Online adaptations of early time series classifiers, particularly RL-based ones, improve robustness to drifting or stochastic decision costs.
Learning under concept drift: A review
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RETROFIT enables continual learning for malware detection and binary summarization by retrospective-free parameter merging with low-rank sparse updates and confidence-guided arbitration, improving retention and generalization without historical data.
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Early Classification of Time Series in Non-Stationary Cost Regimes
Online adaptations of early time series classifiers, particularly RL-based ones, improve robustness to drifting or stochastic decision costs.
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Retrofit: Continual Learning with Controlled Forgetting for Binary Security Detection and Analysis
RETROFIT enables continual learning for malware detection and binary summarization by retrospective-free parameter merging with low-rank sparse updates and confidence-guided arbitration, improving retention and generalization without historical data.