arXiv：cs.LG· Gwenol\'e Quellec·· 4 小时前AI 评分30
约束潜状态建模 CLSM：竞争约束下表征学习的统一视角
Constrained latent state modeling: A unifying perspective on representation learning under competing constraints
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研究者提出约束潜状态建模（CLSM）框架，用预测充分性、最小性、时间一致性、观测兼容性、对干扰因素的不变性、结构约束六个性质刻画潜状态，并将这些性质与诱导它们的代理目标及评估诊断方法分离。该框架重新解释了主要表征学习家族，并通过受控合成基准展示不同目标如何随预测目标、代理形式、参数化和优化产生不同的潜状态组织与经验权衡。参考实现、可复现实验、文档和模型卡已随附仓库发布。
正文
Abstract:Learning latent representations from temporal, multimodal, and partially observed data requires specifying what information a latent state should retain, discard, and organize. Existing approaches encode these requirements through heterogeneous objectives, making methods difficult to compare and learned representations difficult to interpret. We propose Constrained Latent State Modeling (CLSM), a conceptual framework that characterizes latent states through six complementary properties: predictive sufficiency, minimality, temporal coherence, observation compatibility, invariance to nuisance factors, and structural constraints. CLSM separates these properties from the surrogate objectives used to induce them and from the diagnostics used to evaluate them, and clarifies how combinations of constraints can improve identifiability by restricting the space of admissible representations. We reinterpret major representation-learning families through this common design space and illustrate the framework with a controlled synthetic benchmark. The experiments show how objectives produce distinct latent organizations and empirical trade-offs depending on the prediction target, surrogate formulation, parameterization, and optimization. Companion repository containing the reference implementation, reproducible experiments, documentation, and model cards: this https URL
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.15995 [cs.LG] |
| (or arXiv:2605.15995v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15995 arXiv-issued DOI via DataCite |
Submission history
From: Gwenole Quellec [view email]
[v1]
Fri, 15 May 2026 14:25:40 UTC (250 KB)
[v2]
Thu, 23 Jul 2026 16:07:36 UTC (547 KB)
[v3]
Wed, 7 Oct 2026 11:34:25 UTC (552 KB)
来源：arXiv：cs.LG · arxiv.org