Robust bootstrap methods with an application to geolocation in harsh LOS/NLOS environments. - Stefan Vlaski; Michael Muma; Abdelhak M. Zoubir (ICASSP, 2014)

Robust bootstrap based observation classification for Kalman Filtering in harsh LOS/NLOS environments. - Stefan Vlaski; Abdelhak M. Zoubir (SSP, 2014)

Proximal diffusion for stochastic costs with non-differentiable regularizers. - Stefan Vlaski; Ali H. Sayed (ICASSP, 2015)

The brain strategy for online learning. - Stefan Vlaski; Bicheng Ying; Ali H. Sayed (GlobalSIP, 2016)

Diffusion stochastic optimization with non-smooth regularizers. - Stefan Vlaski; Lieven Vandenberghe; Ali H. Sayed (ICASSP, 2016)

Stochastic gradient descent with finite samples sizes. - Kun Yuan 001; Bicheng Ying; Stefan Vlaski; Ali H. Sayed (MLSP, 2016)

On the performance of random reshuffling in stochastic learning. - Bicheng Ying; Kun Yuan 001; Stefan Vlaski; Ali H. Sayed (ITA, 2017)

A blind Adaptive Stimulation Artifact Rejection (ASAR) engine for closed-loop implantable neuromodulation systems. - Sina Basir-Kazeruni; Stefan Vlaski; Hawraa Salami; Ali H. Sayed; Dejan Markovic (NER, 2017)

Online Graph Learning from Sequential Data. - Stefan Vlaski; Hermina Petric Maretic; Roula Nassif; Pascal Frossard; Ali H. Sayed (DSW, 2018)

Distributed Inference Over Multitask Graphs Under Smoothness. - Roula Nassif; Stefan Vlaski; Ali H. Sayed (SPAWC, 2018)

Stochastic Learning under Random Reshuffling. - Bicheng Ying; Kun Yuan 001; Stefan Vlaski; Ali H. Sayed (CoRR, 2018)

Learning over Multitask Graphs - Part I: Stability Analysis. - Roula Nassif; Stefan Vlaski; Ali H. Sayed (CoRR, 2018)

Learning over Multitask Graphs - Part II: Performance Analysis. - Roula Nassif; Stefan Vlaski; Ali H. Sayed (CoRR, 2018)

Distributed Learning over Networks under Subspace Constraints. - Roula Nassif; Stefan Vlaski; Ali H. Sayed (ACSSC, 2019)

Polynomial Escape-Time from Saddle Points in Distributed Non-Convex Optimization. - Stefan Vlaski; Ali H. Sayed (CAMSAP, 2019)

Enhanced Diffusion Learning Over Networks. - Ricardo Merched; Stefan Vlaski; Ali H. Sayed (EUSIPCO, 2019)

Distributed Inference over Networks under Subspace Constraints. - Roula Nassif; Stefan Vlaski; Ali H. Sayed (ICASSP, 2019)

Diffusion Learning in Non-convex Environments. - Stefan Vlaski; Ali H. Sayed (ICASSP, 2019)

Adaptation and learning over networks under subspace constraints. - Roula Nassif; Stefan Vlaski; Ali H. Sayed (CoRR, 2019)

Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex Optimization. - Stefan Vlaski; Ali H. Sayed (CoRR, 2019)

A Regularization Framework for Learning Over Multitask Graphs. - Roula Nassif; Stefan Vlaski; Cédric Richard; Ali H. Sayed (IEEE Signal Process. Lett., 2019)

Stochastic Learning Under Random Reshuffling With Constant Step-Sizes. - Bicheng Ying; Kun Yuan 001; Stefan Vlaski; Ali H. Sayed (IEEE Trans. Signal Process., 2019)

Distributed Stochastic Optimization in Non-Differentiable and Non-Convex Environments - Stefan Vlaski (2019)

Second-Order Guarantees in Federated Learning. - Stefan Vlaski; Elsa Rizk; Ali H. Sayed (ACSSC, 2020)

Linear Speedup in Saddle-Point Escape for Decentralized Non-Convex Optimization. - Stefan Vlaski; Ali H. Sayed (ICASSP, 2020)

Dynamic Federated Learning. - Elsa Rizk; Stefan Vlaski; Ali H. Sayed (SPAWC, 2020)

Second-order guarantees in centralized, federated and decentralized nonconvex optimization. - Stefan Vlaski; Ali H. Sayed (Commun. Inf. Syst., 2020)

Multitask learning over graphs. - Roula Nassif; Stefan Vlaski; Cédric Richard; Jie Chen 022; Ali H. Sayed (CoRR, 2020)

Dif-MAML: Decentralized Multi-Agent Meta-Learning. - Mert Kayaalp; Stefan Vlaski; Ali H. Sayed (CoRR, 2020)

Tracking Performance of Online Stochastic Learners. - Stefan Vlaski; Elsa Rizk; Ali H. Sayed (IEEE Signal Process. Lett., 2020)

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning. - Roula Nassif; Stefan Vlaski; Cédric Richard; Jie Chen 022; Ali H. Sayed (IEEE Signal Process. Mag., 2020)

Adaptation and Learning Over Networks Under Subspace Constraints - Part I: Stability Analysis. - Roula Nassif; Stefan Vlaski; Ali H. Sayed (IEEE Trans. Signal Process., 2020)

Adaptation and Learning Over Networks Under Subspace Constraints - Part II: Performance Analysis. - Roula Nassif; Stefan Vlaski; Ali H. Sayed (IEEE Trans. Signal Process., 2020)

Online Graph Learning from Social Interactions. - Valentina Shumovskaia; Konstantinos Ntemos; Stefan Vlaski; Ali H. Sayed (ACSCC, 2021)

Distributed Meta-Learning with Networked Agents. - Mert Kayaalp; Stefan Vlaski; Ali H. Sayed (EUSIPCO, 2021)

Network Classifiers Based on Social Learning. - Virginia Bordignon; Stefan Vlaski; Vincenzo Matta; Ali H. Sayed (ICASSP, 2021)

Gramian-Based Adaptive Combination Policies for Diffusion Learning Over Networks. - Y. Efe Erginbas; Stefan Vlaski; Ali H. Sayed (ICASSP, 2021)

Social Learning Under Inferential Attacks. - Konstantinos Ntemos; Virginia Bordignon; Stefan Vlaski; Ali H. Sayed (ICASSP, 2021)

Optimal Importance Sampling for Federated Learning. - Elsa Rizk; Stefan Vlaski; Ali H. Sayed (ICASSP, 2021)

Graph-Homomorphic Perturbations for Private Decentralized Learning. - Stefan Vlaski; Ali H. Sayed (ICASSP, 2021)

Competing Adaptive Networks. - Stefan Vlaski; Ali H. Sayed (SSP, 2021)

Deception in Social Learning. - Konstantinos Ntemos; Virginia Bordignon; Stefan Vlaski; Ali H. Sayed (CoRR, 2021)

Hidden Markov Modeling over Graphs. - Mert Kayaalp; Virginia Bordignon; Stefan Vlaski; Ali H. Sayed (CoRR, 2021)

Distributed Learning in Non-Convex Environments - Part I: Agreement at a Linear Rate. - Stefan Vlaski; Ali H. Sayed (IEEE Trans. Signal Process., 2021)

Distributed Learning in Non-Convex Environments - Part II: Polynomial Escape From Saddle-Points. - Stefan Vlaski; Ali H. Sayed (IEEE Trans. Signal Process., 2021)

Distributed Relatively Smooth Optimization. - Sofia Jegnell; Stefan Vlaski (CDC, 2022)

Finite Bit Quantization for Decentralized Learning Under Subspace Constraints. - Roula Nassif; Stefan Vlaski; Marc Antonini; Marco Carpentiero; Vincenzo Matta; Ali H. Sayed (EUSIPCO, 2022)

Social Learning with Disparate Hypotheses. - Konstantinos Ntemos; Virginia Bordignon; Stefan Vlaski; Ali H. Sayed (EUSIPCO, 2022)

ROBUST AND EFFICIENT AGGREGATION FOR DISTRIBUTED LEARNING. - Stefan Vlaski; Christian A. Schroth; Michael Muma; Abdelhak M. Zoubir (EUSIPCO, 2022)

Optimal Combination Policies for Adaptive Social Learning. - Ping Hu; Virginia Bordignon; Stefan Vlaski; Ali H. Sayed (ICASSP, 2022)

Decentralized Learning in the Presence of Low-Rank Noise. - Roula Nassif; Virginia Bordignon; Stefan Vlaski; Ali H. Sayed (ICASSP, 2022)

Dencentralized learning in the presence of low-rank noise. - Roula Nassif; Virginia Bordignon; Stefan Vlaski; Ali H. Sayed (CoRR, 2022)

Networked Signal and Information Processing. - Stefan Vlaski; Soummya Kar; Ali H. Sayed; José M. F. Moura (CoRR, 2022)

Distributed Bayesian Learning of Dynamic States. - Mert Kayaalp; Virginia Bordignon; Stefan Vlaski; Vincenzo Matta; Ali H. Sayed (CoRR, 2022)

Second-Order Guarantees of Stochastic Gradient Descent in Nonconvex Optimization. - Stefan Vlaski; Ali H. Sayed (IEEE Trans. Autom. Control., 2022)

Regularized Diffusion Adaptation via Conjugate Smoothing. - Stefan Vlaski; Lieven Vandenberghe; Ali H. Sayed (IEEE Trans. Autom. Control., 2022)

Explainability and Graph Learning From Social Interactions. - Valentina Shumovskaia; Konstantinos Ntemos; Stefan Vlaski; Ali H. Sayed (IEEE Trans. Signal Inf. Process. over Networks, 2022)

Federated Learning Under Importance Sampling. - Elsa Rizk; Stefan Vlaski; Ali H. Sayed (IEEE Trans. Signal Process., 2022)

Exact Subspace Diffusion for Decentralized Multitask Learning. - Shreya Wadehra; Roula Nassif; Stefan Vlaski (CDC, 2023)

Multi-Agent Adversarial Training Using Diffusion Learning. - Ying Cao; Elsa Rizk; Stefan Vlaski; Ali H. Sayed (ICASSP, 2023)

Local Graph-Homomorphic Processing for Privatized Distributed Systems. - Elsa Rizk; Stefan Vlaski; Ali H. Sayed (ICASSP, 2023)

Robust M-Estimation Based Distributed Expectation Maximization Algorithm with Robust Aggregation. - Christian A. Schroth; Stefan Vlaski; Abdelhak M. Zoubir (ICASSP, 2023)

Robust Network Topologies for Distributed Learning. - Chutian Wang; Stefan Vlaski (ICASSP, 2023)

Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization. - Christian A. Schroth; Stefan Vlaski; Abdelhak M. Zoubir (DSP, 2023)

Quantization for Decentralized Learning Under Subspace Constraints. - Roula Nassif; Stefan Vlaski; Marco Carpentiero; Vincenzo Matta; Marc Antonini; Ali H. Sayed (IEEE Trans. Signal Process., 2023)

Enforcing Privacy in Distributed Learning With Performance Guarantees. - Elsa Rizk; Stefan Vlaski; Ali H. Sayed (IEEE Trans. Signal Process., 2023)

Learning From Heterogeneous Data Based on Social Interactions Over Graphs. - Virginia Bordignon; Stefan Vlaski; Vincenzo Matta; Ali H. Sayed (IEEE Trans. Inf. Theory, 2023)

Networked Signal and Information Processing: Learning by multiagent systems. - Stefan Vlaski; Soummya Kar; Ali H. Sayed; José M. F. Moura (IEEE Signal Process. Mag., 2023)

Self-Aware Social Learning Over Graphs. - Konstantinos Ntemos; Virginia Bordignon; Stefan Vlaski; Ali H. Sayed (IEEE Trans. Inf. Theory, 2023)

Optimal Aggregation Strategies for Social Learning Over Graphs. - Ping Hu; Virginia Bordignon; Stefan Vlaski; Ali H. Sayed (IEEE Trans. Inf. Theory, 2023)

Privatized graph federated learning. - Elsa Rizk; Stefan Vlaski; Ali H. Sayed (EURASIP J. Adv. Signal Process., 2023)

Decentralized Learning with Approximate Finite-Time Consensus. - Aaron Fainman; Stefan Vlaski (IEEECONF, 2024)

Proximally Regularized Multitask Learning over Networks. - Stefan Vlaski; Roula Nassif (IEEECONF, 2024)

Non-Bayesian Social Learning with Multiview Observations. - Dongyan Sui; Weichen Cao 001; Stefan Vlaski; Chun Guan; Siyang Leng (CDC, 2024)

Learned Finite-Time Consensus for Distributed Optimization. - Aaron Fainman; Stefan Vlaski (EUSIPCO, 2024)

Nonconvex Multitask Learning Over Networks. - Stefan Vlaski; Roula Nassif (EUSIPCO, 2024)

Learning Dynamics of Low-Precision Clipped SGD with Momentum. - Roula Nassif; Soummya Kar; Stefan Vlaski (ICASSP, 2024)

Differential Error Feedback for Communication-Efficient Decentralized Optimization. - Roula Nassif; Stefan Vlaski; Marco Carpentiero; Vincenzo Matta; Ali H. Sayed (SAM, 2024)

Decentralized Fusion of Experts Over Networks. - Marco Carpentiero; Vincenzo Matta; Stefan Vlaski; Ali H. Sayed (MLSP, 2024)

Matching centralized learning performance via compressed decentralized learning with error feedback. - Roula Nassif; Marco Carpentiero; Stefan Vlaski; Vincenzo Matta; Ali H. Sayed (SPAWC, 2024)

Sensitivity Curve Maximization: Attacking Robust Aggregators in Distributed Learning. - Christian A. Schroth; Stefan Vlaski; Abdelhak M. Zoubir (CoRR, 2024)

Communication-efficient Exact Diffusion for Decentralized Learning. - Gustavo Faia; Stefan Vlaski; Roula Nassif (ICASSP, 2025)

Deep-Relative-Trust-Based Diffusion for Decentralized Deep Learning. - Muyun Li; Aaron Fainman; Stefan Vlaski (ICASSP, 2025)

Convergence Analysis of alpha-SVRG under Strong Convexity. - Sean Xiao; Sangwoo Park; Stefan Vlaski (ICASSP, 2025)

Differential Error Feedback for Communication-Efficient Decentralized Learning. - Roula Nassif; Stefan Vlaski; Marco Carpentiero; Vincenzo Matta; Ali H. Sayed (IEEE Trans. Signal Process., 2025)

Decentralized Hidden Markov Modeling with Equal Exit Probabilities. - Dongyan Sui; Haitian Zheng; Siyang Leng; Stefan Vlaski (CoRR, 2025)

Hidden Markov Model Filtering with Equal Exit Probabilities. - Dongyan Sui; Haotian Pu; Siyang Leng; Stefan Vlaski (CoRR, 2025)

Scalable Min-Max Optimization via Primal-Dual Exact Pareto Optimization. - Sangwoo Park; Stefan Vlaski; Lajos Hanzo (CoRR, 2025)

Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs. - Xue Xian Zheng; Weihang Liu; Xin Lou; Stefan Vlaski; Tareq Y. Al-Naffouri (CoRR, 2025)

Decentralized Adversarial Training Over Graphs. - Ying Cao; Elsa Rizk; Stefan Vlaski; Ali H. Sayed (IEEE Trans. Inf. Theory, 2025)