Timezone: »
Poster
Stochastic Gradient and Langevin Processes
Xiang Cheng · Dong Yin · Peter Bartlett · Michael Jordan
Wed Jul 15 08:00 AM -- 08:45 AM & Wed Jul 15 09:00 PM -- 09:45 PM (PDT) @ None #None
We prove quantitative convergence rates at which discrete Langevin-like processes converge to the invariant distribution of a related stochastic differential equation. We study the setup where the additive noise can be non-Gaussian and state-dependent and the potential function can be non-convex. We show that the key properties of these processes depend on the potential function and the second moment of the additive noise. We apply our theoretical findings to studying the convergence of Stochastic Gradient Descent (SGD) for non-convex problems and corroborate them with experiments using SGD to train deep neural networks on the CIFAR-10 dataset.
Author Information
Xiang Cheng (UC Berkeley)
Dong Yin (UC Berkeley)
Peter Bartlett (Berkeley)
Michael Jordan (UC Berkeley)
More from the Same Authors
-
2020 Poster: On Thompson Sampling with Langevin Algorithms »
Eric Mazumdar · Aldo Pacchiano · Yian Ma · Michael Jordan · Peter Bartlett -
2020 Poster: Accelerated Message Passing for Entropy-Regularized MAP Inference »
Jonathan Lee · Aldo Pacchiano · Peter Bartlett · Michael Jordan -
2020 Poster: On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems »
Darren Lin · Chi Jin · Michael Jordan -
2020 Poster: Continuous-time Lower Bounds for Gradient-based Algorithms »
Michael Muehlebach · Michael Jordan -
2020 Poster: Learning to Score Behaviors for Guided Policy Optimization »
Aldo Pacchiano · Jack Parker-Holder · Yunhao Tang · Krzysztof Choromanski · Anna Choromanska · Michael Jordan -
2020 Poster: Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games »
Darren Lin · Zhengyuan Zhou · Panayotis Mertikopoulos · Michael Jordan -
2020 Poster: What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? »
Chi Jin · Praneeth Netrapalli · Michael Jordan -
2019 Poster: Bridging Theory and Algorithm for Domain Adaptation »
Yuchen Zhang · Tianle Liu · Mingsheng Long · Michael Jordan -
2019 Poster: Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning »
Dong Yin · Yudong Chen · Kannan Ramchandran · Peter Bartlett -
2019 Oral: Bridging Theory and Algorithm for Domain Adaptation »
Yuchen Zhang · Tianle Liu · Mingsheng Long · Michael Jordan -
2019 Oral: Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning »
Dong Yin · Yudong Chen · Kannan Ramchandran · Peter Bartlett -
2019 Poster: Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers »
Hong Liu · Mingsheng Long · Jianmin Wang · Michael Jordan -
2019 Poster: Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation »
Kaichao You · Ximei Wang · Mingsheng Long · Michael Jordan -
2019 Poster: A Dynamical Systems Perspective on Nesterov Acceleration »
Michael Muehlebach · Michael Jordan -
2019 Poster: Theoretically Principled Trade-off between Robustness and Accuracy »
Hongyang Zhang · Yaodong Yu · Jiantao Jiao · Eric Xing · Laurent El Ghaoui · Michael Jordan -
2019 Oral: A Dynamical Systems Perspective on Nesterov Acceleration »
Michael Muehlebach · Michael Jordan -
2019 Oral: Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation »
Kaichao You · Ximei Wang · Mingsheng Long · Michael Jordan -
2019 Oral: Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers »
Hong Liu · Mingsheng Long · Jianmin Wang · Michael Jordan -
2019 Oral: Theoretically Principled Trade-off between Robustness and Accuracy »
Hongyang Zhang · Yaodong Yu · Jiantao Jiao · Eric Xing · Laurent El Ghaoui · Michael Jordan -
2019 Poster: Rademacher Complexity for Adversarially Robust Generalization »
Dong Yin · Kannan Ramchandran · Peter Bartlett -
2019 Poster: On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms »
Darren Lin · Nhat Ho · Michael Jordan -
2019 Poster: Rao-Blackwellized Stochastic Gradients for Discrete Distributions »
Runjing Liu · Jeffrey Regier · Nilesh Tripuraneni · Michael Jordan · Jon McAuliffe -
2019 Oral: Rademacher Complexity for Adversarially Robust Generalization »
Dong Yin · Kannan Ramchandran · Peter Bartlett -
2019 Oral: Rao-Blackwellized Stochastic Gradients for Discrete Distributions »
Runjing Liu · Jeffrey Regier · Nilesh Tripuraneni · Michael Jordan · Jon McAuliffe -
2019 Oral: On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms »
Darren Lin · Nhat Ho · Michael Jordan -
2018 Poster: On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo »
Niladri S Chatterji · Nicolas Flammarion · Yian Ma · Peter Bartlett · Michael Jordan -
2018 Poster: RLlib: Abstractions for Distributed Reinforcement Learning »
Eric Liang · Richard Liaw · Robert Nishihara · Philipp Moritz · Roy Fox · Ken Goldberg · Joseph Gonzalez · Michael Jordan · Ion Stoica -
2018 Oral: On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo »
Niladri S Chatterji · Nicolas Flammarion · Yian Ma · Peter Bartlett · Michael Jordan -
2018 Oral: RLlib: Abstractions for Distributed Reinforcement Learning »
Eric Liang · Richard Liaw · Robert Nishihara · Philipp Moritz · Roy Fox · Ken Goldberg · Joseph Gonzalez · Michael Jordan · Ion Stoica -
2018 Poster: SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate »
Aaditya Ramdas · Tijana Zrnic · Martin Wainwright · Michael Jordan -
2018 Poster: Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates »
Dong Yin · Yudong Chen · Kannan Ramchandran · Peter Bartlett -
2018 Oral: SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate »
Aaditya Ramdas · Tijana Zrnic · Martin Wainwright · Michael Jordan -
2018 Oral: Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates »
Dong Yin · Yudong Chen · Kannan Ramchandran · Peter Bartlett -
2018 Poster: Learning to Explain: An Information-Theoretic Perspective on Model Interpretation »
Jianbo Chen · Le Song · Martin Wainwright · Michael Jordan -
2018 Oral: Learning to Explain: An Information-Theoretic Perspective on Model Interpretation »
Jianbo Chen · Le Song · Martin Wainwright · Michael Jordan -
2017 Poster: How to Escape Saddle Points Efficiently »
Chi Jin · Rong Ge · Praneeth Netrapalli · Sham Kakade · Michael Jordan -
2017 Talk: How to Escape Saddle Points Efficiently »
Chi Jin · Rong Ge · Praneeth Netrapalli · Sham Kakade · Michael Jordan -
2017 Poster: Deep Transfer Learning with Joint Adaptation Networks »
Mingsheng Long · Han Zhu · Jianmin Wang · Michael Jordan -
2017 Poster: Breaking Locality Accelerates Block Gauss-Seidel »
Stephen Tu · Shivaram Venkataraman · Ashia Wilson · Alex Gittens · Michael Jordan · Benjamin Recht -
2017 Talk: Deep Transfer Learning with Joint Adaptation Networks »
Mingsheng Long · Han Zhu · Jianmin Wang · Michael Jordan -
2017 Talk: Breaking Locality Accelerates Block Gauss-Seidel »
Stephen Tu · Shivaram Venkataraman · Ashia Wilson · Alex Gittens · Michael Jordan · Benjamin Recht