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03/01/2023
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation
Nash Q-learning may be considered one of the first and most known algori...
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09/29/2022
Restricted Strong Convexity of Deep Learning Models with Smooth Activations
We consider the problem of optimization of deep learning models with smo...
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06/07/2022
Discrete State-Action Abstraction via the Successor Representation
When reinforcement learning is applied with sparse rewards, agents must ...
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05/31/2022
One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning
While parallelism has been extensively used in Reinforcement Learning (R...
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01/28/2022
Family-wise error rate control in Gaussian graphical model selection via Distributionally Robust Optimization
Recently, a special case of precision matrix estimation based on a distr...
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05/18/2021
A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows
Much recent interest has focused on the design of optimization algorithm...
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12/15/2020
Distributed Wasserstein Barycenters via Displacement Interpolation
Consider a multi-agent system whereby each agent has an initial probabil...
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03/27/2020
Distributed and time-varying primal-dual dynamics via contraction analysis
In this paper, we provide a holistic analysis of the primal-dual dynamic...
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01/14/2020
Hierarchical Network Formation Games
We propose a novel network formation game that explains the emergence of...
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05/22/2019