Presents first online L2D algorithm for multiclass classification with bandit feedback and varying experts, achieving O((n+n_e)T^{2/3}) regret generally and O((n+n_e)√T) under low noise.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
TSseek approximates time series as line segments and regex queries as bounding rectangles, then uses a distributed spatial index (TSseek-X) to support efficient exact whole-matching and subsequence-matching queries.
Tri-SfSVD is a unified sparse functional SVD framework that performs simultaneous subject, feature, and temporal selection for biclustering and triclustering in longitudinal omics and EEG data.
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Online Learning-to-Defer with Varying Experts
Presents first online L2D algorithm for multiclass classification with bandit feedback and varying experts, achieving O((n+n_e)T^{2/3}) regret generally and O((n+n_e)√T) under low noise.
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TSseek: Regular Expression-Based Similarity Search for Distributed Time Series Datasets
TSseek approximates time series as line segments and regex queries as bounding rectangles, then uses a distributed spatial index (TSseek-X) to support efficient exact whole-matching and subsequence-matching queries.
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Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data
Tri-SfSVD is a unified sparse functional SVD framework that performs simultaneous subject, feature, and temporal selection for biclustering and triclustering in longitudinal omics and EEG data.