arXiv AI By Shizhan Cai, Qixin Zhang, Siyuan Ma, Siyuan Liang, Yongcheng Jing, Dacheng Tao

Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization

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arXiv Machine Learning
Jun 18

BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training

arXiv:2606. 18650v1 Announce Type: new Abstract: As Large Language Model (LLM) datasets scale to trillions of tokens, data selection has emerged as a critical frontier to filter out uninformative noise and construct adaptive learning trajectories.

By Jiaxing Wang, Deping Xiang, Jin Xu, Zirui Liu, Zicheng Zhang, Guoqiang Gong, Jun Fang, Chao Liu, Pengzhang Liu, Tongxuan Liu, Ke Zhang, Qixia Jiang
arXiv Machine Learning
2d ago

Towards Faithful Data Selection: Statistical Limits and Scalable Algorithms

The paper investigates the limits of optimal data selection for machine learning, showing that minimizing population error is impossible without a validation set much larger than the training set. It introduces a new concept called faithfulness, providing an estimable upper bound on the performance gap between training on a subset and on the full dataset. The authors propose a swap‑based local search algorithm that iteratively reduces this bound and demonstrate its effectiveness on CIFAR‑10 and CIFAR‑100 datasets.

By Ahmed Mehdi Inane, Mahdi Haghifam, Gintare Karolina Dziugaite, Ioannis Mitliagkas
arXiv Machine Learning
Jun 26

Learning from a Biased Sample

arXiv:2209. 01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed.

By Roshni Sahoo, Lihua Lei, Stefan Wager