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Quanquan Gu · Courtney Paquette · Mark Schmidt · Sebastian Stich · Martin Takac
Event URL: https://neurips.gather.town/app/YuI0sg9tIRcx6IeY/OPT+ML%20Lounge »
Please join us in gather.town for all breaks and poster sessions. Click on "Open Link" to join gather.town.
Author Information
Quanquan Gu (UCLA)
Courtney Paquette (McGill University)
Mark Schmidt (University of British Columbia)
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)
Martin Takac (Lehigh University)
More from the Same Authors
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2021 : Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization »
Difan Zou · Yuan Cao · Yuanzhi Li · Quanquan Gu -
2021 : Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization »
Difan Zou · Yuan Cao · Yuanzhi Li · Quanquan Gu -
2021 : Heavy-tailed noise does not explain the gap between SGD and Adam on Transformers »
Jacques Chen · Frederik Kunstner · Mark Schmidt -
2021 : Heavy-tailed noise does not explain the gap between SGD and Adam on Transformers »
Jacques Chen · Frederik Kunstner · Mark Schmidt -
2021 : Random-reshuffled SARAH does not need a full gradient computations »
Aleksandr Beznosikov · Martin Takac -
2021 : Escaping Local Minima With Stochastic Noise »
Harshvardhan Harshvardhan · Sebastian Stich -
2021 : Faster Perturbed Stochastic Gradient Methods for Finding Local Minima »
Zixiang Chen · Dongruo Zhou · Quanquan Gu -
2021 : Faster Quasi-Newton Methods for Linear Composition Problems »
Betty Shea · Mark Schmidt -
2021 : A Closer Look at Gradient Estimators with Reinforcement Learning as Inference »
Jonathan Lavington · Michael Teng · Mark Schmidt · Frank Wood -
2021 : An Empirical Study of Non-Uniform Sampling in Off-Policy Reinforcement Learning for Continuous Control »
Nicholas Ioannidis · Jonathan Lavington · Mark Schmidt -
2021 : Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium »
Chris Junchi Li · Dongruo Zhou · Quanquan Gu · Michael Jordan -
2021 : The Peril of Popular Deep Learning Uncertainty Estimation Methods »
Yehao Liu · Matteo Pagliardini · Tatjana Chavdarova · Sebastian Stich -
2022 : Target-based Surrogates for Stochastic Optimization »
Jonathan Lavington · Sharan Vaswani · Reza Babanezhad Harikandeh · Mark Schmidt · Nicolas Le Roux -
2022 : Fast Convergence of Greedy 2-Coordinate Updates for Optimizing with an Equality Constraint »
Amrutha Varshini Ramesh · Aaron Mishkin · Mark Schmidt -
2022 : Fast Convergence of Random Reshuffling under Interpolation and the Polyak-Łojasiewicz Condition »
Chen Fan · Christos Thrampoulidis · Mark Schmidt -
2022 : Effects of momentum scaling for SGD »
Dmitry A. Pasechnyuk · Alexander Gasnikov · Martin Takac -
2022 : Using quadratic equations for overparametrized models »
Shuang Li · William Swartworth · Martin Takac · Deanna Needell · Robert Gower -
2022 : FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences »
Artem Agafonov · Brahim Erraji · Martin Takac -
2022 : Cubic Regularized Quasi-Newton Methods »
Dmitry Kamzolov · Klea Ziu · Artem Agafonov · Martin Takac -
2022 : PSPS: Preconditioned Stochastic Polyak Step-size method for badly scaled data »
Farshed Abdukhakimov · Chulu Xiang · Dmitry Kamzolov · Robert Gower · Martin Takac -
2022 : Practical Structured Riemannian Optimization with Momentum by using Generalized Normal Coordinates »
Wu Lin · Valentin Duruisseaux · Melvin Leok · Frank Nielsen · Mohammad Emtiyaz Khan · Mark Schmidt -
2022 : A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning »
Zixiang Chen · Chris Junchi Li · Angela Yuan · Quanquan Gu · Michael Jordan -
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 : Closing Remarks »
Courtney Paquette -
2022 : Contributed Talks 2 »
Quanquan Gu · Aaron Defazio · Jiajin Li -
2022 : Contributed Talks 1 »
Courtney Paquette · Tian Li · Guy Kornowski -
2022 Workshop: OPT 2022: Optimization for Machine Learning »
Courtney Paquette · Sebastian Stich · Quanquan Gu · Cristóbal Guzmán · John Duchi -
2022 : Welcome Remarks »
Courtney Paquette -
2022 Workshop: Order up! The Benefits of Higher-Order Optimization in Machine Learning »
Albert Berahas · Jelena Diakonikolas · Jarad Forristal · Brandon Reese · Martin Takac · Yan Xu -
2022 Poster: Towards Understanding the Mixture-of-Experts Layer in Deep Learning »
Zixiang Chen · Yihe Deng · Yue Wu · Quanquan Gu · Yuanzhi Li -
2022 Poster: Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs »
Dongruo Zhou · Quanquan Gu -
2022 Poster: Benign Overfitting in Two-layer Convolutional Neural Networks »
Yuan Cao · Zixiang Chen · Misha Belkin · Quanquan Gu -
2022 Poster: Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions »
Courtney Paquette · Elliot Paquette · Ben Adlam · Jeffrey Pennington -
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 »
Jiafan He · Tianhao Wang · Yifei Min · Quanquan Gu -
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: A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate »
Slavomír Hanzely · Dmitry Kamzolov · Dmitry Pasechnyuk · Alexander Gasnikov · Peter Richtarik · Martin Takac -
2022 Poster: Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions »
Jiafan He · Dongruo Zhou · Tong Zhang · Quanquan Gu -
2022 Poster: Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions »
Kiwon Lee · Andrew Cheng · Elliot Paquette · Courtney Paquette -
2022 Poster: Active Ranking without Strong Stochastic Transitivity »
Hao Lou · Tao Jin · Yue Wu · Pan Xu · Quanquan Gu · Farzad Farnoud -
2021 : Closing remarks »
Courtney Paquette -
2021 : Contributed talks in Session 4 (Zoom) »
Quanquan Gu · Agnieszka Słowik · Jacques Chen · Neha Wadia · Difan Zou -
2021 : Opening Remarks to Session 4 »
Quanquan Gu -
2021 : Contributed Talks in Session 2 (Zoom) »
Courtney Paquette · Chris Junchi Li · Jeffery Kline · Junhyung Lyle Kim · Pascal Esser -
2021 : Opening Remarks to Session 2 »
Courtney Paquette -
2021 : Contributed Talks in Session 1 (Zoom) »
Sebastian Stich · Futong Liu · Abdurakhmon Sadiev · Frederik Benzing · Simon Roburin -
2021 : Opening Remarks to Session 1 »
Sebastian Stich -
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 »
Spencer Frei · Quanquan Gu -
2021 Poster: Breaking the centralized barrier for cross-device federated learning »
Sai Praneeth Karimireddy · Martin Jaggi · Satyen Kale · Mehryar Mohri · Sashank Reddi · Sebastian Stich · Ananda Theertha Suresh -
2021 Poster: Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures »
Yuan Cao · Quanquan Gu · Mikhail Belkin -
2021 Poster: Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs »
Jiafan He · Dongruo Zhou · Quanquan Gu -
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: RelaySum for Decentralized Deep Learning on Heterogeneous Data »
Thijs Vogels · Lie He · Anastasiia Koloskova · Sai Praneeth Karimireddy · Tao Lin · Sebastian Stich · Martin Jaggi -
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: An Improved Analysis of Gradient Tracking for Decentralized Machine Learning »
Anastasiia Koloskova · Tao Lin · Sebastian Stich -
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? »
Boxi Wu · Jinghui Chen · Deng Cai · Xiaofei He · Quanquan Gu -
2021 Poster: Pure Exploration in Kernel and Neural Bandits »
Yinglun Zhu · Dongruo Zhou · Ruoxi Jiang · Quanquan Gu · Rebecca Willett · Robert Nowak -
2021 Poster: Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models »
Courtney Paquette · Elliot Paquette -
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) »
Quanquan Gu -
2020 : Welcome remarks to Session 4 »
Quanquan Gu -
2020 : Live Q&A with Michael Friedlander (Zoom) »
Mark Schmidt -
2020 : Intro to Invited Speaker 8 »
Mark Schmidt -
2020 : Contributed talks in Session 3 (Zoom) »
Mark Schmidt · Zhan Gao · Wenjie Li · Preetum Nakkiran · Denny Wu · Chengrun Yang -
2020 : Live Q&A with Rachel Ward (Zoom) »
Mark Schmidt -
2020 : Live Q&A with Ashia Wilson (Zoom) »
Mark Schmidt -
2020 : Welcome remarks to Session 3 »
Mark Schmidt -
2020 : Live Q&A with Suvrit Sra (Zoom) »
Martin Takac -
2020 : Intro to Invited Speaker 5 »
Martin Takac -
2020 : Contributed talks in Session 2 (Zoom) »
Martin Takac · Samuel Horváth · Guan-Horng Liu · Nicolas Loizou · Sharan Vaswani -
2020 : Live Q&A with Donald Goldfarb (Zoom) »
Martin Takac -
2020 : Live Q&A with Andreas Krause (Zoom) »
Martin Takac -
2020 : Welcome remarks to Session 2 »
Martin Takac -
2020 : Contributed talks in Session 1 (Zoom) »
Sebastian Stich · Laurent Condat · Zhize Li · Ohad Shamir · Tiffany Vlaar · Mohammadi Zaki -
2020 : Live Q&A with Volkan Cevher (Zoom) »
Sebastian Stich -
2020 : Live Q&A with Tong Zhang (Zoom) »
Sebastian Stich -
2020 : Welcome remarks to Session 1 »
Sebastian Stich -
2020 Workshop: OPT2020: Optimization for Machine Learning »
Courtney Paquette · Mark Schmidt · Sebastian Stich · Quanquan Gu · 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: Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses »
Yihan Zhou · Victor Sanches Portella · Mark Schmidt · Nicholas Harvey -
2020 Poster: Ensemble Distillation for Robust Model Fusion in Federated Learning »
Tao Lin · Lingjing Kong · Sebastian Stich · Martin Jaggi -
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 Session »
Eduard Gorbunov · Alexandre d'Aspremont · Lingxiao Wang · Liwei Wang · Boris Ginsburg · Alessio Quaglino · Camille Castera · Saurabh Adya · Diego Granziol · Rudrajit Das · Raghu Bollapragada · Fabian Pedregosa · Martin Takac · Majid Jahani · Sai Praneeth Karimireddy · Hilal Asi · Balint Daroczy · Leonard Adolphs · Aditya Rawal · Nicolas Brandt · Minhan Li · Giuseppe Ughi · Orlando Romero · Ivan Skorokhodov · Damien Scieur · Kiwook Bae · Konstantin Mishchenko · Rohan Anil · Vatsal Sharan · Aditya Balu · Chao Chen · Zhewei Yao · Tolga Ergen · Paul Grigas · Chris Junchi Li · Jimmy Ba · Stephen J Roberts · Sharan Vaswani · Armin Eftekhari · Chhavi Sharma -
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: Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates »
Sharan Vaswani · Aaron Mishkin · Issam Laradji · Mark Schmidt · Gauthier Gidel · Simon Lacoste-Julien -
2019 Poster: Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction »
Difan Zou · Pan Xu · Quanquan Gu -
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 »
Difan Zou · Quanquan Gu -
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 Poster: SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient »
Aaron Mishkin · Frederik Kunstner · Didrik Nielsen · Mark Schmidt · Mohammad Emtiyaz Khan -
2018 Spotlight: Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization »
Pan Xu · Jinghui Chen · Difan Zou · Quanquan Gu -
2018 Poster: Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization »
Robert Gower · Filip Hanzely · Peter Richtarik · Sebastian Stich -
2018 Poster: Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization »
Dongruo Zhou · Pan Xu · Quanquan Gu -
2018 Poster: Reinforcement Learning for Solving the Vehicle Routing Problem »
MohammadReza Nazari · Afshin Oroojlooy · Lawrence Snyder · Martin Takac -
2018 Spotlight: Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization »
Dongruo Zhou · Pan Xu · Quanquan Gu -
2018 Poster: Sparsified SGD with Memory »
Sebastian Stich · Jean-Baptiste Cordonnier · Martin Jaggi -
2018 Poster: Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization »
Bargav Jayaraman · Lingxiao Wang · David Evans · Quanquan Gu -
2017 Poster: Safe Adaptive Importance Sampling »
Sebastian Stich · Anant Raj · Martin Jaggi -
2017 Spotlight: Safe Adaptive Importance Sampling »
Sebastian Stich · Anant Raj · Martin Jaggi -
2017 Poster: Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization »
Pan Xu · Jian Ma · Quanquan Gu -
2016 : Fast Patch-based Style Transfer of Arbitrary Style »
Tian Qi Chen · Mark Schmidt -
2016 Poster: Semiparametric Differential Graph Models »
Pan Xu · Quanquan Gu -
2016 Poster: A Multi-Batch L-BFGS Method for Machine Learning »
Albert Berahas · Jorge Nocedal · Martin Takac -
2015 Poster: High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality »
Zhaoran Wang · Quanquan Gu · Yang Ning · Han Liu -
2015 Poster: StopWasting My Gradients: Practical SVRG »
Reza Babanezhad Harikandeh · Mohamed Osama Ahmed · Alim Virani · Mark Schmidt · Jakub Konečný · Scott Sallinen -
2014 Poster: Communication-Efficient Distributed Dual Coordinate Ascent »
Martin Jaggi · Virginia Smith · Martin Takac · Jonathan Terhorst · Sanjay Krishnan · Thomas Hofmann · Michael Jordan -
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