Poster
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Thu 14:00
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Detection and Localization of Changes in Conditional Distributions
Lizhen Nie · Dan Nicolae
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Poster
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Quasi-Newton Methods for Saddle Point Problems
Chengchang Liu · Luo Luo
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Poster
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Tue 14:00
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Fast Bayesian Estimation of Point Process Intensity as Function of Covariates
Hideaki Kim · Taichi Asami · Hiroyuki Toda
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Workshop
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Stochastic Adaptive Regularization Method with Cubics: A High Probability Complexity Bound
Katya Scheinberg · Miaolan Xie
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Workshop
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Fri 6:30
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Efficient Second-Order Stochastic Methods for Machine Learning
Donald Goldfarb
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Poster
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A Unified Convergence Theorem for Stochastic Optimization Methods
Xiao Li · Andre Milzarek
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Poster
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Tue 9:00
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Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling
Dmitry Kovalev · Alexander Gasnikov · Peter Richtarik
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Poster
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Thu 9:00
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An In-depth Study of Stochastic Backpropagation
Jun Fang · Mingze Xu · Hao Chen · Bing Shuai · Zhuowen Tu · Joseph Tighe
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Poster
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Tue 14:00
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Optimal Algorithms for Decentralized Stochastic Variational Inequalities
Dmitry Kovalev · Aleksandr Beznosikov · Abdurakhmon Sadiev · Michael Persiianov · Peter Richtarik · Alexander Gasnikov
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Poster
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Tue 9:00
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Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
Aleksandr Beznosikov · Peter Richtarik · Michael Diskin · Max Ryabinin · Alexander Gasnikov
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Workshop
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A Stochastic Prox-Linear Method for CVaR Minimization
Si Yi Meng · Vasileios Charisopoulos · Robert Gower
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Workshop
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Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
Aleksandr Beznosikov · Eduard Gorbunov · Hugo Berard · Nicolas Loizou
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