arXiv Machine Learning July 31, 2026 By Shahriar Kabir Nahin, Wenxiao Xiao, Joshua Liu, Anshuman Chhabra, Hongfu Liu

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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 Machine Learning

Jun 2

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining

arXiv:2510. 06048v4 Announce Type: replace Abstract: Effective data selection is essential for pretraining large language models (LLMs), enhancing efficiency and improving generalization to downstream tasks.

By Jie Hao, Rui Yu, Wei Zhang, Huixia Wang, Jie Xu, Mingrui Liu

llms benchmarks

arXiv AI

1d ago

Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities

arXiv:2501. 12147v2 Announce Type: replace-cross Abstract: Selecting appropriate training data is crucial for instruction fine-tuning of large language models (LLMs), which aims to (1) elicit strong capabilities, and (2) achieve balanced performance across different tasks.

By Qirun Dai, Dylan Zhang, Jiaqi W. Ma, Hao Peng

llms fine-tuning benchmarks safety

arXiv AI

Jun 18

DRIFT: Refining Instruction Data via On-Policy Data Attribution

arXiv:2606. 18307v1 Announce Type: cross Abstract: Optimizing the training data distribution for Supervised Fine-Tuning (SFT) dictates the capability of Large Language Models (LLMs).

By Zefan Wang, Lincheng Li, Tianyu Yu, Yuan Yao

llms fine-tuning safety

arXiv Machine Learning

Aug 4

Learning the Pareto Frontier of Predictive Models under Distribution Shift

arXiv:2608. 00632v1 Announce Type: new Abstract: Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks.

By Yiming Dong, Jiwei Zhao, Yang Young Lu

fine-tuning

arXiv Machine Learning

1d ago

Limits to scalable evaluation at the frontier: LLM as Judge won't beat twice the data

arXiv:2410. 13341v4 Announce Type: replace Abstract: High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem.

By Florian E. Dorner, Vivian Y. Nastl, Moritz Hardt

llms

Related stories

Model-agnostic Mitigation Strategies of Data Imbalance for Regression

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining

Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities

DRIFT: Refining Instruction Data via On-Policy Data Attribution

Learning the Pareto Frontier of Predictive Models under Distribution Shift

Limits to scalable evaluation at the frontier: LLM as Judge won't beat twice the data