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01/10/2023
Min-Max Optimization Made Simple: Approximating the Proximal Point Method via Contraction Maps
In this paper we present a first-order method that admits near-optimal c...
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10/13/2020
Regret minimization in stochastic non-convex learning via a proximal-gradient approach
Motivated by applications in machine learning and operations research, w...
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07/02/2020
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch
We study the inverse reinforcement learning (IRL) problem under the tran...
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06/19/2020
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
This paper analyzes the trajectories of stochastic gradient descent (SGD...
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06/16/2020
The limits of min-max optimization algorithms: convergence to spurious non-critical sets
Compared to minimization problems, the min-max landscape in machine lear...
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03/21/2020
A new regret analysis for Adam-type algorithms
In this paper, we focus on a theory-practice gap for Adam and its varian...
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01/29/2019
Stochastic Conditional Gradient Method for Composite Convex Minimization
In this paper, we propose the first practical algorithm to minimize stoc...
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05/31/2017
