arXiv Machine Learning July 7, 2026 By Pengcheng Zeng, Siyuan Jiang

Semi-parametric Functional Classification via Path Signatures Logistic Regression with Adaptive Order Selection

Read the original on arXiv Machine Learning →

arXiv:2507. 06637v2 Announce Type: replace-cross Abstract: We propose Path Signatures Logistic Regression (PSLR), a semi-parametric framework for classifying vector-valued functional data with scalar covariates.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

Jul 7

Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation

arXiv:2303. 08777v3 Announce Type: replace-cross Abstract: Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theoretical properties when embedded in a learning procedure remain insufficiently understood.

By Diego Marcondes, Cl\'audia Peixoto

safety

arXiv Machine Learning

Jul 31

Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories

arXiv:2607. 28567v1 Announce Type: cross Abstract: Longitudinal causal studies often record histories as irregular functional fragments: laboratory values, physiologic signals, sensor streams, and image-derived summaries measured at unequal and informative times.

By Mengfei Ran, Yifeng Shen, Ruijie Guan

benchmarks

arXiv Machine Learning

Jul 14

Branched Signature Kernel Solvers for ODEs with rough Single-Trajectory signals

arXiv:2605. 25826v2 Announce Type: replace-cross Abstract: We develop a branched signature kernel solver for linear and nonlinear ordinary differential equations driven by a \emph{single observed trajectory} of a possibly rough forcing signal--a setting common within earthquake engineering, finance, biology, and structural health monitoring, where only one forcing realization is available, and the solver must respect the underlying physical law without an ensemble of realizations.

By Munawar Ali, Qi Feng, Charlie Pyle, George Xu

benchmarks

arXiv Machine Learning

Jun 16

Probabilistic Signature Inversion: Learning Conditional Distributions from Truncated Signatures

arXiv:2606. 15332v1 Announce Type: new Abstract: The signature transform is a principled feature map for continuous-time paths, valued for its uniqueness and universality.

By Junoh Kang, Kiseop Lee, Bohyung Han

diffusion

arXiv Machine Learning

Jun 9

Disentangled Feature Importance

arXiv:2507. 00260v3 Announce Type: replace-cross Abstract: When predictors are statistically dependent, the appropriate definition of feature importance depends on the operational goal.

By Jin-Hong Du, Kathryn Roeder, Larry Wasserman

arXiv Machine Learning

Jul 21

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework

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

fine-tuning

Related stories

Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation

Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories

Branched Signature Kernel Solvers for ODEs with rough Single-Trajectory signals

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Disentangled Feature Importance

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework