1 citations · 1 across the 11 of their papers we have counts for
11 papers
Decentralized Multitask Learning over Learned Task Graphs
Zirui Wan, Stefan Vlaski
This paper investigates decentralized multitask learning over networks when the underlying task relationships are unknown. While existing graph-regularized multitask frameworks typ…
Topology-Independent Robustness of the Weighted Mean under Label Poisoning Attacks in Heterogeneous Decentralized Learning
Jie Peng, Weiyu Li, Stefan Vlaski +1
Robustness to malicious attacks is crucial for practical decentralized signal processing and machine learning systems. A typical example of such attacks is label poisoning, meaning…
Decentralized AI Service Placement, Selection and Routing in Mobile Networks
Jinkun Zhang, Stefan Vlaski, Kin Leung
The rapid development and usage of large-scale AI models by mobile users will dominate the traffic load in future communication networks. The advent of AI technology also facilitat…
Multitask Learning with Learned Task Relationships
Zirui Wan, Stefan Vlaski
Classical consensus-based strategies for federated and decentralized learning are statistically suboptimal in the presence of heterogeneous local data or task distributions. As a r…
Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs
Xue Xian Zheng, Weihang Liu, Xin Lou +2
This paper introduces an innovative error feedback framework designed to mitigate quantization noise in distributed graph filtering, where communications are constrained to quantiz…
Alpha-Beta HMM: Interpretable Low-Parameter Hidden Markov Filtering in Dynamic Environments
Dongyan Sui, Haotian Pu, Yulin Shao +2
Practical online inference in dynamic environments requires a lightweight filtering mechanism that remains adaptive to state changes while retaining reliable information from past…
Scalable Min-Max Optimization via Primal-Dual Exact Pareto Optimization
Sangwoo Park, Stefan Vlaski, Lajos Hanzo
In multi-objective optimization, minimizing the worst objective can be preferable to minimizing the average objective, as this ensures improved fairness across objectives. Due to t…
Convergence Analysis of alpha-SVRG under Strong Convexity
Sean Xiao, Sangwoo Park, Stefan Vlaski
Stochastic first-order methods for empirical risk minimization employ gradient approximations based on sampled data in lieu of exact gradients. Such constructions introduce noise i…
Decentralized Hidden Markov Modeling with Equal Exit Probabilities
Dongyan Sui, Haitian Zheng, Siyang Leng +1
Social learning strategies enable agents to infer the underlying true state of nature in a distributed manner by receiving private environmental signals and exchanging beliefs with…
Mean Aggregator is More Robust than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data
Jie Peng, Weiyu Li, Stefan Vlaski +1
Robustness to malicious attacks is of paramount importance for distributed learning. Existing works usually consider the classical Byzantine attacks model, which assumes that some…
Towards Scalable Semidefinite Programming: Optimal Metric ADMM with A Worst-case Performance Guarantee
Yifan Ran, Stefan Vlaski, Wei Dai
Despite the numerous uses of semidefinite programming (SDP) and its universal solvability via interior point methods (IPMs), it is rarely applied to practical large-scale problems.…