arXiv Machine Learning

Robust Ambiguity Detection (RAD) From Model- and Feature-Space Consistency

arXiv:2608. 11541v1 Announce Type: new Abstract: Machine learning models should be robust, in the sense of remaining predictively consistent under permissible variations.

arXiv Machine Learning

Aug 6

Data-Aware and Scalable Sensitivity Analysis for Decision Tree Ensembles

arXiv:2602. 07453v2 Announce Type: replace Abstract: Decision tree ensembles are widely used in critical domains, making robustness and sensitivity analysis essential to their trustworthiness.

By Namrita Varshney, Ashutosh Gupta, Arhaan Ahmad, Tanay V. Tayal, S. Akshay

robotics safety

arXiv Machine Learning

Jun 2

Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness

arXiv:2606. 01746v1 Announce Type: cross Abstract: Modern neural networks are highly susceptible to adversarial perturbations.

By Kai Wang

fine-tuning benchmarks safety

arXiv Machine Learning

Aug 11

General OOD Detection via Model-aware and Subspace-aware Variable Priority

arXiv:2512. 13003v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is essential for determining when a supervised model encounters inputs that differ meaningfully from its training distribution.

By Min Lu, Hemant Ishwaran

benchmarks

arXiv AI

Jun 19

Beyond Accuracy: Measuring Logical Compliance of Predictive Models

arXiv:2606. 20208v1 Announce Type: new Abstract: Machine learning models are predominantly evaluated through predictive performance metrics such as ranking quality, prediction error, or classification accuracy.

By Guillaume Olivier Delplanque (LIG), Pierre Genev\`es (LIG), Nabil Laya\"ida (LIG,TYREX), Zephirin Faure

rag agents benchmarks data-engineering

arXiv Machine Learning

Jul 10

Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection

arXiv:2603. 23318v2 Announce Type: replace Abstract: Among the different possible strategies for evaluating the reliability of individual predictions of classifiers, robustness quantification stands out as a method that evaluates how much uncertainty a classifier could cope with before changing its prediction.

By Rodrigo F. L. Lassance, Jasper De Bock

arXiv Machine Learning

Jun 16

Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data

arXiv:2605. 27618v2 Announce Type: replace Abstract: Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may not always be reliable.

By Tom\'as Pereira, Jo\~ao Vitorino, Eva Maia, Isabel Pra\c{c}a

benchmarks

arXiv Machine Learning

Jul 9

Optimized Instance Alteration for Explaining and Assessing Robustness of Classifiers

arXiv:2607. 06637v1 Announce Type: new Abstract: In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers.

By Evgenii Kuriabov, David Miller, Jia Li

safety

arXiv Machine Learning

Jul 23

Building Fast, Evaluating Slow: Pipeline Choices Dominate Autointerpretability Score Variance

arXiv:2607. 19386v1 Announce Type: new Abstract: Cross-paper comparison of sparse autoencoder (SAE) interpretability often relies on autointerpretability scores.

By Sinie van der Ben, Neele Roch, Anna Hedstr\"om, Mennatallah El-Assady

llms safety

arXiv Machine Learning

Jul 21

When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift

arXiv:2602. 14161v2 Announce Type: replace Abstract: Detecting prompt injection, jailbreak attacks, and harmful requests is critical for deploying LLM-based agents safely, yet current evaluation practices in this literature overestimate generalization.

By Max Fomin

llms agents benchmarks safety

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 AI

Jul 24

Evaluating Risks in Weak-to-Strong Alignment: A Bias-Variance Perspective

arXiv:2604. 25077v2 Announce Type: replace Abstract: Weak-to-strong alignment offers a promising route to scalable supervision, but it can fail when a strong model becomes confidently wrong on examples that lie in the weak model's blind spots.

By Hamid Osooli, Kareema Batool, Rick Gentry, Tiasa Singha Roy, Ashwin Gupta, Anirudha Ramesh

reinforcement-learning fine-tuning safety

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

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