Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value

Data valuation is a powerful framework for providing statistical insights into which data are beneficial or detrimental to model training. Many Shapley-based data valuation methods have shown promising results in various downstream tasks, however, they are well known to be computationally challenging as it requires training a large number of models. As a result, it has been recognized as infeasible to apply to large datasets. To address this issue, we propose Data-OOB, a new data valuation method for a bagging model that utilizes the out-of-bag estimate. The proposed method is computationally efficient and can scale to millions of data by reusing trained weak learners. Specifically, Data-OOB takes less than 2.25 hours on a single CPU processor when there are $10^6$ samples to evaluate and the input dimension is 100. Furthermore, Data-OOB has solid theoretical interpretations in that it identifies the same important data point as the infinitesimal jackknife influence function when two different points are compared. We conduct comprehensive experiments using 12 classification datasets, each with thousands of sample sizes. 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, highlighting the potential for applying data values in real-world applications.Comment: 18 pages, ICML 202

OpenDataVal: a Unified Benchmark for Data Valuation

Assessing the quality and impact of individual data points is critical for improving model performance and mitigating undesirable biases within the training dataset. Several data valuation algorithms have been proposed to quantify data quality, however, there lacks a systemic and standardized benchmarking system for data valuation. In this paper, we introduce OpenDataVal, an easy-to-use and unified benchmark framework that empowers researchers and practitioners to apply and compare various data valuation algorithms. OpenDataVal provides an integrated environment that includes (i) a diverse collection of image, natural language, and tabular datasets, (ii) implementations of eleven different state-of-the-art data valuation algorithms, and (iii) a prediction model API that can import any models in scikit-learn. Furthermore, we propose four downstream machine learning tasks for evaluating the quality of data values. We perform benchmarking analysis using OpenDataVal, quantifying and comparing the efficacy of state-of-the-art data valuation approaches. We find that no single algorithm performs uniformly best across all tasks, and an appropriate algorithm should be employed for a user's downstream task. OpenDataVal is publicly available at https://opendataval.github.io with comprehensive documentation. Furthermore, we provide a leaderboard where researchers can evaluate the effectiveness of their own data valuation algorithms.Comment: 25 pages, NeurIPS 2023 Track on Datasets and Benchmark

A robust calibration-assisted method for linear mixed effects model under cluster-specific nonignorable missingness

We propose a method for linear mixed effects models when the covariates are completely observed but the outcome of interest is subject to missing under cluster-specific nonignorable (CSNI) missingness. Our strategy is to replace missing quantities in the full-data objective function with unbiased predictors derived from inverse probability weighting and calibration technique. The proposed approach can be applied to estimating equations or likelihood functions with modified E-step, and does not require numerical integration as do previous methods. Unlike usual inverse probability weighting, the proposed method does not require correct specification of the response model as long as the CSNI assumption is correct, and renders inference under CSNI without a full distributional assumption. Consistency and asymptotic normality are shown with a consistent variance estimator. Simulation results and a data example are presented

Lipschitz Continuous Autoencoders in Application to Anomaly Detection

Anomaly detection is the task of finding abnormal data that are distinct from normal behavior. Current deep learning-based anomaly detection methods train neural networks with normal data alone and calculate anomaly scores based on the trained model. In this work, we formalize current practices, build a theoretical framework of anomaly detection algorithms equipped with an objective function and a hypothesis space, and establish a desirable property of the anomaly detection algorithm, namely, admissibility. Admissibility implies that optimal autoencoders for normal data yield a larger reconstruction error for anomalous data than that for normal data on average. We then propose a class of admissible anomaly detection algorithms equipped with an integral probability metric-based objective function and a class of autoencoders, Lipschitz continuous autoencoders. The proposed algorithm for Wasserstein distance is implemented by minimizing an approximated Wasserstein distance with a penalty to enforce Lipschitz continuity with respect to Wasserstein distance. Through ablation studies, we demonstrate the efficacy of enforcing Lipschitz continuity of the proposed method. The proposed method is shown to be more effective in detecting anomalies than existing methods via applications to network traffic and image datasets(1).N

ISLES 2016 and 2017-Benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI

Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).Fundacao para a Ciencia e Tecnologia (FCT), Portugal (scholarship number PD/BD/113968/2015). FCT with the UID/EEA/04436/2013, by FEDER funds through COMPETE 2020, POCI-01-0145-FEDER-006941. NIH Blueprint for Neuroscience Research (T90DA022759/R90DA023427) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under award number 5T32EB1680. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. PAC-PRECISE-LISBOA-01-0145-FEDER-016394. FEDER-POR Lisboa 2020-Programa Operacional Regional de Lisboa PORTUGAL 2020 and Fundacao para a Ciencia e a Tecnologia. GPU computing resources provided by the MGH and BWH Center for Clinical Data Science Graduate School for Computing in Medicine and Life Sciences funded by Germany's Excellence Initiative [DFG GSC 235/2]. National Research National Research Foundation of Korea (NRF) MSIT, NRF-2016R1C1B1012002, MSIT, No. 2014R1A4A1007895, NRF-2017R1A2B4008956 Swiss National Science Foundation-DACH 320030L_163363

Calibrated propensity score method for survey nonresponse in cluster sampling

Weighting adjustment is commonly used in survey sampling to correct for unit nonresponse. In cluster sampling, the missingness indicators are often correlated within clusters and the response mechanism is subject to cluster-specific nonignorable missingness. Based on a parametric working model for the response mechanism that incorporates cluster-specific nonignorable missingness, we propose a method of weighting adjustment. We provide a consistent estimator of the mean or totals in cases where the study variable follows a generalized linear mixed-effects model. The proposed method is robust in the sense that the consistency of the estimator does not require correct specification of the functional forms of the response and outcome models. A consistent variance estimator based on Taylor linearization is also proposed. Numerical results, including a simulation and a real-data application, are presented.N