arXiv Machine Learning

Enhancing Conformal Prediction via Class Similarity

arXiv:2511. 19359v2 Announce Type: replace Abstract: Conformal Prediction (CP) has emerged as a powerful statistical framework for high-stakes classification applications.

arXiv Machine Learning

Jun 30

Conformal Prediction with Macro-Coverage Guarantees

arXiv:2606. 28598v1 Announce Type: cross Abstract: Prediction sets should have high coverage to be useful, but some coverage notions are more practically relevant than others.

By Aabesh Bhattacharyya, Tiffany Ding, Rina Foygel Barber

computer-vision

arXiv Machine Learning

Jul 1

Localized Conformal Prediction for Image Classification with Vision-Language Models

arXiv:2606. 31577v1 Announce Type: cross Abstract: Conformal predictions have attracted significant attention in the field of uncertainty quantification, mainly because of their strong marginal coverage guarantees.

By Cl\'ement Fuchs, Tim Bary, Beno\^it Macq

llms computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 16

Audited Conformal Prediction for Classification under Unknown Distribution Shift

arXiv:2606. 14909v1 Announce Type: cross Abstract: We consider the problem of uncertainty quantification for a pretrained classification model deployed under unknown distribution shift.

By Yanfei Zhou, Rizal Fathony, Nam H. Nguyen, Matteo Sesia

arXiv Machine Learning

Aug 4

xMICD: Explainable Representation of Multiple ICD Codes

arXiv:2608. 00935v1 Announce Type: new Abstract: Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning.

By Pat Vatiwutipong, Kumkup Keeratisiwakul, Albert Phuoc Kien Van Truong, Nutcha Yodrabum, Wasin Pansiritanachot, Marvin N. Wright, Thanapon Noraset

rag safety

arXiv Machine Learning

Jul 31

What Is The Performance Ceiling of My Classifier? Utilizing Category-Wise Influence Functions for Pareto Frontier Analysis

arXiv:2510. 03950v2 Announce Type: replace Abstract: Data-centric learning seeks to improve model performance from the perspective of data quality, and has been drawing increasing attention in the machine learning community.

By Shahriar Kabir Nahin, Wenxiao Xiao, Joshua Liu, Anshuman Chhabra, Hongfu Liu

benchmarks data-engineering

arXiv Machine Learning

Jul 9

A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It

arXiv:2607. 06605v1 Announce Type: new Abstract: Conformal prediction is being adopted in drug discovery to put an honest number on model reliability: pick an error rate alpha, and the method returns prediction sets containing the true label with probability at least 1 - alpha.

By Muhammadjon Tursunbadalov (School of Science and Technology, Champions College Prep, United States), Mustafojon Tursunbadalov (School of Science and Technology, Champions College Prep, United States)

llms safety

arXiv AI

Jun 8

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference

arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.

By Anurag Sharma, Sai Teja Chunchu, Prasenjit Mitra, Sandipan Sikdar, Koustav Rudra

llms benchmarks safety

arXiv Machine Learning

Jul 23

Plausibility-Driven Prioritization of Candidate Biomedical Annotations

arXiv:2607. 20163v1 Announce Type: cross Abstract: The rapid growth of biomedical knowledge has made the validation of automatically generated biological annotations a major bottleneck in biomedical curation.

By Emanuele Cavalleri, Miad Alavinezhad, Dario Malchiodi, Marco Mesiti

rag

arXiv Machine Learning

Jun 25

Model-agnostic Mitigation Strategies of Data Imbalance for Regression

arXiv:2506. 01486v2 Announce Type: replace Abstract: Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability.

By Jelke Wibbeke, Sebastian Rohjans, Andreas Rauh

benchmarks safety

arXiv AI

Jun 29

iCost: A Novel Instance-Complexity-Based Cost-Sensitive Learning Framework

arXiv:2409. 13007v3 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the majority class.

By Asif Newaz, Asif Ur Rahman Adib, Taskeed Jabid

safety

arXiv AI

Jun 15

Learning High Coverage Discriminative Parsimonious Rulesets

arXiv:2606. 14156v1 Announce Type: cross Abstract: Learning systems based on IF-THEN rule representations readily offer interpretability, making them a crucial focus in contemporary AI research.

By Mariamma Antony, Raman Sankaran, Chiranjib Bhattacharyya, Uma Satya Ranjan

benchmarks safety

arXiv Machine Learning

Jun 9

Similarity-Distance-Magnitude Activations

arXiv:2509. 12760v5 Announce Type: replace Abstract: We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax activation function, adding Similarity (i.

By Allen Schmaltz

llms safety

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