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01/30/2023
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
We study a game between autobidding algorithms that compete in an online...
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12/28/2022
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
Offline reinforcement learning (RL) concerns pursuing an optimal policy ...
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10/23/2022
Symmetric (Optimistic) Natural Policy Gradient for Multi-agent Learning with Parameter Convergence
Multi-agent interactions are increasingly important in the context of re...
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06/09/2022
What is a Good Metric to Study Generalization of Minimax Learners?
Minimax optimization has served as the backbone of many machine learning...
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10/26/2020
Tight last-iterate convergence rates for no-regret learning in multi-player games
We study the question of obtaining last-iterate convergence rates for no...
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02/13/2020
An Optimal Multistage Stochastic Gradient Method for Minimax Problems
In this paper, we study the minimax optimization problem in the smooth a...
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01/31/2020
Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems
In this paper we study the smooth convex-concave saddle point problem. S...
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10/31/2019
A Decentralized Proximal Point-type Method for Saddle Point Problems
In this paper, we focus on solving a class of constrained non-convex non...
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06/03/2019
Proximal Point Approximations Achieving a Convergence Rate of O(1/k) for Smooth Convex-Concave Saddle Point Problems: Optimistic Gradient and Extra-gradient Methods
In this paper we analyze the iteration complexity of the optimistic grad...
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01/24/2019
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach
We consider solving convex-concave saddle point problems. We focus on tw...
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12/23/2017
Persistence of the Jordan center in Random Growing Trees
The Jordan center of a graph is defined as a vertex whose maximum distan...
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10/28/2017
