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Join us to hear some new, exciting work at the intersection of optimization and ML. Come and ask questions and join the discussion.
Speakers: Laurent Condat, "Distributed Proximal Splitting Algorithms with Rates and Acceleration" Zhize Li, "PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization" Ohad Shamir, "Can We Find Near-Approximately-Stationary Points of Nonsmooth Nonconvex Functions?" Tiffany Vlaar, "Constraint-Based Regularization of Neural Networks" Mohammadi Zaki, "Employing No Regret Learners for Pure Exploration in Linear Bandits"
You can find a video on the NeurIPS website where the speakers discuss in detail their paper.
Author Information
Sebastian Stich (EPFL)
Dr. [Sebastian U. Stich](https://sstich.ch/) is a faculty at the CISPA Helmholtz Center for Information Security. Research interests: - *methods for machine learning and statistics*—at the interface of theory and practice - *collaborative learning* (distributed, federated and decentralized methods) - *optimization for machine learning* (adaptive stochastic methods and generalization performance)
Laurent Condat (KAUST)
Zhize Li (King Abdullah University of Science and Technology (KAUST))
Ohad Shamir (Weizmann Institute of Science)
Tiffany Vlaar (University of Edinburgh)
Mohammadi Zaki (Indian Institute of Science Bangalore)
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2019 Poster: A unified variance-reduced accelerated gradient method for convex optimization »
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2019 Poster: SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points »
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2018 Poster: Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization »
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2017 Poster: Safe Adaptive Importance Sampling »
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2016 Poster: Dimension-Free Iteration Complexity of Finite Sum Optimization Problems »
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2016 Oral: Without-Replacement Sampling for Stochastic Gradient Methods »
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