Hugging Face Trending Papers August 20, 2026

End-to-end Early Classification of Time Series in Non-Stationary Environments

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Early Classification of Time Series (ECTS) requires making accurate decisions as early as possible in inherently online and evolving environments. Yet, most existing methods assume stationarity and rely on separable designs, where classification and triggering are optimized independently, an assumption that fundamentally limits their adaptability under drift.

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arXiv Machine Learning

Aug 21

End-to-end Early Classification of Time Series in Non-Stationary Environments

arXiv:2608. 20044v1 Announce Type: new Abstract: Early Classification of Time Series (ECTS) requires making accurate decisions as early as possible in inherently online and evolving environments.

By Aur\'elien Renault, Alexis Bondu, Antoine Cornu\'ejols, Vincent Lemaire

reinforcement-learning benchmarks

arXiv AI

Aug 3

TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning

arXiv:2605. 00015v2 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) have demonstrated strong generalization capability and data efficiency in time series forecasting through large-scale pretraining.

By Siyang Li, Yize Chen, Zijie Zhu, Yuxin Pan, Yan Guo, Ming Huang, Hui Xiong

reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 9

Exploration of Fast-Slow Latent Recurrence for Train-Short, Test-Long Generalization

arXiv:2604. 01577v3 Announce Type: replace-cross Abstract: We study out of distribution generalization in streaming tasks where models are trained on short sequences but must operate over much longer, unknown horizons under bounded memory.

By Shota Takashiro, Masanori Koyama, Takeru Miyato, Yusuke Iwasawa, Yutaka Matsuo, Kohei Hayashi

llms reinforcement-learning safety

arXiv Machine Learning

Sep 1

Three Steps at a Time: Learning Representations from Action Sequences in Contrastive RL

arXiv:2608.30640v1 Announce Type: new Abstract: While self-supervised approaches to reinforcement learning have achieved strong results by learning representations of states and actions, a key open q...

By Michal Korniak, Kamil Dybek, Benjamin Eysenbach, Marco Bagatella, Micha{\l} Bortkiewicz

rag reinforcement-learning benchmarks

arXiv AI

Jul 15

Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning

arXiv:2601. 19624v3 Announce Type: replace-cross Abstract: Real-world reinforcement learning often faces environment drift, but most existing methods rely on static entropy coefficients/target entropy, causing over-exploration during stable periods and under-exploration after drift, and leaving unanswered the principled question of how exploration intensity should scale with drift magnitude.

By Tongxi Wang, Zhuoyang Xia, Xinran Chen, Shan Liu

reinforcement-learning

arXiv Machine Learning

Sep 1

Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL

arXiv:2502.06584v2 Announce Type: replace Abstract: Early Classification of Time Series (ECTS) is vital in fields like industrial monitoring and medical triage, where quick and accurate predictions a...

By Aur\'elien Renault, Alexis Bondu, Antoine Cornu\'ejols, Vincent Lemaire

reinforcement-learning

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End-to-end Early Classification of Time Series in Non-Stationary Environments

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