This paper considers a multi-environment factor model in which high-dimensional covariates are collected from heterogeneous environments, with auxiliary labels available in a subset of these environments. The joint distribution of the covariates may vary across environments, whereas the latent structure is decomposed into invariant factors with shared loadings and heterogeneous factors with environment-specific loadings.
arXiv:2608. 16245v1 Announce Type: new Abstract: Disentangled representation learning seeks latent representations whose indicidual dimensions each align with a distinct covariate.
By Ma{\l}gorzata {\L}az\k{e}cka, Ewa Szczurek
The paper introduces a transfer learning framework for structured matrix estimation when both the ambient dimension and the intrinsic representation grow over time. It models the target parameter as an embedded source component plus low‑rank innovations and sparse edits, and proposes an anchored alternating projection estimator that preserves the transferred subspace while estimating only the new components. Deterministic error bounds are derived that separate target noise, representation growth, and source estimation error, showing improved rates when rank and sparsity increments are small, and the framework is applied to Markov transition matrix estimation and structured covariance estimation with theoretical guarantees and empirical validation.
By Jinhang Chai, Xuyuan Liu, Elynn Chen, Yujun Yan
arXiv:1811. 05336v2 Announce Type: replace-cross Abstract: Inference for factor models is often hampered by the lack of tractable and accurate variance estimates, which can materially distort downstream analyses.
By Xingwei Hu, Caihong Hu, Cheng-Kuang Wu
arXiv:2608. 11917v1 Announce Type: new Abstract: Multi-output Gaussian process regression scales cubically in the number of observations times outputs, and dense kernel-matrix methods need bespoke handling whenever different outputs are observed at different inputs.
By Wouter W. L. Nuijten, Esther G. van Pelt, Albert Podusenko, \.Ismail \c{S}en\"oz, Wouter M. Kouw
arXiv:2608. 20065v1 Announce Type: new Abstract: World models construct latent states that support prediction, planning, and reasoning about an underlying system.
By Taoyong Cui, Pheng Ann Heng, Wanli Ouyang
arXiv:2607. 23337v1 Announce Type: new Abstract: Neural operators provide data-driven mappings for modeling dynamical systems.
By Zituo Chen, Qiaofeng Li, Jiaxin Hu, Sili Deng
Orthogonal JEPA introduces a latent world‑modeling framework that factorizes predictive states into orthogonal components. By learning basis matrices and dedicated prediction branches, the method reduces redundancy and improves gradient signals for less dominant predictive structures. The factorized states can be synthesized into complete latent representations for downstream tasks such as decoding, planning, or autoregressive rollout, and are evaluated across vision, biology, health, control, and molecular dynamics domains.
arXiv:2509. 09371v2 Announce Type: replace-cross Abstract: Distributionally robust optimization (DRO) protects statistical learning against distributional shifts by optimizing the worst-case performance over a set of perturbed distributions.
By Zitao Wang, Nian Si, Molei Liu
arXiv:2401. 04890v2 Announce Type: replace-cross Abstract: This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of interest depend sparsely on observed auxiliary variables and/or past latent factors.
By S\'ebastien Lachapelle, Pau Rodr\'iguez L\'opez, Yash Sharma, Katie Everett, R\'emi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien
arXiv:2601. 18128v2 Announce Type: replace-cross Abstract: High-dimensional data often exhibit variation that can be captured by lower-dimensional factors.
By Gemma E. Moran, Anandi Krishnan
arXiv:2607. 08254v1 Announce Type: new Abstract: Quantifying variability in a target population relative to a reference population is central to many scientific and clinical problems (e.
By Sai Spandana Chintapalli, Pratik Chaudhari, Christos Davatzikos