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arXiv:2602.23663v1 Announce Type: new Abstract: Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning …
arXiv:2602.23696v1 Announce Type: new Abstract: We study the geometry of training trajectories in small transformer models and find that parameter updates organize into a dominant …
arXiv:2602.23737v1 Announce Type: new Abstract: Cross-domain reinforcement learning (RL) aims to learn transferable policies under dynamics shifts between source and target domains. A key challenge …
arXiv:2602.23761v1 Announce Type: new Abstract: Optical design is the process of configuring optical elements to precisely manipulate light for high-fidelity imaging. It is inherently a …
arXiv:2602.23770v1 Announce Type: new Abstract: Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. …
arXiv:2602.23784v1 Announce Type: new Abstract: Foundation models have transformed domains from language to genomics by learning general-purpose representations from large-scale, heterogeneous data. We introduce TradeFM, …
arXiv:2602.23785v1 Announce Type: new Abstract: We investigate the identifiability of nonlinear Canonical Correlation Analysis (CCA) in a multi-view setup, where each view is generated by …
arXiv:2602.23789v1 Announce Type: new Abstract: The performance of search algorithms for grid-based pathfinding, e.g. A*, critically depends on the heuristic function that is used to …
arXiv:2602.23795v1 Announce Type: new Abstract: Structured deep model compression methods are hardware-friendly and substantially reduce memory and inference costs. However, under aggressive compression, the resulting …
arXiv:2602.23798v1 Announce Type: new Abstract: Machine unlearning for large language models often faces a privacy dilemma in which strict constraints prohibit sharing either the server's …
arXiv:2602.23804v1 Announce Type: new Abstract: Reinforcement learning (RL) actor-critic algorithms enable autonomous learning but often require a large number of environment interactions, which limits their …
arXiv:2602.23811v1 Announce Type: new Abstract: We investigate the theoretical aspects of offline reinforcement learning (RL) under general function approximation. While prior works (e.g., Xie et …