arXiv AI
Jul 29
Multiclass Classification without Labels via Posterior Simplex Geometry
arXiv:2607. 24943v1 Announce Type: cross Abstract: In many classification problems, reliable instance-level labels are unavailable.
Geometry-Aware Bayesian Quantification via Compositional Data Analysis
arXiv:2607. 04977v1 Announce Type: new Abstract: Accurately estimating the unknown target label distribution is the critical first step for adapting to label shift.
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arXiv Machine Learning
Aug 31
Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classification
arXiv:2603.16621v2 Announce Type: replace Abstract: We propose a conjugate and calibrated Gaussian process (GP) model for multi-class classification by exploiting the geometry of the probability simp...
arXiv Machine Learning
Sep 10
Learning Kernels by Alignment for Multiclass Bayes Classification
The paper introduces a framework that learns the kernel used in kernel methods through alignment, leveraging the Collaborative Learning and Inference (CLaI) approach. It demonstrates that CLaI can be interpreted as a kernel alignment process and that its inference stage is equivalent to kernel Bayes classification with Parzen-window density estimation. By replacing cosine similarity with a learned Mahalanobis distance, the authors extend CLaI to multiclass classification, achieving higher accuracy, faster convergence, and lower calibration error on datasets such as CIFAR-10, PathMNIST, and SleepEDF, while also showing connections to Gaussian processes and competitive calibration in sepsis prediction.
arXiv Machine Learning
Jul 22
Information Theoretic Bayesian Optimization over the Probability Simplex
arXiv:2603. 09793v2 Announce Type: replace Abstract: Bayesian optimization is a data-efficient technique that has been shown to be extremely powerful to optimize expensive, black-box, and possibly noisy objective functions.
arXiv AI
Jul 7
CuBAS: Information Geometric Curvature-Based Adaptive Sampling for Supervised Classification
arXiv:2607. 03145v1 Announce Type: cross Abstract: The informativeness of a training set is as consequential as its size, yet most sampling strategies remain agnostic to the intrinsic geometry of the data distribution.
arXiv Machine Learning
Aug 27
JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning
JEPAMatch introduces a new semi‑supervised learning framework that replaces traditional output‑thresholding with explicit geometric shaping of latent representations. By combining the FlexMatch loss with a latent‑space regularization inspired by LeJEPA, the method encourages isotropic Gaussian structure in the embedding space, mitigating class imbalance and noisy pseudo‑labels. Experiments on CIFAR‑100, STL‑10, and Tiny‑ImageNet show consistent performance gains and faster convergence compared to existing FixMatch‑based baselines.
arXiv Statistics ML
Aug 26
Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification
arXiv:2608.23960v1 Announce Type: cross Abstract: Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of...
arXiv Machine Learning
Jul 9
MARGIN: Margin-Aware Regularized Geometry for Imbalanced Vulnerability Detection
arXiv:2605. 10240v3 Announce Type: replace-cross Abstract: Software vulnerability detection is critical for ensuring software security and reliability.
arXiv AI
Jun 16
Canonical Variates in Wasserstein Metric Space
arXiv:2405. 15768v2 Announce Type: replace-cross Abstract: In this paper, we address the classification of instances represented by distributions on a vector space rather than single points.
arXiv Statistics ML
3d ago
Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification
arXiv:2609.39829v1 Announce Type: new Abstract: Modern machine learning depends heavily on massive datasets, but obtaining high-quality annotations at scale is often expensive. As a result, learning...
arXiv Machine Learning
Sep 1
Informative Label Missingness in Multiclass Classification Information Geometry and Excess Risk
arXiv:2608.30561v1 Announce Type: cross Abstract: Informative label missingness can change the usual efficiency ordering between completely and partially labelled classifiers because the pattern of m...
arXiv Machine Learning
Jul 16
The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model
arXiv:2607. 13660v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyperspherical geometry.