Stefan Vlaski
Associate Professor
Studies distributed optimisation and learning over networks of agents, at the intersection of machine learning theory and network science.
Stefan Vlaski's lab at Imperial works on learning theory & optimization and distributed, cloud & edge systems. 38 papers since 2023, 7 at top venues, mostly ICASSP and ICC. 2 PhD students. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 18%
- PhD students
- 2
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 3 · collecting
- 9 papers2025 · T1 4
- 9 papers2024 · T1 0
- 13 papers2023 · T1 0
T1 venuesOtherVenues: ICASSP, ICC, JMLR
Research focuspaper titles that use each topic's words
- Learning theory & optimization27 papers
- Distributed, cloud & edge systems8 papers
- studies distributed0 papers
- network science0 papers
- optimisation0 papers
Paper list
From the lab homepage.
20267 papers
- ICCDecentralized AI Service Placement, Selection and Routing in Mobile Networks (opens in a new tab)
- ICASSP (T1)Graph-Aware Learning Rates for Decentralized Optimization (opens in a new tab)
- ICASSP (T1)Multitask Learning with Learned Task Relationships (opens in a new tab)
- IEEE Transactions on Signal ProcessingOn the Convergence of Decentralized Stochastic Gradient-Tracking with Finite-Time Consensus (opens in a new tab)
- ICASSP (T1)Parameter Adaptation in Hidden Markov Models with Equal Exit Probabilities (opens in a new tab)
- IEEE Transactions on Signal ProcessingQuantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs (opens in a new tab)
- IEEE Transactions on Signal and Information Processing over NetworksTopology-Independent Learning Error of the Weighted Mean under Label Poisoning Attacks in Heterogeneous Decentralized Learning (opens in a new tab)
20259 papers
- ICASSP (T1)Communication-Efficient Exact Diffusion for Decentralized Learning (opens in a new tab)
- ICASSP (T1)Convergence Analysis of alpha-SVRG under Strong Convexity (opens in a new tab)
- IEEE Transactions on Information TheoryDecentralized Adversarial Training over Graphs
- EUSIPCODecentralized Hidden Markov Modeling with Equal Exit Probabilities (opens in a new tab)
- ICASSP (T1)Deep-Relative-Trust-Based Diffusion for Decentralized Deep Learning (opens in a new tab)
- IEEE Transactions on Signal ProcessingDifferential Error Feedback for Communication-Efficient Decentralized Learning
- JMLR (T1)Mean Aggregator Is More Robust Than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data (opens in a new tab)
- In Proc. of Asilomar Conference on Signals, Systems, and ComputersPareto-Optimal Multi-Objective Learning over Graphs is Capable of Attaining Perfect Resource-Fairness: A Primal-Dual Min-Max Optimization Approach (opens in a new tab)
- EUSIPCOScalable Min-Max Optimization via Primal-Dual Exact Pareto Optimization (opens in a new tab)
20249 papers
- MLSPDecentralized Fusion of Experts Over Networks (opens in a new tab)
- In Proc. of Asilomar Conference on Signals, Systems, and ComputersDecentralized Learning with Approximate Finite-Time Consensus (opens in a new tab)
- In Proc. of IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), DecDifferential Error Feedback for Communication-Efficient Decentralized Optimization (opens in a new tab)
- In Proc. of European Signal Processing Conference (EUSIPCO), DecLearned Finite-Time Consensus for Distributed Optimization (opens in a new tab)
- In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), DecLearning Dynamics of Low-Precision Clipped SGD with Momentum (opens in a new tab)
- SPAWCMatching centralized learning performance via compressed decentralized learning with error feedback (opens in a new tab)
- Proc. of IEEE Conference on Decision and Control (CDC), DecNon-Bayesian Social Learning with Multiview Observations
- EUSIPCONonconvex Multitask Learning Over Networks (opens in a new tab)
- In Proc. of Asilomar Conference on Signals, Systems, and ComputersProximally Regularized Multitask Learning over Networks (opens in a new tab)
202313 papers
- In Proc. of International Conference on Digital Signal Processing (DSP), DecAttacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization (opens in a new tab)
- IEEE Transactions on Signal Processing, DecEnforcing Privacy in Distributed Learning With Performance Guarantees (opens in a new tab)
- In Proc. of IEEE Conference on Decision and Control (CDC), DecExact Subspace Diffusion for Decentralized Multitask Learning (opens in a new tab)
- IEEE Transactions on Information Theory, DecLearning from Heterogeneous Data Based on Social Interactions over Graphs (opens in a new tab)
- In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), DecLocal Graph-Homomorphic Processing for Privatized Distributed Systems (opens in a new tab)
- In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), DecMulti-Agent Adversarial Training Using Diffusion Learning (opens in a new tab)
- IEEE Signal Processing Magazine, DecNetworked Signal and Information Processing: Learning by Multiagent Systems (opens in a new tab)
- IEEE Transactions on Information Theory, DecOptimal Aggregation Strategies for Social Learning Over Graphs (opens in a new tab)
- EURASIP Journal on Advances in Signal Processing, DecPrivatized graph federated learning (opens in a new tab)
- IEEE Transactions on Signal Processing, DecQuantization for Decentralized Learning Under Subspace Constraints (opens in a new tab)
- In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), DecRobust M-Estimation Based Distributed Expectation Maximization Algorithm with Robust Aggregation (opens in a new tab)
- In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), DecRobust Network Topologies for Distributed Learning (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to Imperial2027 (Apr/Oct)
Computing PhD
- Round
- Round 2 (1 Dec 2026)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Confirmed on the program page.
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