A Comparative Analysis of Influence Signals for Data Debugging

• URL: http://arxiv.org/abs/2506.11584v1
• Date: Fri, 13 Jun 2025 08:47:04 GMT
• Title: A Comparative Analysis of Influence Signals for Data Debugging
• Authors: Nikolaos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides,
• Abstract summary: influence-based signals can potentially identify both mislabeled and anomalous samples from a potentially noisy training set.<n>We show that signals like Self-Influence effectively detect mislabeled samples, but none of the existing signals can detect anomalies.
• Score: 3.6458439734112695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improving the quality of training samples is crucial for improving the reliability and performance of ML models. In this paper, we conduct a comparative evaluation of influence-based signals for debugging training data. These signals can potentially identify both mislabeled and anomalous samples from a potentially noisy training set as we build the models and hence alleviate the need for dedicated glitch detectors. Although several influence-based signals (e.g., Self-Influence, Average Absolute Influence, Marginal Influence, GD-class) have been recently proposed in the literature, there are no experimental studies for assessing their power in detecting different glitch types (e.g., mislabeled and anomalous samples) under a common influence estimator (e.g., TraceIn) for different data modalities (image and tabular), and deep learning models (trained from scratch or foundation). Through extensive experiments, we show that signals like Self-Influence effectively detect mislabeled samples, but none of the existing signals can detect anomalies. Existing signals do not take into account the training dynamics, i.e., how the samples' influence on the model changes during training, while some signals fall into influence cancellation effects, i.e., influence score is zero due to unsigned scores accumulation, resulting in misleading influence attribution.

Related papers

• Dissecting Representation Misalignment in Contrastive Learning via Influence Function [15.28417468377201]
  We introduce the Extended Influence Function for Contrastive Loss (ECIF), an influence function crafted for contrastive loss.<n>ECIF considers both positive and negative samples and provides a closed-form approximation of contrastive learning models.<n>Building upon ECIF, we develop a series of algorithms for data evaluation, misalignment detection, and misprediction trace-back tasks.
  arXiv  Detail & Related papers  (2024-11-18T15:45:41Z)
• Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection [82.43311784594384]
  Real-world datasets contain not only noisy labels but also class imbalance. We propose a simple yet effective method to address noisy labels in imbalanced datasets.
  arXiv  Detail & Related papers  (2024-02-17T10:34:53Z)
• The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes [30.30769701138665]
  We introduce and explore the Mirrored Influence Hypothesis, highlighting a reciprocal nature of influence between training and test data. Specifically, it suggests that evaluating the influence of training data on test predictions can be reformulated as an equivalent, yet inverse problem. We introduce a new method for estimating the influence of training data, which requires calculating gradients for specific test samples, paired with a forward pass for each training point.
  arXiv  Detail & Related papers  (2024-02-14T03:43:05Z)
• Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation [53.27596811146316]
  Diffusion models operate over a sequence of timesteps instead of instantaneous input-output relationships in previous contexts. We present Diffusion-TracIn that incorporates this temporal dynamics and observe that samples' loss gradient norms are highly dependent on timestep. We introduce Diffusion-ReTrac as a re-normalized adaptation that enables the retrieval of training samples more targeted to the test sample of interest.
  arXiv  Detail & Related papers  (2024-01-17T07:58:18Z)
• Representation Learning for Wearable-Based Applications in the Case of Missing Data [20.37256375888501]
  multimodal sensor data in real-world environments is still challenging due to low data quality and limited data annotations. We investigate representation learning for imputing missing wearable data and compare it with state-of-the-art statistical approaches. Our study provides insights for the design and development of masking-based self-supervised learning tasks.
  arXiv  Detail & Related papers  (2024-01-08T08:21:37Z)
• Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks [91.15120211190519]
  This paper aims to understand the nature of noise in pre-training datasets and to mitigate its impact on downstream tasks. We propose a light-weight black-box tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise.
  arXiv  Detail & Related papers  (2023-09-29T06:18:15Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features [43.41573458276422]
  We introduce a novel learning-based solution, leveraging a noise detector, instanced by an LSTM network. The proposed method trains the noise detector in a supervised manner using the dataset with synthesized label noises. Results show that the proposed method precisely detects mislabeled samples on various datasets without further adaptation.
  arXiv  Detail & Related papers  (2022-12-19T09:39:30Z)
• Tracking the risk of a deployed model and detecting harmful distribution shifts [105.27463615756733]
  In practice, it may make sense to ignore benign shifts, under which the performance of a deployed model does not degrade substantially. We argue that a sensible method for firing off a warning has to both (a) detect harmful shifts while ignoring benign ones, and (b) allow continuous monitoring of model performance without increasing the false alarm rate.
  arXiv  Detail & Related papers  (2021-10-12T17:21:41Z)
• Assessing the Quality of the Datasets by Identifying Mislabeled Samples [14.881597737762316]
  We propose a novel statistic -- noise score -- as a measure for the quality of each data point to identify mislabeled samples. In our work, we use the representations derived by the inference network of data quality supervised variational autoencoder (AQUAVS) We validate our proposed statistic through experimentation by corrupting MNIST, FashionMNIST, and CIFAR10/100 datasets.
  arXiv  Detail & Related papers  (2021-09-10T17:14:09Z)
• Learning with Out-of-Distribution Data for Audio Classification [60.48251022280506]
  We show that detecting and relabelling certain OOD instances, rather than discarding them, can have a positive effect on learning. The proposed method is shown to improve the performance of convolutional neural networks by a significant margin.
  arXiv  Detail & Related papers  (2020-02-11T21:08:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.