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Stefan Vlaski

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Related publications (40)

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A Regularization Framework for Learning Over Multitask Graphs

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This letter proposes a general regularization framework for inference over multitask networks. The optimization approach relies on minimizing a global cost consisting of the aggregate sum of individual costs regularized by a term that allows to incorporate ...

IEEE Institute of Electrical and Electronics Engineers2019

Distributed Inference over Multitask Graphs under Smoothness

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This paper formulates a multitask optimization problem where agents in the network have individual objectives to meet, or individual parameter vectors to estimate, subject to a smoothness condition over the graph. The smoothness requirement softens the tra ...

IEEE2018

Distributed Learning in Non-Convex Environments-Part I: Agreement at a Linear Rate

Ali H. Sayed, Stefan Vlaski

Driven by the need to solve increasingly complex optimization problems in signal processing and machine learning, there has been increasing interest in understanding the behavior of gradient-descent algorithms in non-convex environments. Most available wor ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

Ali H. Sayed, Stefan Vlaski, Roula Nassif

The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is an appro ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Social Learning with Disparate Hypotheses

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos

In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents. In this setup, each agent receives data that might be generated from a different hypothesis (or state) than the data other agents receive. In c ...

IEEE2022

Gramian-Based Adaptive Combination Policies For Diffusion Learning Over Networks

Ali H. Sayed, Stefan Vlaski

This paper presents an adaptive combination strategy for distributed learning over diffusion networks. Since learning relies on the collaborative processing of the stochastic information at the dispersed agents, the overall performance can be improved by d ...

IEEE2021

Graph-Homomorphic Perturbations For Private Decentralized Learning

Ali H. Sayed, Stefan Vlaski

Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information through repeated local exchanges of intermediate estimates. Such structures are particularly appealing in situations where agents may be hesitant to shar ...

IEEE2021

Linear Speedup In Saddle-Point Escape For Decentralized Non-Convex Optimization

Ali H. Sayed, Stefan Vlaski

Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to no ...

IEEE2020

Finite Bit Quantization For Decentralized Learning Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

In this paper, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained form ...

IEEE2022

Regularized Diffusion Adaptation via Conjugate Smoothing

Ali H. Sayed, Stefan Vlaski

The purpose of this article is to develop and study a decentralized strategy for Pareto optimization of an aggregate cost consisting of regularized risks. Each risk is modeled as the expectation of some loss function with unknown probability distribution, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Enforcing Privacy in Distributed Learning With Performance Guarantees

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...

Ieee-Inst Electrical Electronics Engineers Inc2023

Distributed Meta-Learning with Networked Agents

Ali H. Sayed, Stefan Vlaski, Mert Kayaalp

Meta-learning aims to improve efficiency of learning new tasks by exploiting the inductive biases obtained from related tasks. Previous works consider centralized or federated architectures that rely on central processors, whereas, in this paper, we propos ...

EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021

Distributed Inference Over Networks Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that enforce the objectives across the network to lie in a low-dimensiona ...

IEEE2019

Stochastic Learning Under Random Reshuffling With Constant Step-Sizes

Ali H. Sayed, Stefan Vlaski, Kun Yuan, Bicheng Ying

In empirical risk optimization, it has been observed that stochastic gradient implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data uniformly. Recent works have pursued ju ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Optimal Combination Policies For Adaptive Social Learning

Stefan Vlaski, Virginia Bordignon, Ping Hu

This paper investigates the effect of combination policies on the performance of adaptive social learning in non-stationary environments. By analyzing the relation between the error probability and the underlying graph topology, we prove that in the slow a ...

IEEE2022

Social Learning Under Inferential Attacks

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries a ...

IEEE2021

Networked Signal and Information Processing: Learning by multiagent systems

Ali H. Sayed, Stefan Vlaski

This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Tracking Performance of Online Stochastic Learners

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

The utilization of online stochastic algorithms is popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and process data in large batches. When a constant step-size is used, these algorithms ...

IEEE Institute of Electrical and Electronics Engineers2020

Hidden Markov Modeling Over Graphs

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Mert Kayaalp

This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference fro ...

IEEE2022

Online Graph Learning from Sequential Data

Pascal Frossard, Ali H. Sayed, Stefan Vlaski, Roula Nassif, Hermina Petric Maretic

2018