mlsanityData to 7 Oct 2026

Data Valuation Without Training of a Model

arXiv 2301.00930 ↗First posted 3 Jan 2023cs.LGcited by 35

Not on the map. Why?

Abstract (arXiv)

Many recent works on understanding deep learning try to quantify how much individual data instances influence the optimization and generalization of a model. Such attempts reveal characteristics and importance of individual instances, which may provide useful information in diagnosing and improving deep learning. However, most of the existing works on data valuation require actual training of a model, which often demands high-computational cost. In this paper, we provide a training-free data valuation score, called complexity-gap score, which is a data-centric score to quantify the influence of individual instances in generalization of two-layer overparameterized neural networks. The proposed score can quantify irregularity of the instances and measure how much each data instance contributes in the total movement of the network parameters during training. We theoretically analyze and empirically demonstrate the effectiveness of the complexity-gap score in finding `irregular or mislabeled' data instances, and also provide applications of the score in analyzing datasets and diagnosing training dynamics. Our code is publicly available at https://github.com/JJchy/CG_score

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 33 of its 43 references that are in the map. First posted on arXiv 3 Jan 2023.

Builds on

33 of 43 references.

References · 33

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  6. 2016Densely Connected Convolutional Networkscited by 6,691
  7. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  8. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  9. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  10. 2019Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descentcited by 732
  11. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
  12. 2018Learning to Reweight Examples for Robust Deep Learningcited by 670
21 more
  1. 2018An Empirical Study of Example Forgetting during Deep Neural Network Learningcited by 669
  2. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
  3. 2020Estimating Training Data Influence by Tracing Gradient Descentcited by 565
  4. 2019Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networkscited by 562
  5. 2021Deep Learning on a Data Diet: Finding Important Examples Early in Trainingcited by 529
  6. 2022Beyond neural scaling laws: beating power law scaling via data pruningcited by 519
  7. 2020What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimationcited by 492
  8. 2020Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamicscited by 423
  9. 2020Identifying Mislabeled Data using the Area Under the Margin Rankingcited by 259
  10. 2020Geometry-aware Instance-reweighted Adversarial Trainingcited by 198
  11. 2020Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernelcited by 185
  12. 2017Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samplescited by 147
  13. 2021Deep Learning Through the Lens of Example Difficultycited by 139
  14. 2020When Do Curricula Work?cited by 105
  15. 2020Estimating Example Difficulty Using Variance of Gradientscited by 96
  16. 2020Characterizing Structural Regularities of Labeled Data in Overparameterized Modelscited by 93
  17. 2021FINE Samples for Learning with Noisy Labelscited by 82
  18. 2019Robust Inference via Generative Classifiers for Handling Noisy Labelscited by 67
  19. 2020Efficient computation and analysis of distributional Shapley valuescited by 38
  20. 2021Rethinking Influence Functions of Neural Networks in the Over-parameterized Regimecited by 26
  21. 2021Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networkscited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 15; 2025: 11; 2026: 4.

35 papers.

Sort:

By area:

  1. 2023DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Modelscited by 121
  2. 2024What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functionscited by 88
  3. 2024Data Shapley in One Training Runcited by 76
  4. 2024Rethinking Data Shapley for Data Selection Tasks: Misleads and Meritscited by 29
  5. 2024A CLIP-Powered Framework for Robust and Generalizable Data Selectioncited by 20
  6. 2024Dataset Difficulty and the Role of Inductive Biascited by 12
  7. 2024BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Rangescited by 8
  8. 2023What do larger image classifiers memorise?cited by 6
  9. 2024Precedence-Constrained Winter Value for Effective Graph Data Valuationcited by 6
  10. 2025When Dynamic Data Selection Meets Data Augmentationcited by 6
  11. 2023GMValuator: Similarity-based Data Valuation for Generative Modelscited by 5
  12. 2023Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategycited by 5
23 more
  1. 2025Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracingcited by 4
  2. 2025Nonparametric Data Attribution for Diffusion Modelscited by 4
  3. 2023Not All Data Matters: An End-to-End Adaptive Dataset Pruning Framework for Enhancing Model Performance and Efficiencycited by 3
  4. 2024CHG Shapley: Efficient Data Valuation and Selection towards Trustworthy Machine Learningcited by 3
  5. 2025RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessmentcited by 3
  6. 2024EcoVal: An Efficient Data Valuation Framework for Machine Learningcited by 2
  7. 2024Data-Centric AI in the Age of Large Language Modelscited by 2
  8. 2024Fast Training Dataset Attribution via In-Context Learningcited by 2
  9. 2025UtilGen: Utility-Centric Generative Data Augmentation with Dual-Level Task Adaptationcited by 2
  10. 2024Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniquescited by 1
  11. 2024Private, Augmentation-Robust and Task-Agnostic Data Valuation Approach for Data Marketplacecited by 1
  12. 2025Unifying and Optimizing Data Values for Selection via Sequential Decision-Makingcited by 1
  13. 2025Shapley-Guided Utility Learning for Effective Graph Inference Data Valuationcited by 1
  14. 2025Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learningcited by 1
  15. 2024VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AIcited by 0
  16. 2024Reframing Data Value for Large Language Models Through the Lens of Plausibilitycited by 0
  17. 2025Data Overvaluation Attack and Truthful Data Valuation in Federated Learningcited by 0
  18. 2025KCES: Training-Free Defense for Robust Graph Neural Networks via Kernel Complexitycited by 0
  19. 2025Diffusion Reconstruction-based Data Likelihood Estimation for Core-Set Selectioncited by 0
  20. 2026Data Agent: Learning to Select Data via End-to-End Dynamic Optimizationcited by 0
  21. 2026Beyond What to Select: A Plug-and-play Oscillatory Data-Volume Scheduling for Efficient Model Trainingcited by 0
  22. 2026Data Pruning: Redundant, Problematic, and Interdependent Samplescited by 0
  23. 2026Kernel-Complexity Edge Sanitization for Training-Free Defense against Structural Graph Attackscited by 0