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Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling

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Ageing and reinforcement learning

Michael Herzog, Wei-Hsiang Lin

Learning to achieve one’s goal in a complex environment is a complicated task. In reinforcement learning (RL) tasks, an agent interacts with the environment to learn optimal actions. In humans, striatal areas are strongly involved in these tasks. During ag ...

2021

Multikernel Regression with Sparsity Constraint

Michaël Unser, Shayan Aziznejad

In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised l ...

2021

The Multi-species Mean-Field Spin-Glass on the Nishimori Line

Emanuele Mingione, Diego Alberici

In this paper we study a multi-species disordered model on the Nishimori line. The typical properties of this line, a set of identities and inequalities among correlation functions, allow us to prove the replica symmetry i.e. the concentration of the order ...

SPRINGER2021

On the Influence of Bias-Correction on Distributed Stochastic Optimization

Ali H. Sayed, Bicheng Ying, Kun Yuan, Sulaiman A S A E Alghunaim

Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Volkan Cevher

Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been in the limelight because of many recent breakthroughs in artificial intelligence, including defeating humans in games (e.g., chess, Go, StarCraft), self-driving cars, s ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Variance-Reduced Stochastic Learning Under Random Reshuffling

Ali H. Sayed, Bicheng Ying, Kun Yuan

Several useful variance-reduced stochastic gradient algorithms, such as SVRG, SAGA, Finito, and SAG, have been proposed to minimize empirical risks with linear convergence properties to the exact minimizer. The existing convergence results assume uniform d ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Distributed Learning over Networks under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This work presents and studies a distributed algorithm for solving optimization problems over networks where agents have individual costs to minimize subject to subspace constraints that require the minimizers across the network to lie in a low-dimensional ...

IEEE2019

On the Performance of Exact Diffusion over Adaptive Networks

Ali H. Sayed, Bicheng Ying, Kun Yuan, Sulaiman A S A E Alghunaim

Various bias-correction methods such as EXTRA, DIGing, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact solution under prop ...

IEEE2019

Decentralized Consensus Optimization With Asynchrony and Delays

Ali H. Sayed, Kun Yuan, Tianyu Wu

We propose an asynchronous, decentralized algorithm for consensus optimization. The algorithm runs over a network in which the agents communicate with their neighbors and perform local computation. In the proposed algorithm, each agent can compute and comm ...

2018

Tomography Of Adaptive Multi-Agent Networks Under Limited Observation

Ali H. Sayed

This work studies the problem of inferring from streaming data whether an agent is directly influenced by another agent over an adaptive network of interacting agents. Agent i influences agent j if they are connected, and if agent j uses the information fr ...

IEEE2018