Timezone: »
Contributed Talks in Session 2 (Zoom)
Courtney Paquette · Chris Junchi Li · Jeffery Kline · Junhyung Lyle Kim · Pascal Esser
Oral (10 min)
- On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging, Junchi Li
Spotlights (5 min)
- Farkas' Theorem of the Alternative for Prior Knowledge in Deep Networks, Jeffery Kline
- Acceleration and Stability of the Stochastic Proximal Point Algorithm, Lyle Kim
- Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds, Pascal Esser
There will be a Q&A in the last 5 minutes for all speakers. Abstracts for the talks are below the schedule.
Author Information
Courtney Paquette (McGill University)
Chris Junchi Li (University of California, Berkeley)
Jeffery Kline (American Family Insurance)
Junhyung Lyle Kim (Rice University)
Pascal Esser (Technical University of Munich)
More from the Same Authors
-
2021 : Farkas' Theorem of the Alternative for Prior Knowledge in Deep Networks »
Jeffery Kline · Joseph Bockhorst -
2021 : Farkas' Theorem of the Alternative for Prior Knowledge in Deep Networks »
Jeffery Kline · Joseph Bockhorst -
2021 : Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds »
Pascal Esser · Frank Nielsen -
2021 : Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds »
Pascal Esser · Frank Nielsen -
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 : On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging »
Chris Junchi Li · Yaodong Yu · Nicolas Loizou · Gauthier Gidel · Yi Ma · Nicolas Le Roux perso · Michael Jordan -
2021 : On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging »
Chris Junchi Li · Yaodong Yu · Nicolas Loizou · Gauthier Gidel · Yi Ma · Nicolas Le Roux perso · Michael Jordan -
2021 : Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium »
Chris Junchi Li · Dongruo Zhou · Quanquan Gu · Michael Jordan -
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 : Momentum Extragradient is Optimal for Games with Cross-Shaped Spectrum »
Junhyung Lyle Kim · Gauthier Gidel · Anastasios Kyrillidis · Fabian Pedregosa -
2022 : A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning »
Zixiang Chen · Chris Junchi Li · Angela Yuan · Quanquan Gu · Michael Jordan -
2022 : Closing Remarks »
Courtney Paquette -
2022 : 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 -
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 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: Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions »
Kiwon Lee · Andrew Cheng · Elliot Paquette · Courtney Paquette -
2021 : Closing remarks »
Courtney Paquette -
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 : Opening Remarks to Session 2 »
Courtney Paquette -
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 Workshop: OPT 2021: Optimization for Machine Learning »
Courtney Paquette · Quanquan Gu · Oliver Hinder · Katya Scheinberg · Sebastian Stich · Martin Takac -
2021 Poster: Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks »
Pascal Esser · Leena Chennuru Vankadara · Debarghya Ghoshdastidar -
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 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 : Spotlight: Weighting Vectors for Machine Learning: Numerical Harmonic Analysis Applied to Boundary Detection »
Eric Bunch · Jeffery Kline · Daniel Dickinson · Glenn Fung -
2020 Poster: Near-Optimal Comparison Based Clustering »
Michaël Perrot · Pascal Esser · Debarghya Ghoshdastidar -
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: Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent »
Wenqing Hu · Chris Junchi Li · Xiangru Lian · Ji Liu · Angela Yuan -
2018 Poster: SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator »
Cong Fang · Chris Junchi Li · Zhouchen Lin · Tong Zhang -
2018 Spotlight: SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator »
Cong Fang · Chris Junchi Li · Zhouchen Lin · Tong Zhang -
2017 Poster: Diffusion Approximations for Online Principal Component Estimation and Global Convergence »
Chris Junchi Li · Mengdi Wang · Tong Zhang -
2017 Oral: Diffusion Approximations for Online Principal Component Estimation and Global Convergence »
Chris Junchi Li · Mengdi Wang · Tong Zhang -
2016 Poster: Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes »
Chris Junchi Li · Zhaoran Wang · Han Liu