Data Valuation by Leveraging Global and Local Statistical Information

• URL: http://arxiv.org/abs/2405.17464v1
• Date: Thu, 23 May 2024 08:58:08 GMT
• Title: Data Valuation by Leveraging Global and Local Statistical Information
• Authors: Xiaoling Zhou, Ou Wu, Michael K. Ng, Hao Jiang,
• Abstract summary: We show that both global and local value distributions hold significant potential for data valuation within the context of machine learning. We propose a new data valuation method that estimates Shapley values by incorporating the explored distribution characteristics into an existing method, AME. We also present a new path to address the dynamic data valuation problem by formulating an optimization problem that integrates information of both global and local value distributions.
• Score: 25.911043100052588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data valuation has garnered increasing attention in recent years, given the critical role of high-quality data in various applications, particularly in machine learning tasks. There are diverse technical avenues to quantify the value of data within a corpus. While Shapley value-based methods are among the most widely used techniques in the literature due to their solid theoretical foundation, the accurate calculation of Shapley values is often intractable, leading to the proposal of numerous approximated calculation methods. Despite significant progress, nearly all existing methods overlook the utilization of distribution information of values within a data corpus. In this paper, we demonstrate that both global and local statistical information of value distributions hold significant potential for data valuation within the context of machine learning. Firstly, we explore the characteristics of both global and local value distributions across several simulated and real data corpora. Useful observations and clues are obtained. Secondly, we propose a new data valuation method that estimates Shapley values by incorporating the explored distribution characteristics into an existing method, AME. Thirdly, we present a new path to address the dynamic data valuation problem by formulating an optimization problem that integrates information of both global and local value distributions. Extensive experiments are conducted on Shapley value estimation, value-based data removal/adding, mislabeled data detection, and incremental/decremental data valuation. The results showcase the effectiveness and efficiency of our proposed methodologies, affirming the significant potential of global and local value distributions in data valuation.

Related papers

• Data Distribution Valuation [56.71023681599737]
  Existing data valuation methods define a value for a discrete dataset. In many use cases, users are interested in not only the value of the dataset, but that of the distribution from which the dataset was sampled. We propose a maximum mean discrepancy (MMD)-based valuation method which enables theoretically principled and actionable policies.
  arXiv  Detail & Related papers  (2024-10-06T07:56:53Z)
• Is Data Valuation Learnable and Interpretable? [3.9325957466009203]
  Current data valuation methods ignore the interpretability of the output values. This study aims to answer an important question: is data valuation learnable and interpretable?
  arXiv  Detail & Related papers  (2024-06-03T08:13:47Z)
• Neural Dynamic Data Valuation [4.286118155737111]
  We propose a novel data valuation method from the perspective of optimal control, named the neural dynamic data valuation (NDDV) Our method has solid theoretical interpretations to accurately identify the data valuation via the sensitivity of the data optimal control state. In addition, we implement a data re-weighting strategy to capture the unique features of data points, ensuring fairness through the interaction between data points and the mean-field states.
  arXiv  Detail & Related papers  (2024-04-30T13:39:26Z)
• TRIAGE: Characterizing and auditing training data for improved regression [80.11415390605215]
  We introduce TRIAGE, a novel data characterization framework tailored to regression tasks and compatible with a broad class of regressors. TRIAGE utilizes conformal predictive distributions to provide a model-agnostic scoring method, the TRIAGE score. We show that TRIAGE's characterization is consistent and highlight its utility to improve performance via data sculpting/filtering, in multiple regression settings.
  arXiv  Detail & Related papers  (2023-10-29T10:31:59Z)
• OpenDataVal: a Unified Benchmark for Data Valuation [38.15852021170501]
  We introduce OpenDataVal, an easy-to-use and unified benchmark framework for data valuation. OpenDataVal provides an integrated environment that includes eleven different state-of-the-art data valuation algorithms. We perform benchmarking analysis using OpenDataVal, quantifying and comparing the efficacy of state-of-the-art data valuation approaches.
  arXiv  Detail & Related papers  (2023-06-18T14:38:29Z)
• Shapley Value on Probabilistic Classifiers [6.163093930860032]
  In the context of machine learning (ML), data valuation methods aim to equitably measure the contribution of each data point to the utility of an ML model. Traditional Shapley-based data valuation methods may not effectively distinguish between beneficial and detrimental training data points. We propose Probabilistic Shapley (P-Shapley) value by constructing a probability-wise utility function.
  arXiv  Detail & Related papers  (2023-06-12T15:09:13Z)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value [17.340091573913316]
  We propose Data-OOB, a new data valuation method for a bagging model that utilizes the out-of-bag estimate. Data-OOB takes less than 2.25 hours on a single CPU processor when there are $106$ samples to evaluate and the input dimension is 100. We demonstrate that the proposed method significantly outperforms existing state-of-the-art data valuation methods in identifying mislabeled data and finding a set of helpful (or harmful) data points.
  arXiv  Detail & Related papers  (2023-04-16T08:03:58Z)
• Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning [60.41501515192088]
  Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively. The data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model. In this work, we integrate the local real data with the global gradient prototypes to form the local balanced datasets.
  arXiv  Detail & Related papers  (2023-01-25T03:18:10Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• A Principled Approach to Data Valuation for Federated Learning [73.19984041333599]
  Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources. The Shapley value (SV) defines a unique payoff scheme that satisfies many desiderata for a data value notion. This paper proposes a variant of the SV amenable to FL, which we call the federated Shapley value.
  arXiv  Detail & Related papers  (2020-09-14T04:37:54Z)

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.