| 2025 | ACSSC | Pareto-Optimal Multi-Objective Learning over Graphs is Capable of Attaining Perfect Resource-Fairness: A Primal-Dual Min-Max Optimization Approach. | Sangwoo Park, Stefan Vlaski, Lajos Hanzo |
| 2025 | ICASSP | Communication-efficient Exact Diffusion for Decentralized Learning. | Gustavo Faia, Stefan Vlaski, Roula Nassif |
| 2025 | ICASSP | Deep-Relative-Trust-Based Diffusion for Decentralized Deep Learning. | Muyun Li, Aaron Fainman, Stefan Vlaski |
| 2025 | ICASSP | Convergence Analysis of alpha-SVRG under Strong Convexity. | Sean Xiao, Sangwoo Park, Stefan Vlaski |
| 2024 | ACSSC | Decentralized Learning with Approximate Finite-Time Consensus. | Aaron Fainman, Stefan Vlaski |
| 2024 | ACSSC | Proximally Regularized Multitask Learning over Networks. | Stefan Vlaski, Roula Nassif |
| 2024 | ICASSP | Learning Dynamics of Low-Precision Clipped SGD with Momentum. | Roula Nassif, Soummya Kar, Stefan Vlaski |
| 2023 | ICASSP | Multi-Agent Adversarial Training Using Diffusion Learning. | Ying Cao, Elsa Rizk, Stefan Vlaski, Ali H. Sayed |
| 2023 | ICASSP | Local Graph-Homomorphic Processing for Privatized Distributed Systems. | Elsa Rizk, Stefan Vlaski, Ali H. Sayed |
| 2023 | ICASSP | Robust M-Estimation Based Distributed Expectation Maximization Algorithm with Robust Aggregation. | Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir |
| 2023 | ICASSP | Robust Network Topologies for Distributed Learning. | Chutian Wang, Stefan Vlaski |
| 2022 | ICASSP | Optimal Combination Policies for Adaptive Social Learning. | Ping Hu, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed |
| 2022 | ICASSP | Decentralized Learning in the Presence of Low-Rank Noise. | Roula Nassif, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed |
| 2021 | ACSSC | Online Graph Learning from Social Interactions. | Valentina Shumovskaia, Konstantinos Ntemos, Stefan Vlaski, Ali H. Sayed |
| 2021 | ICASSP | Network Classifiers Based on Social Learning. | Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, Ali H. Sayed |
| 2021 | ICASSP | Gramian-Based Adaptive Combination Policies for Diffusion Learning Over Networks. | Y. Efe Erginbas, Stefan Vlaski, Ali H. Sayed |
| 2021 | ICASSP | Social Learning Under Inferential Attacks. | Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed |
| 2021 | ICASSP | Optimal Importance Sampling for Federated Learning. | Elsa Rizk, Stefan Vlaski, Ali H. Sayed |
| 2021 | ICASSP | Graph-Homomorphic Perturbations for Private Decentralized Learning. | Stefan Vlaski, Ali H. Sayed |
| 2020 | ACSSC | Second-Order Guarantees in Federated Learning. | Stefan Vlaski, Elsa Rizk, Ali H. Sayed |
| 2020 | ICASSP | Linear Speedup in Saddle-Point Escape for Decentralized Non-Convex Optimization. | Stefan Vlaski, Ali H. Sayed |
| 2019 | ACSSC | Distributed Learning over Networks under Subspace Constraints. | Roula Nassif, Stefan Vlaski, Ali H. Sayed |
| 2019 | ICASSP | Distributed Inference over Networks under Subspace Constraints. | Roula Nassif, Stefan Vlaski, Ali H. Sayed |
| 2019 | ICASSP | Diffusion Learning in Non-convex Environments. | Stefan Vlaski, Ali H. Sayed |
| 2017 | ITA | On the performance of random reshuffling in stochastic learning. | Bicheng Ying, Kun Yuan, Stefan Vlaski, Ali H. Sayed |
| 2016 | ICASSP | Diffusion stochastic optimization with non-smooth regularizers. | Stefan Vlaski, Lieven Vandenberghe, Ali H. Sayed |
| 2015 | ICASSP | Proximal diffusion for stochastic costs with non-differentiable regularizers. | Stefan Vlaski, Ali H. Sayed |
| 2014 | ICASSP | Robust bootstrap methods with an application to geolocation in harsh LOS/NLOS environments. | Stefan Vlaski, Michael Muma, Abdelhak M. Zoubir |