More from the Same Authors

• 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
• 2023 Poster: Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking »
  Frederik Kunstner · Victor Sanches Portella · Mark Schmidt · Nicholas Harvey
• 2023 Poster: Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models »
  Leonardo Galli · Holger Rauhut · Mark Schmidt
• 2023 Poster: BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization »
  Chen Fan · Gaspard Choné-Ducasse · Mark Schmidt · Christos Thrampoulidis
• 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: Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities »
  Aleksandr Beznosikov · Martin Takac · Alexander Gasnikov
• 2023 Poster: Corruption-Robust Offline Reinforcement Learning with General Function Approximation »
  Chenlu Ye · Rui Yang · Quanquan Gu · Tong Zhang
• 2023 Poster: Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks »
  Yiwen Kou · Zixiang Chen · Quanquan Gu
• 2023 Poster: Byzantine-Tolerant Methods for Distributed Variational Inequalities »
  Nazarii Tupitsa · Eduard Gorbunov · Abdulla Jasem Almansoori · Yanlin Wu · Martin Takac · Karthik Nandakumar · Samuel Horváth
• 2023 Poster: Robust Learning with Progressive Data Expansion Against Spurious Correlation »
  Yihe Deng · Yu Yang · Baharan Mirzasoleiman · Quanquan Gu
• 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 : Contributed Talks 2 »
  Courtney Paquette
• 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
• 2023 Workshop: OPT 2023: Optimization for Machine Learning »
  Cristóbal Guzmán · Courtney Paquette · Katya Scheinberg · Aaron Sidford · Sebastian Stich
• 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