On-the-fly category discovery requires deciding for each incoming test sample whether to assign it to an existing category or spawn a new one. Existing methods typically implement this decision through matching-based heuristics, such as radius- or hash-based rules.
arXiv:2607. 24583v1 Announce Type: new Abstract: Large scale Bayesian nonparametrics (BNP) learner such as Stochastic Variational Inference (SVI) can handle datasets with large class number and large training size at fractional cost.
arXiv:2608.21729v1 Announce Type: new Abstract: Simulation-Based Inference (SBI) serves as a vital framework for parameter inference in scientific fields where simulators involve intractable likeliho...
Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention: given partially labelled data, the goal is to correctly recognize known classes while d...
arXiv:2601.17609v3 Announce Type: replace Abstract: In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that st...
arXiv:2607. 22961v1 Announce Type: new Abstract: Verbalized Machine Learning (VML) parameterizes a model as a natural-language prompt that an LLM evaluates as f(x; theta).
arXiv:2607. 24943v1 Announce Type: cross Abstract: In many classification problems, reliable instance-level labels are unavailable.
arXiv:2608. 11162v1 Announce Type: new Abstract: The Naive Bayes (NB) classifier remains a standard choice for categorical data, yet its widely used smoothing rules, such as Laplace, Lidstone, Krichevsky-Trofimov, and the $m$-estimate, all prescribe a fixed smoothing strength that ignores feature cardinality, sample size, and class imbalance, inducing a non-vanishing bias on modern high-cardinality tabular data.
arXiv:2412. 12807v4 Announce Type: replace-cross Abstract: Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confident and abstain when uncertainty is high.
The paper introduces Structured Prior Knowledge (SPK), a framework that extracts and organizes latent priors from pretrained object detectors to improve out-of-distribution (OoD) detection. SPK uses in-distribution data and hallucination-inducing samples to elicit part-level semantic concepts, then combines these with geometric and contextual priors into a compact five-dimensional representation. Experiments across various detector architectures and OoD benchmarks show that SPK achieves state-of-the-art performance, demonstrating that pretrained detectors encode richer latent knowledge than previously exploited.
CloSeR is a plug‑and‑play framework that enhances Generalized Category Discovery (GCD) by injecting closed‑set relational knowledge from a lightweight teacher model. The teacher is built by fine‑tuning adapters on labeled data while keeping the backbone frozen, preserving pretrained priors. Unified Relational Distillation then transfers both global sample‑to‑prototype and local sample‑to‑sample relations to the GCD task, reducing optimization interference and improving performance across six benchmarks with DINO and DINOv2 backbones.
arXiv:2606. 18933v1 Announce Type: new Abstract: Active feature acquisition (AFA) sequentially selects which features to observe to reach a classification or ranking decision.
arXiv:2606. 20538v1 Announce Type: new Abstract: Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization.