arXiv Machine Learning

Machine Learning under Imperfect Data: Challenges and Methods

arXiv Machine Learning

Aug 11

ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning

arXiv:2608. 09768v1 Announce Type: new Abstract: A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the model is mistaken.

By Ange-Cl\'ement Akazan, Ineza Remy Mugenga, Abebe Geletu, Jean Medard Ngnotchouye, Issa Karambal

benchmarks

arXiv Machine Learning

Jul 7

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.

By Nicolas Sournac, Ahmed Baha Ben Jmaa, Bertrand Braeckeveldt

benchmarks safety

arXiv Machine Learning

Jun 17

Learning Credal Ensembles via Distributionally Robust Optimization

arXiv:2602. 08470v3 Announce Type: replace Abstract: Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions.

By Kaizheng Wang, Ghifari Adam Faza, Fabio Cuzzolin, Siu Lun Chau, David Moens, Hans Hallez

benchmarks

arXiv Machine Learning

Aug 11

Coupled Training with Privileged Information and Unlabeled Data

arXiv:2605. 23268v2 Announce Type: replace-cross Abstract: In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model is deployed.

By Jiahao Shi, Omar Hagrass, Jason M. Klusowski

arXiv Machine Learning

Aug 12

Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration

arXiv:2608. 10372v1 Announce Type: new Abstract: Post-hoc calibration aligns a classifier's predicted confidences with its empirical accuracy without retraining.

By Lening Zhao, Qipeng Zhan, Li Shen

computer-vision benchmarks

arXiv Machine Learning

Jun 25

Certification of Machine Learning Models via Directional Sharpness

arXiv:2606. 25004v1 Announce Type: new Abstract: In machine learning, model certification has been identified as an important method for gaining assurance about a model's trustworthiness and quality.

By Gefei Tan, Adria Gascon, Sarah Meiklejohn, Mariana Raykova

arXiv Machine Learning

Aug 5

Stop Replacing Noise with Noise: Two-Source Reliability Assessment for Label Correction and Sample Reweighting in Label-Noise Learning

arXiv:2608. 03432v1 Announce Type: new Abstract: Refurbishment-based noisy-label learning mixes an observed label with a model-derived pseudo target, typically using one sample-wise cleanliness score to control both branches.

By Wenxiao Fan, Kan Li

benchmarks

arXiv Machine Learning

Aug 11

From Uncertainty to Failure Attribution: Self-Diagnosing Models for Failure Attribution under Distribution Shift

arXiv:2608. 07953v1 Announce Type: new Abstract: Distribution shift poses a significant challenge to the robustness of machine learning models, but the current solutions only aim to detect out-of-distribution (OOD) samples and predict uncertainty levels.

By Yiyao Yang

benchmarks safety

arXiv Statistics ML

Sep 10

Theoretical Analysis of Measure Consistency Regularization for Partially Observed Data

arXiv:2602.01437v2 Announce Type: replace-cross Abstract: The problem of corrupted data, missing features, or missing modalities continues to plague the modern machine learning landscape. To address...

By Yinsong Wang, Shahin Shahrampour

arXiv Machine Learning

3d ago

Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learning

arXiv:2609.14045v1 Announce Type: new Abstract: Agentic learning systems are often required to adapt after deployment by observing new data and reusing prior knowledge under limited supervision or fe...

By Yingxu Wang, Kunyu Zhang, Siyang Gao

rag agents fine-tuning benchmarks

arXiv AI

Jul 1

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.

By Miao Zhang, Junpeng Li, Changchun Hua, Yana Yang

arXiv AI

Jul 7

Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification

arXiv:2607. 05393v1 Announce Type: cross Abstract: Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines.

By Rapha\"el Bonnet-Guerrini, Bruno Sanchez, Dominique Fouchez, Benjamin Racine, Maya Guy, Mariam Sabalbal, Manal Yassine, Vincenzo Piuri

benchmarks

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