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20242026
most citedPruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring

2 citations · 2 across the 11 of their papers we have counts for

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cs.LG20262 cited

Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring

Christian Internò, Elena Raponi, Markus Olhofer +5

The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by…

cs.LG2026

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors

Qi Huang, Furong Ye, Ananta Shahane +2

Large Language Models (LLMs) have already been widely adopted for automated algorithm design, demonstrating strong abilities in generating and evolving algorithms across various fi…

cs.LG20251 cited

PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series

Lucas Correia, Jan-Christoph Goos, Thomas Bäck +1

Benchmarking anomaly detection approaches for multivariate time series is a challenging task due to a lack of high-quality datasets. Current publicly available datasets are too sma…

cs.LG20252 cited

TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data

Lucas Correia, Jan-Christoph Goos, Philipp Klein +2

As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. Thi…

cs.LG2025

DQS: A Low-Budget Query Strategy for Enhancing Unsupervised Data-driven Anomaly Detection Approaches

Lucas Correia, Jan-Christoph Goos, Thomas Bäck +2

Truly unsupervised approaches for time series anomaly detection are rare in the literature. Those that exist suffer from a poorly set threshold, which hampers detection performance…

cs.LG2024

Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions

Lucas Correia, Jan-Christoph Goos, Philipp Klein +2

Time-series anomaly detection plays an important role in engineering processes, like development, manufacturing and other operations involving dynamic systems. These processes can…