Timezone: »
Poster
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
Arun Suggala · Praneeth Netrapalli
We consider the problem of online learning and its application to solving minimax games. For the online learning problem, Follow the Perturbed Leader (FTPL) is a widely studied algorithm which enjoys the optimal $O(T^{1/2})$ \emph{worst case} regret guarantee for both convex and nonconvex losses. In this work, we show that when the sequence of loss functions is \emph{predictable}, a simple modification of FTPL which incorporates optimism can achieve better regret guarantees, while retaining the optimal worst-case regret guarantee for unpredictable sequences. A key challenge in obtaining these tighter regret bounds is the stochasticity and optimism in the algorithm, which requires different analysis techniques than those commonly used in the analysis of FTPL. The key ingredient we utilize in our analysis is the dual view of perturbation as regularization.
While our algorithm has several applications, we consider the specific application of minimax games. For solving smooth convex-concave games, our algorithm only requires access to a linear optimization oracle. For Lipschitz and smooth nonconvex-nonconcave games, our algorithm requires access to an optimization oracle which computes the perturbed best response. In both these settings, our algorithm solves the game up to an accuracy of $O(T^{-1/2})$ using $T$ calls to the optimization oracle. An important feature of our algorithm is that it is highly parallelizable and requires only $O(T^{1/2})$ iterations, with each iteration making $O(T^{1/2})$ parallel calls to the optimization oracle.
Author Information
Arun Suggala (Carnegie Mellon University)
Praneeth Netrapalli (Microsoft Research)
More from the Same Authors
-
2021 Poster: Boosted CVaR Classification »
Runtian Zhai · Chen Dan · Arun Suggala · J. Zico Kolter · Pradeep Ravikumar -
2020 Poster: Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method »
Kiran Thekumparampil · Prateek Jain · Praneeth Netrapalli · Sewoong Oh -
2020 Spotlight: Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method »
Kiran Thekumparampil · Prateek Jain · Praneeth Netrapalli · Sewoong Oh -
2020 Poster: The Pitfalls of Simplicity Bias in Neural Networks »
Harshay Shah · Kaustav Tamuly · Aditi Raghunathan · Prateek Jain · Praneeth Netrapalli -
2020 Poster: Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms »
Dheeraj Nagaraj · Xian Wu · Guy Bresler · Prateek Jain · Praneeth Netrapalli -
2020 Spotlight: Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms »
Dheeraj Nagaraj · Xian Wu · Guy Bresler · Prateek Jain · Praneeth Netrapalli -
2020 Poster: Generalized Boosting »
Arun Suggala · Bingbin Liu · Pradeep Ravikumar -
2020 Poster: MOReL: Model-Based Offline Reinforcement Learning »
Rahul Kidambi · Aravind Rajeswaran · Praneeth Netrapalli · Thorsten Joachims -
2019 : Lunch Break and Posters »
Xingyou Song · Elad Hoffer · Wei-Cheng Chang · Jeremy Cohen · Jyoti Islam · Yaniv Blumenfeld · Andreas Madsen · Jonathan Frankle · Sebastian Goldt · Satrajit Chatterjee · Abhishek Panigrahi · Alex Renda · Brian Bartoldson · Israel Birhane · Aristide Baratin · Niladri Chatterji · Roman Novak · Jessica Forde · YiDing Jiang · Yilun Du · Linara Adilova · Michael Kamp · Berry Weinstein · Itay Hubara · Tal Ben-Nun · Torsten Hoefler · Daniel Soudry · Hsiang-Fu Yu · Kai Zhong · Yiming Yang · Inderjit Dhillon · Jaime Carbonell · Yanqing Zhang · Dar Gilboa · Johannes Brandstetter · Alexander R Johansen · Gintare Karolina Dziugaite · Raghav Somani · Ari Morcos · Freddie Kalaitzis · Hanie Sedghi · Lechao Xiao · John Zech · Muqiao Yang · Simran Kaur · Qianli Ma · Yao-Hung Hubert Tsai · Ruslan Salakhutdinov · Sho Yaida · Zachary Lipton · Daniel Roy · Michael Carbin · Florent Krzakala · Lenka Zdeborová · Guy Gur-Ari · Ethan Dyer · Dilip Krishnan · Hossein Mobahi · Samy Bengio · Behnam Neyshabur · Praneeth Netrapalli · Kris Sankaran · Julien Cornebise · Yoshua Bengio · Vincent Michalski · Samira Ebrahimi Kahou · Md Rifat Arefin · Jiri Hron · Jaehoon Lee · Jascha Sohl-Dickstein · Samuel Schoenholz · David Schwab · Dongyu Li · Sang Keun Choe · Henning Petzka · Ashish Verma · Zhichao Lin · Cristian Sminchisescu -
2019 : Contributed talk: What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? »
Praneeth Netrapalli -
2019 Poster: On the (In)fidelity and Sensitivity of Explanations »
Chih-Kuan Yeh · Cheng-Yu Hsieh · Arun Suggala · David Inouye · Pradeep Ravikumar -
2019 Poster: Efficient Algorithms for Smooth Minimax Optimization »
Kiran Thekumparampil · Prateek Jain · Praneeth Netrapalli · Sewoong Oh -
2019 Poster: The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares »
Rong Ge · Sham Kakade · Rahul Kidambi · Praneeth Netrapalli -
2018 Poster: Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds »
Raghav Somani · Chirag Gupta · Prateek Jain · Praneeth Netrapalli -
2018 Spotlight: Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds »
Raghav Somani · Chirag Gupta · Prateek Jain · Praneeth Netrapalli -
2018 Poster: Connecting Optimization and Regularization Paths »
Arun Suggala · Adarsh Prasad · Pradeep Ravikumar -
2017 : Posters »
Reihaneh Rabbany · Tianxi Li · Jacob Carroll · Yin Cheng Ng · Xueyu Mao · Alexandre Hollocou · Jeric Briones · James Atwood · John Santerre · Natalie Klein · Pranamesh Chakraborty · Zahra Razaee · Chandan Singh · Arun Suggala · Beilun Wang · Andrew R. Lawrence · Aditya Grover · FARSHAD HARIRCHI · radhika arava · Qing Zhou · Takatomi Kubo · Josue Orellana · Govinda Kamath · Vivek Kumar Bagaria -
2017 : The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities »
Arun Suggala -
2017 Poster: The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities »
Arun Suggala · Mladen Kolar · Pradeep Ravikumar