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Training Data Influence Analysis and Estimation: A Survey
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Good models require good training data. For overparameterized deep models, the causal relationship between training data and model predictions is increasingly opaque and poorly understood. Influence analysis partially demystifies training's underlying interactions by quantifying the amount each training instance alters the final model. Measuring the training data's influence exactly can be provably hard in the worst case; this has led to the development and use of influence estimators, which only approximate the true influence. This paper provides the first comprehensive survey of training data influence analysis and estimation. We begin by formalizing the various, and in places orthogonal, definitions of training data influence. We then organize state-of-the-art influence analysis methods into a taxonomy; we describe each of these methods in detail and compare their underlying assumptions, asymptotic complexities, and overall strengths and weaknesses. Finally, we propose future research directions to make influence analysis more useful in practice as well as more theoretically and empirically sound. A curated, up-to-date list of resources related to influence analysis is available at https://github.com/ZaydH/influence_analysis_papers.
Forward citations
Cited by 7 Pith papers
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The Ultimate Cookbook for Invisible Poison: Crafting Subtle Clean-Label Text Backdoors with Style Attributes
AttrBkd uses fine-grained stylistic attributes as backdoor triggers, achieving higher human-reported subtlety and comparable or higher attack success than prior conspicuous triggers.
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Understanding Data Influence with Differential Approximation
This paper introduces Diff-In, an influence estimator that accumulates second-order approximations of influence differences across training steps and shows strong accuracy in data cleaning, deletion, and coreset selec...
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MAGIC: Near-Optimal Data Attribution for Deep Learning
MAGIC computes the exact influence function for smooth, deterministic deep learning training runs and achieves near-perfect linear datamodeling scores on CIFAR-10, GPT-2, and Gemma-2B, far outperforming TRAK and EK-FAC.
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Dataset Distillation by Influence Matching
Inf-Match distills datasets by matching estimated parameter influence of real and synthetic data, reporting SOTA classification and retrieval, but with an unsupported theoretical core.
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A Comparative Analysis of Influence Signals for Data Debugging
A benchmark shows Self-Influence finds mislabeled training samples, while all tested influence signals fail to detect clustered anomalies and outliers under cumulative TracIn scoring.
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DeGLIF for Label Noise Robust Node Classification using GNNs
DeGLIF identifies noisy graph nodes by approximating how much each node's removal would improve loss on a small clean set, relabels them with the model's most confident alternative class, and retrains, improving accur...
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PXGen: A Post-hoc Explainable Method for Generative Models
PXGen is a post-hoc, training-free explanation framework that scores anchor samples with intrinsic and extrinsic criteria, groups them by thresholds, and selects representative examples via k-dispersion or k-center.
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