Timezone: »
Poster Session 1
Andrew Lowy · Thomas Bonnier · Yiling Xie · Guy Kornowski · Simon Schug · Seungyub Han · Nicolas Loizou · xinwei zhang · Laurent Condat · Tabea E. Röber · Si Yi Meng · Marco Mondelli · Runlong Zhou · Eshaan Nichani · Adrian Goldwaser · Rudrajit Das · Kayhan Behdin · Atish Agarwala · Mukul Gagrani · Gary Cheng · Tian Li · Haoran Sun · Hossein Taheri · Allen Liu · Siqi Zhang · Dmitrii Avdiukhin · Bradley Brown · Miaolan Xie · Junhyung Lyle Kim · Sharan Vaswani · Xinmeng Huang · Ganesh Ramachandra Kini · Angela Yuan · Weiqiang Zheng · Jiajin Li
Author Information
Andrew Lowy (USC)
Thomas Bonnier (Société Générale)
Yiling Xie (Georgia Institute of Technology)
Guy Kornowski (Weizmann Institute of Science)
Simon Schug (ETH Zürich)
Seungyub Han (Seoul National University)
Nicolas Loizou (Johns Hopkins University)
xinwei zhang (University of Minnesota)
Laurent Condat (KAUST)
Tabea E. Röber (University of Amsterdam)
Si Yi Meng (Cornell University)
Marco Mondelli (IST Austria)
Runlong Zhou (Paul G. Allen School of Computer Science & Engineering, University of Washington)
Eshaan Nichani (Princeton University)
Adrian Goldwaser (University of Cambridge)
Rudrajit Das (University of Texas at Austin)
Kayhan Behdin (Massachusetts Institute of Technology)
Atish Agarwala (Google Research)
Mukul Gagrani (Qualcomm)
Gary Cheng (Stanford University)
Tian Li (CMU)
Haoran Sun (Georgia Institute of Technology)
Hossein Taheri (UCSB)
Allen Liu (MIT)
Siqi Zhang (Johns Hopkins University)
Dmitrii Avdiukhin (Indiana University)
Bradley Brown (University of Waterloo)
Miaolan Xie (Cornell University)
Junhyung Lyle Kim (Rice University)
Sharan Vaswani (Simon Fraser University)
Xinmeng Huang (University of Pennsylvania)
Ganesh Ramachandra Kini (UC Santa Barbara)
Angela Yuan (University of California, Los Angeles)
Weiqiang Zheng (Yale University)
Jiajin Li (Stanford University)
More from the Same Authors
-
2020 : Session B, Poster 18: Improving Learning To Branch Via Reinforcement Learning »
Haoran Sun -
2021 Spotlight: Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret »
Jean Tarbouriech · Runlong Zhou · Simon Du · Matteo Pirotta · Michal Valko · Alessandro Lazaric -
2021 : Acceleration and Stability of the Stochastic Proximal Point Algorithm »
Junhyung Lyle Kim · Panos Toulis · Anastasios Kyrillidis -
2021 : Acceleration and Stability of the Stochastic Proximal Point Algorithm »
Junhyung Lyle Kim · Panos Toulis · Anastasios Kyrillidis -
2021 : Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent »
Sharan Vaswani · Benjamin Dubois-Taine · Reza Babanezhad Harikandeh -
2021 : Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses »
Andrew Lowy · Meisam Razaviyayn -
2021 : A Unified Framework to Understand Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective »
xinwei zhang · Mingyi Hong · Nicola Elia -
2021 : Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions »
Xiaotie Deng · Xinyan Hu · Tao Lin · Weiqiang Zheng -
2021 : Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions »
Xiaotie Deng · Xinyan Hu · Tao Lin · Weiqiang Zheng -
2022 : Relating Regularization and Generalization through the Intrinsic Dimension of Activations »
Bradley Brown · Jordan Juravsky · Anthony Caterini · Gabriel Loaiza-Ganem -
2022 : On the Implicit Geometry of Cross-Entropy Parameterizations for Label-Imbalanced Data »
Tina Behnia · Ganesh Ramachandra Kini · Vala Vakilian · Christos Thrampoulidis -
2022 : Bidirectional Adaptive Communication for Heterogeneous Distributed Learning »
Dmitrii Avdiukhin · Vladimir Braverman · Nikita Ivkin · Sebastian Stich -
2022 : Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization »
Chris Junchi Li · Angela Yuan · Gauthier Gidel · Michael Jordan -
2022 : Optimal Complexity in Non-Convex Decentralized Learning over Time-Varying Networks »
Xinmeng Huang · Kun Yuan -
2022 : Target-based Surrogates for Stochastic Optimization »
Jonathan Lavington · Sharan Vaswani · Reza Babanezhad Harikandeh · Mark Schmidt · Nicolas Le Roux -
2022 : Momentum Extragradient is Optimal for Games with Cross-Shaped Spectrum »
Junhyung Lyle Kim · Gauthier Gidel · Anastasios Kyrillidis · Fabian Pedregosa -
2022 : Stochastic Adaptive Regularization Method with Cubics: A High Probability Complexity Bound »
Katya Scheinberg · Miaolan Xie -
2022 : ProxSkip for Stochastic Variational Inequalities: A Federated Learning Algorithm for Provable Communication Acceleration »
Siqi Zhang · Nicolas Loizou -
2022 : Relating Regularization and Generalization through the Intrinsic Dimension of Activations »
Bradley Brown · Jordan Juravsky · Anthony Caterini · Gabriel Loaiza-Ganem -
2022 : Nonsmooth Composite Nonconvex-Concave Minimax Optimization »
Jiajin Li · Linglingzhi Zhu · Anthony Man-Cho So -
2022 : Escaping from Moderately Constrained Saddles »
Dmitrii Avdiukhin · Grigory Yaroslavtsev -
2022 : Uniform Convergence and Generalization for Nonconvex Stochastic Minimax Problems »
Siqi Zhang · Yifan Hu · Liang Zhang · Niao He -
2022 : Semi-Random Sparse Recovery in Nearly-Linear Time »
Jonathan Kelner · Jerry Li · Allen Liu · Aaron Sidford · Kevin Tian -
2022 : Generalization of Decentralized Gradient Descent with Separable Data »
Hossein Taheri · Christos Thrampoulidis -
2022 : Annealed Training for Combinatorial Optimization on Graphs »
Haoran Sun · Etash Guha · Hanjun Dai -
2022 : Differentially Private Adaptive Optimization with Delayed Preconditioners »
Tian Li · Manzil Zaheer · Ken Liu · Sashank Reddi · H. Brendan McMahan · Virginia Smith -
2022 : Differentially Private Adaptive Optimization with Delayed Preconditioners »
Tian Li · Manzil Zaheer · Ken Liu · Sashank Reddi · H. Brendan McMahan · Virginia Smith -
2022 : adaStar: A Method for Adapting to Interpolation »
Gary Cheng · John Duchi -
2022 : A Second-order Regression Model Shows Edge of Stability Behavior »
Fabian Pedregosa · Atish Agarwala · Jeffrey Pennington -
2022 : Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization »
Kayhan Behdin · Qingquan Song · Aman Gupta · Sathiya Selvaraj · David Durfee · Ayan Acharya · Rahul Mazumder -
2022 : Differentially Private Federated Learning with Normalized Updates »
Rudrajit Das · Abolfazl Hashemi · Sujay Sanghavi · Inderjit Dhillon -
2022 : Learning deep neural networks by iterative linearisation »
Adrian Goldwaser · Hong Ge -
2022 : Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability »
Alex Damian · Eshaan Nichani · Jason Lee -
2022 : Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization »
Runlong Zhou · Yuandong Tian · YI WU · Simon Du -
2022 : Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence »
Diyuan Wu · Vyacheslav Kungurtsev · Marco Mondelli -
2022 : A Stochastic Prox-Linear Method for CVaR Minimization »
Si Yi Meng · Vasileios Charisopoulos · Robert Gower -
2022 : Counterfactual Explanations Using Optimization With Constraint Learning »
Donato Maragno · Tabea E. Röber · Ilker Birbil -
2022 : Accelerated Single-Call Methods for Constrained Min-Max Optimization »
Yang Cai · Weiqiang Zheng -
2022 : Accelerated Algorithms for Monotone Inclusion and Constrained Nonconvex-Nonconcave Min-Max Optimization »
Yang Cai · Argyris Oikonomou · Weiqiang Zheng -
2022 : RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates »
Laurent Condat · Peter Richtarik -
2022 : A Unified Framework to Understand Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective »
xinwei zhang · Nicola Elia · Mingyi Hong -
2022 : Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods »
Aleksandr Beznosikov · Eduard Gorbunov · Hugo Berard · Nicolas Loizou -
2022 : Adaptive Methods for Nonconvex Continual Learning »
Seungyub Han · Yeongmo Kim · Taehyun Cho · Jungwoo Lee -
2022 : Random initialisations performing above chance and how to find them »
Frederik Benzing · Simon Schug · Robert Meier · Johannes von Oswald · Yassir Akram · Nicolas Zucchet · Laurence Aitchison · Angelika Steger -
2022 : On the Complexity of Finding Small Subgradients in Nonsmooth Optimization »
Guy Kornowski · Ohad Shamir -
2022 : On the Complexity of Finding Small Subgradients in Nonsmooth Optimization »
Guy Kornowski · Ohad Shamir -
2022 : Solving a Special Type of Optimal Transport Problem by a Modified Hungarian Algorithm »
Yiling Xie · Yiling Luo · Xiaoming Huo -
2022 : Completing the Model Optimization Process by Correcting Patterns of Failure in Regression Tasks »
Thomas Bonnier -
2022 : Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses & Extension to Non-Convex Losses »
Andrew Lowy · Meisam Razaviyayn -
2022 : Building Large Machine Learning Models from Small Distributed Models: A Layer Matching Approach »
xinwei zhang · Bingqing Song · Mehrdad Honarkhah · Jie Ding · Mingyi Hong -
2022 : Motley: Benchmarking Heterogeneity and Personalization in Federated Learning »
Shanshan Wu · Tian Li · Zachary Charles · Yu Xiao · Ken Liu · Zheng Xu · Virginia Smith -
2022 : Causal Inference out of Control: Identifying the Steerability of Consumption »
Gary Cheng · Moritz Hardt · Celestine Mendler-Dünner -
2022 : Tuned Quadratic Landscapes for Benchmarking Model-Guided Protein Design »
Neil Thomas · Atish Agarwala · David Belanger · Yun Song · Lucy Colwell -
2022 : Engineering Uncertainty Representations to Monitor Distribution Shifts »
Thomas Bonnier · Benjamin Bosch -
2022 : The Union of Manifolds Hypothesis »
Bradley Brown · Anthony Caterini · Brendan Ross · Jesse Cresswell · Gabriel Loaiza-Ganem -
2022 : A Stochastic Optimization Framework for Fair Risk Minimization »
Andrew Lowy · Sina Baharlouei · Rakesh Pavan · Meisam Razaviyayn · Ahmad Beirami -
2022 : Synthetic Principle Component Design: Fast Covariate Balancing with Synthetic Controls »
Yiping Lu · Jiajin Li · Lexing Ying · Jose Blanchet -
2022 : A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games »
Samuel Sokota · Ryan D'Orazio · J. Zico Kolter · Nicolas Loizou · Marc Lanctot · Ioannis Mitliagkas · Noam Brown · Christian Kroer -
2022 : Perturbed Quantile Regression for Distributional Reinforcement Learning »
Taehyun Cho · Seungyub Han · Heesoo Lee · Kyungjae Lee · Jungwoo Lee -
2022 : A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning »
Zixiang Chen · Chris Junchi Li · Angela Yuan · Quanquan Gu · Michael Jordan -
2022 : Poster Session 2 »
Jinwuk Seok · Bo Liu · Ryotaro Mitsuboshi · David Martinez-Rubio · Weiqiang Zheng · Ilgee Hong · Chen Fan · Kazusato Oko · Bo Tang · Miao Cheng · Aaron Defazio · Tim G. J. Rudner · Gabriele Farina · Vishwak Srinivasan · Ruichen Jiang · Peng Wang · Jane Lee · Nathan Wycoff · Nikhil Ghosh · Yinbin Han · David Mueller · Liu Yang · Amrutha Varshini Ramesh · Siqi Zhang · Kaifeng Lyu · David Yunis · Kumar Kshitij Patel · Fangshuo Liao · Dmitrii Avdiukhin · Xiang Li · Sattar Vakili · Jiaxin Shi -
2022 : Causal Inference out of Control: Identifying the Steerability of Consumption »
Gary Cheng · Moritz Hardt · Celestine Mendler-Dünner -
2022 : Stochastic Differentially Private and Fair Learning »
Andrew Lowy · Devansh Gupta · Meisam Razaviyayn -
2022 : Contributed Talks 2 »
Quanquan Gu · Aaron Defazio · Jiajin Li -
2022 : Contributed Talks 1 »
Courtney Paquette · Tian Li · Guy Kornowski -
2022 : To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning »
Yae Jee Cho · Divyansh Jhunjhunwala · Tian Li · Virginia Smith · Gauri Joshi -
2022 Poster: A contrastive rule for meta-learning »
Nicolas Zucchet · Simon Schug · Johannes von Oswald · Dominic Zhao · João Sacramento -
2022 Poster: Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials »
Eshaan Nichani · Yu Bai · Jason Lee -
2022 Poster: The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation? »
Jean Barbier · TianQi Hou · Marco Mondelli · Manuel Saenz -
2022 Poster: Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization »
Simone Bombari · Mohammad Hossein Amani · Marco Mondelli -
2022 Poster: Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints »
Xinmeng Huang · Donghwan Lee · Edgar Dobriban · Hamed Hassani -
2022 Poster: Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization »
Kun Yuan · Xinmeng Huang · Yiming Chen · Xiaohan Zhang · Yingya Zhang · Pan Pan -
2022 Poster: Optimal Scaling for Locally Balanced Proposals in Discrete Spaces »
Haoran Sun · Hanjun Dai · Dale Schuurmans -
2022 Poster: Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints »
Jiajin Li · Sirui Lin · Jose Blanchet · Viet Anh Nguyen -
2022 Poster: Imbalance Trouble: Revisiting Neural-Collapse Geometry »
Christos Thrampoulidis · Ganesh Ramachandra Kini · Vala Vakilian · Tina Behnia -
2022 Poster: Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression »
Xinmeng Huang · Yiming Chen · Wotao Yin · Kun Yuan -
2022 Poster: Robust Model Selection and Nearly-Proper Learning for GMMs »
Allen Liu · Jerry Li · Ankur Moitra -
2022 Poster: Near-Optimal Sample Complexity Bounds for Constrained MDPs »
Sharan Vaswani · Lin Yang · Csaba Szepesvari -
2022 Poster: EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization »
Laurent Condat · Kai Yi · Peter Richtarik -
2022 Poster: Finite-Time Last-Iterate Convergence for Learning in Multi-Player Games »
Yang Cai · Argyris Oikonomou · Weiqiang Zheng -
2022 Poster: Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution »
Antonio Orvieto · Simon Lacoste-Julien · Nicolas Loizou -
2021 : Poster Session 2 (gather.town) »
Wenjie Li · Akhilesh Soni · Jinwuk Seok · Jianhao Ma · Jeffery Kline · Mathieu Tuli · Miaolan Xie · Robert Gower · Quanqi Hu · Matteo Cacciola · Yuanlu Bai · Boyue Li · Wenhao Zhan · Shentong Mo · Junhyung Lyle Kim · Sajad Fathi Hafshejani · Chris Junchi Li · Zhishuai Guo · Harshvardhan Harshvardhan · Neha Wadia · Tatjana Chavdarova · Difan Zou · Zixiang Chen · Aman Gupta · Jacques Chen · Betty Shea · Benoit Dherin · Aleksandr Beznosikov -
2021 : Contributed Talks in Session 2 (Zoom) »
Courtney Paquette · Chris Junchi Li · Jeffery Kline · Junhyung Lyle Kim · Pascal Esser -
2021 : Contributed Talk 2: A Unified Framework to Understand Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective »
xinwei zhang · Mingyi Hong · Nicola Elia -
2021 : Poster Session 1 (gather.town) »
Hamed Jalali · Robert Hönig · Maximus Mutschler · Manuel Madeira · Abdurakhmon Sadiev · Egor Shulgin · Alasdair Paren · Pascal Esser · Simon Roburin · Julius Kunze · Agnieszka Słowik · Frederik Benzing · Futong Liu · Hongyi Li · Ryotaro Mitsuboshi · Grigory Malinovsky · Jayadev Naram · Zhize Li · Igor Sokolov · Sharan Vaswani -
2021 Poster: When Are Solutions Connected in Deep Networks? »
Quynh Nguyen · Pierre Bréchet · Marco Mondelli -
2021 Poster: Label-Imbalanced and Group-Sensitive Classification under Overparameterization »
Ganesh Ramachandra Kini · Orestis Paraskevas · Samet Oymak · Christos Thrampoulidis -
2021 Poster: Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing »
Mikhail Khodak · Renbo Tu · Tian Li · Liam Li · Maria-Florina Balcan · Virginia Smith · Ameet Talwalkar -
2021 Poster: Margin-Independent Online Multiclass Learning via Convex Geometry »
Guru Guruganesh · Allen Liu · Jon Schneider · Joshua Wang -
2021 Poster: Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret »
Jean Tarbouriech · Runlong Zhou · Simon Du · Matteo Pirotta · Michal Valko · Alessandro Lazaric -
2021 Poster: Oracle Complexity in Nonsmooth Nonconvex Optimization »
Guy Kornowski · Ohad Shamir -
2021 Poster: Multi-task Learning of Order-Consistent Causal Graphs »
Xinshi Chen · Haoran Sun · Caleb Ellington · Eric Xing · Le Song -
2021 Oral: Oracle Complexity in Nonsmooth Nonconvex Optimization »
Guy Kornowski · Ohad Shamir -
2021 Poster: Learning where to learn: Gradient sparsity in meta and continual learning »
Johannes von Oswald · Dominic Zhao · Seijin Kobayashi · Simon Schug · Massimo Caccia · Nicolas Zucchet · João Sacramento -
2021 Poster: Escaping Saddle Points with Compressed SGD »
Dmitrii Avdiukhin · Grigory Yaroslavtsev -
2021 Poster: An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders »
Xinmeng Huang · Kun Yuan · Xianghui Mao · Wotao Yin -
2021 Poster: PCA Initialization for Approximate Message Passing in Rotationally Invariant Models »
Marco Mondelli · Ramji Venkataramanan -
2020 : Poster Session B »
Ravichandra Addanki · Andreea-Ioana Deac · Yujia Xie · Francesco Landolfi · Antoine Prouvost · Claudius Gros · Renzo Massobrio · Abhishek Cauligi · Simon Alford · Hanjun Dai · Alberto Franzin · Nitish Kumar Panigrahy · Brandon Kates · Iddo Drori · Taoan Huang · Zhou Zhou · Marin Vlastelica · Anselm Paulus · Aaron Zweig · Minsu Cho · Haiyan Yin · Michal Lisicki · Nan Jiang · Haoran Sun -
2020 : Poster Session 2 (gather.town) »
Sharan Vaswani · Nicolas Loizou · Wenjie Li · Preetum Nakkiran · Zhan Gao · Sina Baghal · Jingfeng Wu · Roozbeh Yousefzadeh · Jinyi Wang · Jing Wang · Cong Xie · Anastasia Borovykh · Stanislaw Jastrzebski · Soham Dan · Yiliang Zhang · Mark Tuddenham · Sarath Pattathil · Ievgen Redko · Jeremy Cohen · Yasaman Esfandiari · Zhanhong Jiang · Mostafa ElAraby · Chulhee Yun · Michael Psenka · Robert Gower · Xiaoyu Wang -
2020 : Contributed talks in Session 2 (Zoom) »
Martin Takac · Samuel Horváth · Guan-Horng Liu · Nicolas Loizou · Sharan Vaswani -
2020 : Contributed Video: Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search), Sharan Vaswani »
Sharan Vaswani -
2020 : Contributed Video: How to make your optimizer generalize better, Sharan Vaswani »
Sharan Vaswani -
2020 : Poster Session 1 (gather.town) »
Laurent Condat · Tiffany Vlaar · Ohad Shamir · Mohammadi Zaki · Zhize Li · Guan-Horng Liu · Samuel Horváth · Mher Safaryan · Yoni Choukroun · Kumar Shridhar · Nabil Kahale · Jikai Jin · Pratik Kumar Jawanpuria · Gaurav Kumar Yadav · Kazuki Koyama · Junyoung Kim · Xiao Li · Saugata Purkayastha · Adil Salim · Dighanchal Banerjee · Peter Richtarik · Lakshman Mahto · Tian Ye · Bamdev Mishra · Huikang Liu · Jiajie Zhu -
2020 : Contributed talks in Session 1 (Zoom) »
Sebastian Stich · Laurent Condat · Zhize Li · Ohad Shamir · Tiffany Vlaar · Mohammadi Zaki -
2020 : Contributed Video: Distributed Proximal Splitting Algorithms with Rates and Acceleration, Laurent Condat »
Laurent Condat -
2020 Poster: Tensor Completion Made Practical »
Allen Liu · Ankur Moitra -
2020 Poster: Minibatch Stochastic Approximate Proximal Point Methods »
Hilal Asi · Karan Chadha · Gary Cheng · John Duchi -
2020 Spotlight: Minibatch Stochastic Approximate Proximal Point Methods »
Hilal Asi · Karan Chadha · Gary Cheng · John Duchi -
2020 Poster: Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology »
Quynh Nguyen · Marco Mondelli -
2020 Poster: Myersonian Regression »
Allen Liu · Renato Leme · Jon Schneider -
2019 : Spotlight talks »
Paul Grigas · Zhewei Yao · Aurelien Lucchi · Si Yi Meng -
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: Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates »
Sharan Vaswani · Aaron Mishkin · Issam Laradji · Mark Schmidt · Gauthier Gidel · Simon Lacoste-Julien -
2019 Poster: Robust and Communication-Efficient Collaborative Learning »
Amirhossein Reisizadeh · Hossein Taheri · Aryan Mokhtari · Hamed Hassani · Ramtin Pedarsani -
2018 : Poster session »
David Zeng · Marzieh S. Tahaei · Shuai Chen · Felix Meister · Meet Shah · Anant Gupta · Ajil Jalal · Eirini Arvaniti · David Zimmerer · Konstantinos Kamnitsas · Pedro Ballester · Nathaniel Braman · Udaya Kumar · Sil C. van de Leemput · Junaid Qadir · Hoel Kervadec · Mohamed Akrout · Adrian Tousignant · Matthew Ng · Raghav Mehta · Miguel Monteiro · Sumana Basu · Jonas Adler · Adrian Dalca · Jizong Peng · Sungyeob Han · Xiaoxiao Li · Karthik Gopinath · Joseph Cheng · Bogdan Georgescu · Kha Gia Quach · Karthik Sarma · David Van Veen