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Opening Remarks to Session 4
Quanquan Gu
Author Information
Quanquan Gu (UCLA)
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2021 : Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization »
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2021 : Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization »
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2021 : Faster Perturbed Stochastic Gradient Methods for Finding Local Minima »
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2021 : Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium »
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2022 : A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning »
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2023 Poster: Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure »
Angela Yuan · Chris Junchi Li · Gauthier Gidel · Michael Jordan · Quanquan Gu · Simon Du -
2023 Poster: Corruption-Robust Offline Reinforcement Learning with General Function Approximation »
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2023 Poster: Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks »
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2023 Poster: Robust Learning with Progressive Data Expansion Against Spurious Correlation »
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2023 Poster: Why Does Sharpness-Aware Minimization Generalize Better Than SGD? »
Zixiang Chen · Junkai Zhang · Yiwen Kou · Xiangning Chen · Cho-Jui Hsieh · Quanquan Gu -
2023 Workshop: New Frontiers of AI for Drug Discovery and Development »
Animashree Anandkumar · Ilija Bogunovic · Ti-chiun Chang · Quanquan Gu · Jure Leskovec · Michelle Li · Chong Liu · Nataša Tagasovska · Wei Wang -
2022 Spotlight: Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime »
Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Sham Kakade -
2022 : Contributed Talks 2 »
Quanquan Gu · Aaron Defazio · Jiajin Li -
2022 Workshop: OPT 2022: Optimization for Machine Learning »
Courtney Paquette · Sebastian Stich · Quanquan Gu · Cristóbal Guzmán · John Duchi -
2022 Poster: Towards Understanding the Mixture-of-Experts Layer in Deep Learning »
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2022 Poster: Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs »
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2022 Poster: Benign Overfitting in Two-layer Convolutional Neural Networks »
Yuan Cao · Zixiang Chen · Misha Belkin · Quanquan Gu -
2022 Poster: Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium »
Chris Junchi Li · Dongruo Zhou · Quanquan Gu · Michael Jordan -
2022 Poster: A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits »
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2022 Poster: The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift »
Jingfeng Wu · Difan Zou · Vladimir Braverman · Quanquan Gu · Sham Kakade -
2022 Poster: Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime »
Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Sham Kakade -
2022 Poster: Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions »
Jiafan He · Dongruo Zhou · Tong Zhang · Quanquan Gu -
2022 Poster: Active Ranking without Strong Stochastic Transitivity »
Hao Lou · Tao Jin · Yue Wu · Pan Xu · Quanquan Gu · Farzad Farnoud -
2021 : Contributed talks in Session 4 (Zoom) »
Quanquan Gu · Agnieszka Słowik · Jacques Chen · Neha Wadia · Difan Zou -
2021 Workshop: OPT 2021: Optimization for Machine Learning »
Courtney Paquette · Quanquan Gu · Oliver Hinder · Katya Scheinberg · Sebastian Stich · Martin Takac -
2021 Poster: The Benefits of Implicit Regularization from SGD in Least Squares Problems »
Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Dean Foster · Sham Kakade -
2021 Poster: Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation »
Jiafan He · Dongruo Zhou · Quanquan Gu -
2021 Poster: Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent »
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2021 Poster: Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures »
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2021 Poster: Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs »
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2021 Poster: Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation »
Weitong ZHANG · Dongruo Zhou · Quanquan Gu -
2021 Poster: Variance-Aware Off-Policy Evaluation with Linear Function Approximation »
Yifei Min · Tianhao Wang · Dongruo Zhou · Quanquan Gu -
2021 Poster: Iterative Teacher-Aware Learning »
Luyao Yuan · Dongruo Zhou · Junhong Shen · Jingdong Gao · Jeffrey L Chen · Quanquan Gu · Ying Nian Wu · Song-Chun Zhu -
2021 Poster: Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints »
Tianhao Wang · Dongruo Zhou · Quanquan Gu -
2021 Poster: Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks »
Hanxun Huang · Yisen Wang · Sarah Erfani · Quanquan Gu · James Bailey · Xingjun Ma -
2021 Poster: Do Wider Neural Networks Really Help Adversarial Robustness? »
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2021 Poster: Pure Exploration in Kernel and Neural Bandits »
Yinglun Zhu · Dongruo Zhou · Ruoxi Jiang · Quanquan Gu · Rebecca Willett · Robert Nowak -
2020 : Closing remarks »
Quanquan Gu · Courtney Paquette · Mark Schmidt · Sebastian Stich · Martin Takac -
2020 : Contributed talks in Session 4 (Zoom) »
Quanquan Gu · sanae lotfi · Charles Guille-Escuret · Tolga Ergen · Dongruo Zhou -
2020 : Live Q&A with Deanna Needell and Hanbake Lyu (Zoom) »
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2020 : Welcome remarks to Session 4 »
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2020 Workshop: OPT2020: Optimization for Machine Learning »
Courtney Paquette · Mark Schmidt · Sebastian Stich · Quanquan Gu · Martin Takac -
2020 : Welcome event (gather.town) »
Quanquan Gu · Courtney Paquette · Mark Schmidt · Sebastian Stich · Martin Takac -
2020 Poster: A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks »
Zixiang Chen · Yuan Cao · Quanquan Gu · Tong Zhang -
2020 Poster: Agnostic Learning of a Single Neuron with Gradient Descent »
Spencer Frei · Yuan Cao · Quanquan Gu -
2020 Poster: A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods »
Yue Wu · Weitong ZHANG · Pan Xu · Quanquan Gu -
2019 : Poster and Coffee Break 2 »
Karol Hausman · Kefan Dong · Ken Goldberg · Lihong Li · Lin Yang · Lingxiao Wang · Lior Shani · Liwei Wang · Loren Amdahl-Culleton · Lucas Cassano · Marc Dymetman · Marc Bellemare · Marcin Tomczak · Margarita Castro · Marius Kloft · Marius-Constantin Dinu · Markus Holzleitner · Martha White · Mengdi Wang · Michael Jordan · Mihailo Jovanovic · Ming Yu · Minshuo Chen · Moonkyung Ryu · Muhammad Zaheer · Naman Agarwal · Nan Jiang · Niao He · Nikolaus Yasui · Nikos Karampatziakis · Nino Vieillard · Ofir Nachum · Olivier Pietquin · Ozan Sener · Pan Xu · Parameswaran Kamalaruban · Paul Mineiro · Paul Rolland · Philip Amortila · Pierre-Luc Bacon · Prakash Panangaden · Qi Cai · Qiang Liu · Quanquan Gu · Raihan Seraj · Richard Sutton · Rick Valenzano · Robert Dadashi · Rodrigo Toro Icarte · Roshan Shariff · Roy Fox · Ruosong Wang · Saeed Ghadimi · Samuel Sokota · Sean Sinclair · Sepp Hochreiter · Sergey Levine · Sergio Valcarcel Macua · Sham Kakade · Shangtong Zhang · Sheila McIlraith · Shie Mannor · Shimon Whiteson · Shuai Li · Shuang Qiu · Wai Lok Li · Siddhartha Banerjee · Sitao Luan · Tamer Basar · Thinh Doan · Tianhe Yu · Tianyi Liu · Tom Zahavy · Toryn Klassen · Tuo Zhao · Vicenç Gómez · Vincent Liu · Volkan Cevher · Wesley Suttle · Xiao-Wen Chang · Xiaohan Wei · Xiaotong Liu · Xingguo Li · Xinyi Chen · Xingyou Song · Yao Liu · YiDing Jiang · Yihao Feng · Yilun Du · Yinlam Chow · Yinyu Ye · Yishay Mansour · · Yonathan Efroni · Yongxin Chen · Yuanhao Wang · Bo Dai · Chen-Yu Wei · Harsh Shrivastava · Hongyang Zhang · Qinqing Zheng · SIDDHARTHA SATPATHI · Xueqing Liu · Andreu Vall -
2019 Poster: Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks »
Spencer Frei · Yuan Cao · Quanquan Gu -
2019 Poster: Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks »
Difan Zou · Ziniu Hu · Yewen Wang · Song Jiang · Yizhou Sun · Quanquan Gu -
2019 Poster: Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction »
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2019 Poster: Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks »
Yuan Cao · Quanquan Gu -
2019 Poster: An Improved Analysis of Training Over-parameterized Deep Neural Networks »
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2019 Poster: Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks »
Yuan Cao · Quanquan Gu -
2019 Spotlight: Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks »
Yuan Cao · Quanquan Gu -
2018 Poster: Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima »
Yaodong Yu · Pan Xu · Quanquan Gu -
2018 Poster: Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization »
Pan Xu · Jinghui Chen · Difan Zou · Quanquan Gu -
2018 Spotlight: Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization »
Pan Xu · Jinghui Chen · Difan Zou · Quanquan Gu -
2018 Poster: Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization »
Dongruo Zhou · Pan Xu · Quanquan Gu -
2018 Spotlight: Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization »
Dongruo Zhou · Pan Xu · Quanquan Gu -
2018 Poster: Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization »
Bargav Jayaraman · Lingxiao Wang · David Evans · Quanquan Gu -
2017 Poster: Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization »
Pan Xu · Jian Ma · Quanquan Gu -
2016 Poster: Semiparametric Differential Graph Models »
Pan Xu · Quanquan Gu -
2015 Poster: High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality »
Zhaoran Wang · Quanquan Gu · Yang Ning · Han Liu -
2014 Poster: Sparse PCA with Oracle Property »
Quanquan Gu · Zhaoran Wang · Han Liu -
2014 Poster: Robust Tensor Decomposition with Gross Corruption »
Quanquan Gu · Huan Gui · Jiawei Han -
2012 Poster: Selective Labeling via Error Bound Minimization »
Quanquan Gu · Tong Zhang · Chris Ding · Jiawei Han