arXiv Machine Learning
Jul 22
Weak-to-Strong Learning in Decision Making
arXiv:2607. 18467v1 Announce Type: new Abstract: Many operational decisions rely on predictive models that estimate uncertain outcomes conditional on observable contexts.
A Unified and Stable Risk Minimization Framework for Weakly Supervised Learning with Theoretical Guarantees
arXiv:2511. 22823v2 Announce Type: replace-cross Abstract: Weakly supervised learning has emerged as a practical alternative to fully supervised learning when complete and accurate labels are costly or infeasible to acquire.
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arXiv Statistics ML
3d ago
Constrained Classification and Policy Learning
The paper investigates the consistency of surrogate loss methods for classification and policy learning when the set of admissible classifiers is constrained, such as by interpretability or fairness requirements. It shows that hinge loss is the only surrogate that preserves consistency when constraints limit only the prediction set, but consistency can fail if constraints also restrict the functional form. The authors derive conditions guaranteeing consistency for hinge-risk-minimizing classifiers and use these results to design efficient hinge-loss-based procedures for monotone classification problems.
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 15
Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
arXiv:2512. 17788v2 Announce Type: replace Abstract: Multi-instance partial-label learning (MIPL) is a weakly supervised framework that extends the principles of multi-instance learning (MIL) and partial-label learning (PLL) to address the challenges of inexact supervision in both instance and label spaces.
arXiv Machine Learning
Sep 24
Learning Risk Scores Robust to Unobserved Confounders
The paper introduces a method for learning risk scores that remain reliable even when historical data contain unobserved confounders. By treating propensity weights as uncertain and applying sensitivity analysis with Wasserstein distributionally robust optimization, the authors formulate a robust learning problem solvable via an exponential cone program. Experiments on semi‑synthetic UCI data show the approach improves calibration by up to 29.2% over traditional benchmarks and 11.1% over the state of the art, without harming other performance metrics.
arXiv Machine Learning
Aug 10
Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions
arXiv:2608. 06896v1 Announce Type: new Abstract: Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data.
arXiv AI
Sep 1
Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning
arXiv:2608.30699v1 Announce Type: cross Abstract: Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to...
arXiv AI
Jun 11
CoVar: Confidence-Variance-Guided Pseudo-Label Selection for Semi-Supervised Learning
arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.
arXiv AI
Jul 28
Directional Influence Function: Estimating Training Data Influence in Constrained Learning
arXiv:2607. 23388v1 Announce Type: cross Abstract: As constrained learning becomes increasingly common, models are trained under explicit feasibility requirements to enforce fairness, safety, robustness, regulariza- tion, and physics or logic constraints.
Hugging Face Trending Papers
Aug 24
Conformal Risk Minimization for Semi-Supervised Domain Adaptation via Optimal Transport
In high-stakes healthcare applications, machine learning models are frequently trained on data from one patient population and deployed on another, creating a distribution shift that degrades both acc...
Hugging Face Trending Papers
Aug 7
Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions
Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data. However, this requirement is often not met in real-world applications.
arXiv AI
2d ago
Optimal Transport Reweighting for Robust Learning under Spurious Correlations and Label Noise
arXiv:2610.01028v1 Announce Type: cross Abstract: Machine learning models often suffer performance degradation under subpopulation shift, particularly when spurious correlations cause models to rely...