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Books and Theses
- 2019
[b1]Stefan Vlaski:
Distributed Stochastic Optimization in Non-Differentiable and Non-Convex Environments. University of California, Los Angeles, USA, 2019
Journal Articles
- 2023
- [j20]Elsa Rizk
, Stefan Vlaski, Ali H. Sayed:
Privatized graph federated learning. EURASIP J. Adv. Signal Process. 2023(1): 87 (2023) - [j19]Stefan Vlaski, Soummya Kar, Ali H. Sayed, José M. F. Moura:
Networked Signal and Information Processing: Learning by multiagent systems. IEEE Signal Process. Mag. 40(5): 92-105 (2023) - [j18]Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, Ali H. Sayed:
Learning From Heterogeneous Data Based on Social Interactions Over Graphs. IEEE Trans. Inf. Theory 69(5): 3347-3371 (2023) - [j17]Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Self-Aware Social Learning Over Graphs. IEEE Trans. Inf. Theory 69(8): 5299-5317 (2023) - [j16]Ping Hu, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Optimal Aggregation Strategies for Social Learning Over Graphs. IEEE Trans. Inf. Theory 69(9): 6048-6070 (2023) - [j15]Roula Nassif, Stefan Vlaski, Marco Carpentiero, Vincenzo Matta, Marc Antonini, Ali H. Sayed:
Quantization for Decentralized Learning Under Subspace Constraints. IEEE Trans. Signal Process. 71: 2320-2335 (2023) - [j14]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Enforcing Privacy in Distributed Learning With Performance Guarantees. IEEE Trans. Signal Process. 71: 3385-3398 (2023) - 2022
- [j13]Stefan Vlaski, Lieven Vandenberghe, Ali H. Sayed:
Regularized Diffusion Adaptation via Conjugate Smoothing. IEEE Trans. Autom. Control. 67(5): 2343-2358 (2022) - [j12]Stefan Vlaski, Ali H. Sayed:
Second-Order Guarantees of Stochastic Gradient Descent in Nonconvex Optimization. IEEE Trans. Autom. Control. 67(12): 6489-6504 (2022) - [j11]Valentina Shumovskaia, Konstantinos Ntemos, Stefan Vlaski, Ali H. Sayed:
Explainability and Graph Learning From Social Interactions. IEEE Trans. Signal Inf. Process. over Networks 8: 946-959 (2022) - [j10]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Federated Learning Under Importance Sampling. IEEE Trans. Signal Process. 70: 5381-5396 (2022) - 2021
- [j9]Stefan Vlaski, Ali H. Sayed:
Distributed Learning in Non-Convex Environments - Part I: Agreement at a Linear Rate. IEEE Trans. Signal Process. 69: 1242-1256 (2021) - [j8]Stefan Vlaski, Ali H. Sayed:
Distributed Learning in Non-Convex Environments - Part II: Polynomial Escape From Saddle-Points. IEEE Trans. Signal Process. 69: 1257-1270 (2021) - 2020
- [j7]Stefan Vlaski, Ali H. Sayed:
Second-order guarantees in centralized, federated and decentralized nonconvex optimization. Commun. Inf. Syst. 20(3): 353-388 (2020) - [j6]Stefan Vlaski, Elsa Rizk, Ali H. Sayed:
Tracking Performance of Online Stochastic Learners. IEEE Signal Process. Lett. 27: 1385-1389 (2020) - [j5]Roula Nassif, Stefan Vlaski, Cédric Richard, Jie Chen, Ali H. Sayed:
Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning. IEEE Signal Process. Mag. 37(3): 14-25 (2020) - [j4]Roula Nassif, Stefan Vlaski, Ali H. Sayed:
Adaptation and Learning Over Networks Under Subspace Constraints - Part I: Stability Analysis. IEEE Trans. Signal Process. 68: 1346-1360 (2020) - [j3]Roula Nassif, Stefan Vlaski, Ali H. Sayed:
Adaptation and Learning Over Networks Under Subspace Constraints - Part II: Performance Analysis. IEEE Trans. Signal Process. 68: 2948-2962 (2020) - 2019
- [j2]Roula Nassif, Stefan Vlaski, Cédric Richard, Ali H. Sayed:
A Regularization Framework for Learning Over Multitask Graphs. IEEE Signal Process. Lett. 26(2): 297-301 (2019) - [j1]Bicheng Ying, Kun Yuan, Stefan Vlaski, Ali H. Sayed:
Stochastic Learning Under Random Reshuffling With Constant Step-Sizes. IEEE Trans. Signal Process. 67(2): 474-489 (2019)
Conference and Workshop Papers
- 2024
- [c44]Stefan Vlaski, Roula Nassif:
Nonconvex Multitask Learning Over Networks. EUSIPCO 2024: 992-996 - [c43]Aaron Fainman, Stefan Vlaski:
Learned Finite-Time Consensus for Distributed Optimization. EUSIPCO 2024: 1047-1051 - [c42]Roula Nassif, Soummya Kar, Stefan Vlaski:
Learning Dynamics of Low-Precision Clipped SGD with Momentum. ICASSP 2024: 6075-6079 - [c41]Roula Nassif, Stefan Vlaski, Marco Carpentiero, Vincenzo Matta, Ali H. Sayed:
Differential Error Feedback for Communication-Efficient Decentralized Optimization. SAM 2024: 1-5 - [c40]Marco Carpentiero, Vincenzo Matta, Stefan Vlaski, Ali H. Sayed:
Decentralized Fusion of Experts Over Networks. MLSP 2024: 1-6 - [c39]Roula Nassif, Marco Carpentiero, Stefan Vlaski, Vincenzo Matta, Ali H. Sayed:
Matching centralized learning performance via compressed decentralized learning with error feedback. SPAWC 2024: 431-435 - 2023
- [c38]Shreya Wadehra, Roula Nassif, Stefan Vlaski:
Exact Subspace Diffusion for Decentralized Multitask Learning. CDC 2023: 6172-6179 - [c37]Ying Cao, Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Multi-Agent Adversarial Training Using Diffusion Learning. ICASSP 2023: 1-5 - [c36]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Local Graph-Homomorphic Processing for Privatized Distributed Systems. ICASSP 2023: 1-5 - [c35]Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir:
Robust M-Estimation Based Distributed Expectation Maximization Algorithm with Robust Aggregation. ICASSP 2023: 1-5 - [c34]Chutian Wang, Stefan Vlaski:
Robust Network Topologies for Distributed Learning. ICASSP 2023: 1-5 - [c33]Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir:
Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization. DSP 2023: 1-5 - 2022
- [c32]Sofia Jegnell, Stefan Vlaski:
Distributed Relatively Smooth Optimization. CDC 2022: 6511-6517 - [c31]Stefan Vlaski, Christian A. Schroth, Michael Muma, Abdelhak M. Zoubir:
ROBUST AND EFFICIENT AGGREGATION FOR DISTRIBUTED LEARNING. EUSIPCO 2022: 817-821 - [c30]Roula Nassif, Stefan Vlaski, Marc Antonini, Marco Carpentiero, Vincenzo Matta, Ali H. Sayed:
Finite Bit Quantization for Decentralized Learning Under Subspace Constraints. EUSIPCO 2022: 1851-1855 - [c29]Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Social Learning with Disparate Hypotheses. EUSIPCO 2022: 2171-2175 - [c28]Roula Nassif, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Decentralized Learning in the Presence of Low-Rank Noise. ICASSP 2022: 5667-5671 - [c27]Ping Hu, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Optimal Combination Policies for Adaptive Social Learning. ICASSP 2022: 5842-5846 - 2021
- [c26]Valentina Shumovskaia, Konstantinos Ntemos, Stefan Vlaski, Ali H. Sayed:
Online Graph Learning from Social Interactions. ACSCC 2021: 1263-1267 - [c25]Mert Kayaalp, Stefan Vlaski, Ali H. Sayed:
Distributed Meta-Learning with Networked Agents. EUSIPCO 2021: 1361-1365 - [c24]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Optimal Importance Sampling for Federated Learning. ICASSP 2021: 3095-3099 - [c23]Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, Ali H. Sayed:
Network Classifiers Based on Social Learning. ICASSP 2021: 5185-5189 - [c22]Y. Efe Erginbas, Stefan Vlaski, Ali H. Sayed:
Gramian-Based Adaptive Combination Policies for Diffusion Learning Over Networks. ICASSP 2021: 5215-5219 - [c21]Stefan Vlaski, Ali H. Sayed:
Graph-Homomorphic Perturbations for Private Decentralized Learning. ICASSP 2021: 5240-5244 - [c20]Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Social Learning Under Inferential Attacks. ICASSP 2021: 5479-5483 - [c19]Stefan Vlaski, Ali H. Sayed:
Competing Adaptive Networks. SSP 2021: 71-75 - 2020
- [c18]Stefan Vlaski, Elsa Rizk, Ali H. Sayed:
Second-Order Guarantees in Federated Learning. ACSSC 2020: 915-922 - [c17]Stefan Vlaski, Ali H. Sayed:
Linear Speedup in Saddle-Point Escape for Decentralized Non-Convex Optimization. ICASSP 2020: 8589-8593 - [c16]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Dynamic Federated Learning. SPAWC 2020: 1-5 - 2019
- [c15]Roula Nassif, Stefan Vlaski, Ali H. Sayed:
Distributed Learning over Networks under Subspace Constraints. ACSSC 2019: 194-198 - [c14]Stefan Vlaski, Ali H. Sayed:
Polynomial Escape-Time from Saddle Points in Distributed Non-Convex Optimization. CAMSAP 2019: 171-175 - [c13]Ricardo Merched, Stefan Vlaski, Ali H. Sayed:
Enhanced Diffusion Learning Over Networks. EUSIPCO 2019: 1-5 - [c12]Roula Nassif, Stefan Vlaski, Ali H. Sayed:
Distributed Inference over Networks under Subspace Constraints. ICASSP 2019: 5232-5236 - [c11]Stefan Vlaski, Ali H. Sayed:
Diffusion Learning in Non-convex Environments. ICASSP 2019: 5262-5266 - 2018
- [c10]Stefan Vlaski, Hermina Petric Maretic, Roula Nassif, Pascal Frossard, Ali H. Sayed:
Online Graph Learning from Sequential Data. DSW 2018: 190-194 - [c9]Roula Nassif, Stefan Vlaski, Ali H. Sayed:
Distributed Inference Over Multitask Graphs Under Smoothness. SPAWC 2018: 1-5 - 2017
- [c8]Bicheng Ying, Kun Yuan, Stefan Vlaski, Ali H. Sayed:
On the performance of random reshuffling in stochastic learning. ITA 2017: 1-5 - [c7]Sina Basir-Kazeruni, Stefan Vlaski, Hawraa Salami, Ali H. Sayed, Dejan Markovic:
A blind Adaptive Stimulation Artifact Rejection (ASAR) engine for closed-loop implantable neuromodulation systems. NER 2017: 186-189 - 2016
- [c6]Stefan Vlaski, Bicheng Ying, Ali H. Sayed:
The brain strategy for online learning. GlobalSIP 2016: 1285-1289 - [c5]Stefan Vlaski, Lieven Vandenberghe, Ali H. Sayed:
Diffusion stochastic optimization with non-smooth regularizers. ICASSP 2016: 4149-4153 - [c4]Kun Yuan, Bicheng Ying, Stefan Vlaski, Ali H. Sayed:
Stochastic gradient descent with finite samples sizes. MLSP 2016: 1-6 - 2015
- [c3]Stefan Vlaski, Ali H. Sayed:
Proximal diffusion for stochastic costs with non-differentiable regularizers. ICASSP 2015: 3352-3356 - 2014
- [c2]Stefan Vlaski, Michael Muma, Abdelhak M. Zoubir:
Robust bootstrap methods with an application to geolocation in harsh LOS/NLOS environments. ICASSP 2014: 7988-7992 - [c1]Stefan Vlaski, Abdelhak M. Zoubir:
Robust bootstrap based observation classification for Kalman Filtering in harsh LOS/NLOS environments. SSP 2014: 332-335
Informal and Other Publications
- 2024
- [i44]Roula Nassif, Stefan Vlaski, Marco Carpentiero, Vincenzo Matta, Ali H. Sayed:
Differential error feedback for communication-efficient decentralized learning. CoRR abs/2406.18418 (2024) - [i43]Dongyan Sui, Weichen Cao, Stefan Vlaski, Chun Guan, Siyang Leng:
Non-Bayesian Social Learning with Multiview Observations. CoRR abs/2407.20770 (2024) - [i42]Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir:
Sensitivity Curve Maximization: Attacking Robust Aggregators in Distributed Learning. CoRR abs/2412.17740 (2024) - 2023
- [i41]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Enforcing Privacy in Distributed Learning with Performance Guarantees. CoRR abs/2301.06412 (2023) - [i40]Ying Cao, Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Multi-Agent Adversarial Training Using Diffusion Learning. CoRR abs/2303.01936 (2023) - [i39]Ying Cao, Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Decentralized Adversarial Training over Graphs. CoRR abs/2303.13326 (2023) - [i38]Shreya Wadehra, Roula Nassif, Stefan Vlaski:
Exact Subspace Diffusion for Decentralized Multitask Learning. CoRR abs/2304.07358 (2023) - [i37]Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir:
Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization. CoRR abs/2304.14024 (2023) - 2022
- [i36]Valentina Shumovskaia, Konstantinos Ntemos, Stefan Vlaski, Ali H. Sayed:
Online Graph Learning from Social Interactions. CoRR abs/2203.06007 (2022) - [i35]Ping Hu, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Optimal Aggregation Strategies for Social Learning over Graphs. CoRR abs/2203.07065 (2022) - [i34]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Privatized Graph Federated Learning. CoRR abs/2203.07105 (2022) - [i33]Roula Nassif, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Dencentralized learning in the presence of low-rank noise. CoRR abs/2203.09810 (2022) - [i32]Stefan Vlaski, Christian A. Schroth, Michael Muma, Abdelhak M. Zoubir:
Robust and Efficient Aggregation for Distributed Learning. CoRR abs/2204.00586 (2022) - [i31]Roula Nassif, Stefan Vlaski, Marco Carpentiero, Vincenzo Matta, Marc Antonini, Ali H. Sayed:
Quantization for decentralized learning under subspace constraints. CoRR abs/2209.07821 (2022) - [i30]Stefan Vlaski, Soummya Kar, Ali H. Sayed, José M. F. Moura:
Networked Signal and Information Processing. CoRR abs/2210.13767 (2022) - [i29]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Local Graph-homomorphic Processing for Privatized Distributed Systems. CoRR abs/2210.15414 (2022) - [i28]Mert Kayaalp, Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, Ali H. Sayed:
Distributed Bayesian Learning of Dynamic States. CoRR abs/2212.02565 (2022) - 2021
- [i27]Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Deception in Social Learning. CoRR abs/2103.14729 (2021) - [i26]Stefan Vlaski, Ali H. Sayed:
Competing Adaptive Networks. CoRR abs/2103.15664 (2021) - [i25]Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Self-aware Social Learning over Graphs. CoRR abs/2110.13292 (2021) - [i24]Mert Kayaalp, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Hidden Markov Modeling over Graphs. CoRR abs/2111.13626 (2021) - [i23]Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, Ali H. Sayed:
Learning from Heterogeneous Data Based on Social Interactions over Graphs. CoRR abs/2112.09483 (2021) - 2020
- [i22]Roula Nassif, Stefan Vlaski, Cédric Richard, Jie Chen, Ali H. Sayed:
Multitask learning over graphs. CoRR abs/2001.02112 (2020) - [i21]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Dynamic Federated Learning. CoRR abs/2002.08782 (2020) - [i20]Stefan Vlaski, Ali H. Sayed:
Second-Order Guarantees in Centralized, Federated and Decentralized Nonconvex Optimization. CoRR abs/2003.14366 (2020) - [i19]Stefan Vlaski, Elsa Rizk, Ali H. Sayed:
Tracking Performance of Online Stochastic Learners. CoRR abs/2004.01942 (2020) - [i18]Mert Kayaalp, Stefan Vlaski, Ali H. Sayed:
Dif-MAML: Decentralized Multi-Agent Meta-Learning. CoRR abs/2010.02870 (2020) - [i17]Stefan Vlaski, Ali H. Sayed:
Graph-Homomorphic Perturbations for Private Decentralized Learning. CoRR abs/2010.12288 (2020) - [i16]Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, Ali H. Sayed:
Network Classifiers Based on Social Learning. CoRR abs/2010.12306 (2020) - [i15]Y. Efe Erginbas, Stefan Vlaski, Ali H. Sayed:
Gramian-Based Adaptive Combination Policies for Diffusion Learning over Networks. CoRR abs/2010.13104 (2020) - [i14]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Optimal Importance Sampling for Federated Learning. CoRR abs/2010.13600 (2020) - [i13]Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed:
Social learning under inferential attacks. CoRR abs/2010.13660 (2020) - [i12]Stefan Vlaski, Elsa Rizk, Ali H. Sayed:
Second-Order Guarantees in Federated Learning. CoRR abs/2012.01474 (2020) - [i11]Elsa Rizk, Stefan Vlaski, Ali H. Sayed:
Federated Learning under Importance Sampling. CoRR abs/2012.07383 (2020) - 2019
- [i10]Roula Nassif, Stefan Vlaski, Ali H. Sayed:
Adaptation and learning over networks under subspace constraints. CoRR abs/1905.08750 (2019) - [i9]Roula Nassif, Stefan Vlaski, Ali H. Sayed:
Adaptation and learning over networks under subspace constraints - Part II: Performance Analysis. CoRR abs/1906.12250 (2019) - [i8]Stefan Vlaski, Ali H. Sayed:
Distributed Learning in Non-Convex Environments - Part I: Agreement at a Linear Rate. CoRR abs/1907.01848 (2019) - [i7]Stefan Vlaski, Ali H. Sayed:
Distributed Learning in Non-Convex Environments - Part II: Polynomial Escape from Saddle-Points. CoRR abs/1907.01849 (2019) - [i6]Stefan Vlaski, Ali H. Sayed:
Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex Optimization. CoRR abs/1908.07023 (2019) - [i5]Stefan Vlaski, Lieven Vandenberghe, Ali H. Sayed:
Regularized Diffusion Adaptation via Conjugate Smoothing. CoRR abs/1909.09417 (2019) - [i4]Stefan Vlaski, Ali H. Sayed:
Linear Speedup in Saddle-Point Escape for Decentralized Non-Convex Optimization. CoRR abs/1910.13852 (2019) - 2018
- [i3]Bicheng Ying, Kun Yuan, Stefan Vlaski, Ali H. Sayed:
Stochastic Learning under Random Reshuffling. CoRR abs/1803.07964 (2018) - [i2]Roula Nassif, Stefan Vlaski, Ali H. Sayed:
Learning over Multitask Graphs - Part I: Stability Analysis. CoRR abs/1805.08535 (2018) - [i1]Roula Nassif, Stefan Vlaski, Ali H. Sayed:
Learning over Multitask Graphs - Part II: Performance Analysis. CoRR abs/1805.08547 (2018)
Coauthor Index
[j20] [j19] [j18] [j17] [j16] [j15] [j14] [j13] [j12] [j11] [j10] [j9] [j8] [j7] [j6] [j5] [j4] [j3] [j2] [j1] [c41] [c40] [c39] [c37] [c36] [c30] [c29] [c28] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [c16] [c15] [c14] [c13] [c12] [c11] [c10] [c9] [c8] [c7] [c6] [c5] [c4] [c3] [i44] [i41] [i40] [i39] [i36] [i35] [i34] [i33] [i31] [i30] [i29] [i28] [i27] [i26] [i25] [i24] [i23] [i22] [i21] [i20] [i19] [i18] [i17] [i16] [i15] [i14] [i13] [i12] [i11] [i10] [i9] [i8] [i7] [i6] [i5] [i4] [i3] [i2] [i1]
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