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
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
OliO is a self‑supervised learning method for time‑series forecasting that models bidirectional temporal flows using a linear ODE‑based transition operator. By enforcing a strictly upper triangular constraint on the transition matrix, it ensures numerical stability and captures long‑term dependencies without exponential divergence. Experiments on multiple backbones and benchmarks show up to 5.75% MSE improvement over state‑of‑the‑art baselines.
By Kwangryeol Park, Seulki Lee
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
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
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