Efficient Data Selection

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Period ending 2026-09-07

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Exact Recovery Thresholds for Weighted Data Selection in Vector-Valued Linear Regression
Aug 31, 2026Guangjian ZhangEfficient Data Selection Regression
Epiplexity Guided Data Selection and Generation for Out-of-Distribution Generalization
Aug 12, 2026Ellen Su, Andres Potapczynski, Shikai Qiu +2Efficient Data Selection Distributional Learning
Agentic Instruction Data Selection: Let DataMaster Interpret Your Intent
Aug 11, 2026Fanqi Zhou, Qiaosheng Chen, Zixian Huang +1Efficient Data Selection Data Science Agents
SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement
Jul 21, 2026Arther Tian, Alex Ding, Simon Wu +1Gating Supervised Fine-Tuning
RIMS: Preference Optimization via Smoothed Multi-pair Aggregation for Small-Scale LLM Retrieval-Augmented Generation
Jul 17, 2026Pei Tian, Zihan Dong, Tianci Liu +2Retrieval-Augmented Generation Small Large Language Models
Online Data Selection Is Implicit Alignment
Jul 8, 2026Aoxiong Zeng, Yuxin Yang, Xiangquan YangEfficient Data Selection
SIEVE: Structure-Aware Data Selection for Imitation Learning with VLA Models
Jul 7, 2026Changti Wu, Bin Yu, Zhaolong Shen +6Imitation Learning Vision-Language-Action
Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection
Jun 17, 2026Richard Yi Da XuEfficient Data Selection Determinantal Point Process
BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training
Jun 17, 2026Jiaxing Wang, Deping Xiang, Jin Xu +9Efficient Data Selection Large Language Model Training
QueryMarket: Cost-Aware Online Active Learning in Data Markets
Jun 16, 2026Xiwen Huang, Pierre PinsonActive Learning Prices
Spokes: Optimizing for Diverse Pretraining Data Selection
Jun 13, 2026Clarence Lee, Yejin Choi, Luke Zettlemoyer +2Efficient Data Selection Output and Model Diversity
RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning
Jun 10, 2026Atif Hassan, Swanand Khare, Jiaul H. PaikExtreme Class Imbalance Magnitude-Based Pruning
Hubs or Fringes: Pretraining Data Selection via Web Graph Centrality
Jun 9, 2026Vedant Badoni, Danqi Chen, Xinyi WangPersonalized Pagerank Efficient Data Selection
Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey
Jun 9, 2026Vanessa Schmidt, Huy Hoang Nguyen, Cédric Jung +2Efficient Data Selection Large Language Model Training
Minibatch Selection for Language Models via Partition Matroid Constrained Gradient Matching
Jun 6, 2026Prayas Agrawal, Prateek Chanda, Ishita Khatri +3Efficient Data Selection Partitioning
HARP: Efficient Data Selection for Finetuning Large Language Models
Jun 5, 2026Ning Wang, Zhengxin Zhang, Maosen Tang +3Efficient Data Selection Finetuning
MLSkip: Data Skipping for ML Filters via Lightweight Metadata
Jun 2, 2026Mihail Stoian, Mark Gerarts, Pascal Ginter +3Efficient Data Selection Semantic Deduplication
On the Difficulty of Learning a Meta-network for Training Data Selection
May 30, 2026Zilin Du, Junqi Zhao, Boyang Albert LiEfficient Data Selection Meta-Learning
Towards Efficient LLMs Annealing with Principled Sample Selection
May 29, 2026Yuanjian Xu, Jianing Hao, Wanbo Zhang +2Efficient Data Selection Cosine Annealing
Adaptive data selection improves wearable prediction under low baseline performance
May 29, 2026Ali KargarandehkordiWearable Devices Efficient Data Selection
MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection
May 28, 2026Haowen Wang, Yaxin Du, Jian Yang +9Efficient Data Selection Large Language
Complement Submodular Information Measures for Balanced and Robust Data Selection
May 23, 2026Rishabh IyerSubmodular Maximization Efficient Data Selection
PRISM: Preference-Aware Influence Function Based Data Selection Method for Efficient Fine-Tuning
May 20, 2026Qihao Lin, Guanxu Chen, Dongrui Liu +1Efficient Data Selection Finetuning
From Parameters to Data: A Task-Parameter-Guided Fine-Tuning Pipeline for Efficient LLM Alignment
May 20, 2026Hao Chen, Qi Zhang, Liyao Li +7Parameter-Efficient Fine-Tuning Methods Large Language Model Alignment
DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models
May 17, 2026Haichao Sha, Zihao Wang, Yuncheng Wu +2Differential Privacy Finetuning
SEED: Targeted Data Selection by Weighted Independent Set
May 15, 2026Yuan Zhang, Lifeng Guo, Junwen Pan +5Efficient Data Selection Seeds
Interaction-Aware Influence Functions for Group Attribution
May 15, 2026Jaeseung Heo, Kyeongheung Yun, Youngbin Choi +3Efficient Influence Function Influence
Beyond What to Select: A Plug-and-play Oscillatory Data-Volume Scheduling for Efficient Model Training
May 14, 2026Suorong Yang, Hanqi Zhu, Hai Gan +4Efficient Data Selection Regularization Methods
WASHH: An Anchor-Aware Whale-Guided Selection Hyper-Heuristic for Continuous Optimization and SVC Configuration
May 13, 2026Yifu Zhao, Xiaofan Zou, Junhao Wei +9Black-Box Optimization Hyperparameter
SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning
May 13, 2026Run Zou, Jianhang Ding, Yifan Ding +3Efficient Data Selection Instruction-Tuned Large Language Models
Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning
May 13, 2026Siyuan Liu, Tinghong Chen, Xinghan Li +2Large Language Model Fine-Tuning Supervised Fine-Tuning
Constraint-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments
May 11, 2026Danilo Brajovic, David A. Kreplin, Marco F. HuberMagnitude-Based Pruning Efficient Data Selection
DataMaster: Data-Centric Autonomous AI Research
May 11, 2026Yaxin Du, Xiyuan Yang, Zhifan Zhou +12Data Science Agents Mle-Bench Lite
Is Data Shapley Not Better than Random in Data Selection? Ask NASH
May 11, 2026Xiao Tian, Jue Fan, Rachael Hwee Ling Sim +3Efficient Data Selection Shapley Value
LLM-Driven Performance-Space Augmentation for Meta-Learning-Based Algorithm Selection
May 10, 2026Darren Zhu, Daren LerMeta-Learning Data Augmentation
Let the Target Select for Itself: Data Selection via Target-Aligned Paths
May 10, 2026Huitao Yang, Hengzhi He, Guang ChengEfficient Data Selection Reweighting
DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation
May 9, 2026Yang Zhou, Can Jin, Zihan Dong +7Efficient Data Selection
How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanism
May 8, 2026Kana Miyamoto, Kanata Suzuki, Tetsuya OgataImitation Learning Robotic Data Acquisition
Efficient Data Selection for Multimodal Models via Incremental Optimization Utility
May 8, 2026Jinhao Jing, Qiannian Zhao, Chao Huang +1Large Multimodal Models Efficient Data Selection
Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training
May 8, 2026Pingbang Hu, Xueshen Liu, Z. Morley Mao +1Efficient Data Selection Large Language Model Post-Training
One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
May 7, 2026Xinrui Chen, Liu Yang, Ou WuLarge Language Model Fine-Tuning Finetuning
Building informative materials datasets beyond targeted objectives
May 6, 2026Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6Materials Discovery Efficient Data Selection
From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models
Apr 28, 2026Ling Shi, Xinwei Wu, Xiaohu Zhao +7Efficient Data Selection Mechanistic Interpretability
What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective
Apr 28, 2026Guangzeng Han, Xiaolei HuangInstruction Tuning Efficient Data Selection
Nearly Optimal Subdata Selection
Apr 27, 2026Min Yang, Wei Zheng, John Stufken +3Efficient Data Selection Submodular Maximization
Quality-Driven Selective Mutation for Deep Learning
Apr 24, 2026Zaheed Ahmed, Emmanuel Charleson Dapaah, Philip Makedonski +1Cryptographically Authorized Mutation Efficient Data Selection
Cross-Domain Data Selection and Augmentation for Automatic Compliance Detection
Apr 23, 2026Fariz Ikhwantri, Dusica MarijanCompliance Efficient Data Selection

Aug 31, 2026 · cs.LGJ/K move · Enter open · S save

Exact Recovery Thresholds for Weighted Data Selection in Vector-Valued Linear Regression

Guangjian Zhang

We resolve the threshold part of Question 4 of the COLT 2025 open problem "Data Selection for Regression Tasks" of Hanneke, Moran, Shlimovich and Yehudayoff. In vector-valued linear regression with square loss

\ell_{(x,y)}(W)=|Wx-y|_2^2

, where

x\in\mathbb{R}^d

y\in\mathbb{R}^m

and the learner is the empirical risk minimizer of minimal Frobenius norm, we prove that the minimal budget of weighted examples that recovers the full-data loss on every finite dataset is exactly

n^*(d,m)=(m+1)d

. We further determine two more values of the weighted selection profile

F_w(d,m,n)

: at the near-threshold budget,

F_w(d,m,(m+1)d-1)=1+\frac{1}{dm^2}

, and at the spanning budget,

F_w(d,m,d)=d+1

for every

m

, while

F_w(d,m,n)=\infty

for

n<d

. For the smallest open intermediate cell

(d,m)=(2,2)

we prove

F_w(2,2,3)\in[13/8,15/8]

and

F_w(2,2,4)\in[5/4,3/2]

, reduce the conjectured exact values

13/8

and

5/4

to a finite moment problem on the circle with at most seven atoms, and establish strong structural evidence for the conjecture. The upper-bound techniques (a fixed-basis conic compression lemma, a determinant-facet rigidity theorem for maximal certificates, and sharp sparsification lemmas for zero-mean weighted point systems) are of independent interest. As a byproduct we correct an erroneous claim circulating in a recent unrefereed preprint, exhibiting an explicit dataset with

m=2

on which no weighted selection of

2d

points recovers the optimal loss. All results are new only for

m\ge 2

; the scalar case

m=1

is due to Hanneke et al.

Efficient Data Selection Regression Spectral Norm Sparsity Budgets

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