Good data stewardship requires removal of data at the request of the data's owner. This raises the question if and how a trained machine-learning model, which implicitly stores information about its training data, should be affected by such a removal request. Is it possible to "remove" data from a machine-learning mode…
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48 results for “data removal”
Selective removal of data subsets can efficiently unlearn unwanted distributions.
problem Efficiently removing unwanted data subsets without losing important information.
method Formalized as distributional unlearning, using Kullback-Leibler divergence constraints to select a small subset of data.
result Proposed method achieves corresponding log-loss bounds and is quadratically more sample-efficient than random removal.
PUMA augments models to remove unique data points without performance loss.
problem Preserving model performance while removing unique training data points.
method Explicitly models data influence, reweights remaining data optimally.
result PUMA effectively removes unique data points without performance degradation.
D3M debiases models by selectively removing problematic examples.
problem Model failures on underrepresented subgroups.
method Isolates and removes specific training examples that cause failures.
result Efficiently trains debiased classifiers with minimal example removal.
This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Gaussian process model…
DaRE forests enable efficient data deletion from random forests.
problem Efficiently removing data from machine learning models.
method Random Forests with data deletion enabled (DaRE).
result Data deletion from DaRE models is orders of magnitude faster than retraining.
A statistical framework for removing unwanted data domains in machine learning.
problem Removing unwanted data domains in machine learning while preserving desired performance.
method Modeling domains as probability distributions and using hypothesis testing to select samples to remove.
result Characterization of allowable edited data distributions and removal-preservation Pareto frontiers for various distribution families.
Squint bound improved by removing term.
problem Improving the Squint bound by removing the term.
method Using the Krichevsky--Trofimov algorithm to change the prior.
result Removed the term from the Squint bound.
Machine learning confound removal biases results, leading to misleading predictions.
problem Common confound removal methods in machine learning lead to misleading predictions.
method Featurewise removal of confound variance by linear regression before applying ML.
result This common deconfounding approach can leak information, amplifying null or moderate effects.
Algorithm removes spurious concepts from neural network representations without harming task performance.
problem Spurious correlations hinder neural network out-of-distribution generalization.
method Iterative algorithm that identifies two orthogonal subspaces in neural network representation.
result Algorithm outperforms existing methods on computer vision and natural language processing benchmarks.
Paper explores unsupervised learning for ultrasound image artifact removal.
problem Improving visual quality of ultrasound images from various artifacts.
method Inspired by optimal transport cycleGAN, unsupervised deep learning for artifact removal.
result Unsupervised learning method provides comparable results to supervised learning.
Fair Adversarial Networks remove bias from data.
problem Bias in datasets leads to biased outcomes, making analysis illegal and sub-optimal.
method Remove bias from data by altering all proxy variables, ensuring fairness without changing analytical pipelines.
result Fair Adversarial Networks effectively remove bias from data.
While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to …
A new algorithm removes unexpected correlations in biased data for better clustering.
problem Clustering with selection bias in data.
method Decorrelation regularized K-Means (DCKM) algorithm.
result DCKM achieves significant performance gains on real-world datasets.
Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers. It is common practice to (1) identify and remove outliers by looking at the data and (2) to fit OLS and form confidence intervals and p-values on the remaining data as if this were the original data col…
Removing noise from correlations in multivariate stock price datacond-mat.stat-mech
This paper examines the applicability of Random Matrix Theory to portfolio management in finance. Starting from a group of normally distributed stochastic processes with given correlations we devise an algorithm for removing noise from the estimator of correlations constructed from measured time series. We then apply t…
Data ownership and data protection are increasingly important topics with ethical and legal implications, e.g., with the right to erasure established in the European General Data Protection Regulation (GDPR). In this light, we investigate network embeddings, i.e., the representation of network nodes as low-dimensional …
A new method is proposed to compute connectivity measures on multivariate time series with gaps. Rather than removing or filling the gaps, the rows of the joint data matrix containing empty entries are removed and the calculations are done on the remainder matrix. The method, called measure adapted gap removal (MAGR), …
Machine learning predicts S&P 500 additions and removals with high accuracy.
problem Forecasting S&P 500 membership changes to inform investment decisions.
method Used Random Forest model on quarterly data from 2013 onwards, incorporating various features.
result Achieved a test F1 score of 0.85, outperforming other models.
Paper identifies and removes mislabeled data to improve neural network training.
problem Improving neural network training by identifying and removing ambiguous or mislabeled data.
method Introduces Area Under the Margin (AUM) statistic to identify mislabeled data and a simple procedure to remove it.
result Consistently improves test error on synthetic and real-world datasets.
Unified analysis of removal-based feature attributions robustness.
problem Robustness of removal-based feature attributions is not well understood.
method Theoretical analysis and upper bounds derivation for removal-based feature attributions under input and model perturbations.
result Upper bounds for the difference between intact and perturbed attributions derived under various perturbation settings.
Algorithm removes specific training data from models efficiently in high-dimensional settings.
problem Efficiently removing specific training data from high-dimensional models without full retraining.
method Starts from original model parameters, performs Newton steps, adds isotropic Laplacian noise.
result Two Newton steps are sufficient for effective unlearning in high-dimensional problems.
An important problem in networked systems is detection and removal of suspected malicious nodes. A crucial consideration in such settings is the uncertainty endemic in detection, coupled with considerations of network connectivity, which impose indirect costs from mistakely removing benign nodes as well as failing to r…
Efficiently removes specific data subsets without retraining for GDPR compliance.
problem Efficiently removing specific data subsets to comply with GDPR regulations.
method Statistical framework for machine unlearning with minimax optimality for squared loss.
result Developed Unlearning Least Squares (ULS) achieving minimax optimality for estimating model parameters.
Paper removes sensitive data from IoT and Big Data for privacy.
problem Privacy concerns in IoT and Big Data.
method Develops new supervised and adversarial learning methods to remove sensitive data.
result Models maintain predictive model utility while making sensitive predictions ineffective.
Unified theory explains how data augmentation improves deep learning models.
problem Understanding why data augmentation improves model generalization.
method Unified theoretical framework explaining two key effects: partial semantic feature removal and feature mixing.
result Data augmentation enhances generalization through partial semantic feature removal and feature mixing.
A network removes irrelevant structures from chest radiographs for better analysis.
problem Clutter in chest radiographs hinders visual inspection and analysis.
method Fully Convolutional Network to suppress undesired visual structure.
result Improved classifier performance with limited training data.
Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in -- and can be recovered from -- the intermediate representations learned by text-based neural classifiers. The …
Bayesian unlearning uses Bayes' rule to remove data from a model, but faces challenges in obtaining the exact posterior.
problem Removing data from a trained model while maintaining model accuracy.
method Uses Laplace approximation and Variational Inference to approximate the updated posterior.
result Insights on the applicability of Bayesian unlearning in practical scenarios for neural networks.
Perturbation-based explanation methods often measure the contribution of an input feature to an image classifier's outputs by heuristically removing it via e.g. blurring, adding noise, or graying out, which often produce unrealistic, out-of-samples. Instead, we propose to integrate a generative inpainter into three rep…
We study the classical problem of maximizing a monotone submodular function subject to a cardinality constraint k, with two additional twists: (i) elements arrive in a streaming fashion, and (ii) m items from the algorithm's memory are removed after the stream is finished. We develop a robust submodular algorithm STAR-…
MAGIC method optimally estimates model predictions changes.
problem Estimating how training data affects model predictions in large-scale settings.
method Combines classical methods and recent advances in metadifferentiation.
result MAGIC method nearly optimally estimates model predictions changes.
Unified framework for explaining models by removing features.
problem Unclear relationships and preferences among existing model explanation methods.
method Removal-based explanations characterized by three dimensions.
result Unified framework unifies 26 existing methods.
Predictive modeling is increasingly being employed to assist human decision-makers. One purported advantage of replacing human judgment with computer models in high stakes settings-- such as sentencing, hiring, policing, college admissions, and parole decisions-- is the perceived "neutrality" of computers. It is argued…
New method removes specific training data influence from neural networks.
problem Removing specific training data influence from neural networks for privacy and regulatory reasons.
method Noisy fine-tuning on retain data to ensure provable unlearning guarantees without restrictive assumptions.
result Achieves formal unlearning guarantees and performs effectively in practice.
MNIST and Fashion MNIST are extremely popular for testing in the machine learning space. Fashion MNIST improves on MNIST by introducing a harder problem, increasing the diversity of testing sets, and more accurately representing a modern computer vision task. In order to increase the data quality of FashionMNIST, this …
Ray Singer torsion is a numerical invariant associated with a compact Riemannian manifold equipped with a flat bundle and a Hermitian structure on this bundle. In this note we show how one can remove the dependence on the Riemannian metric and on the Hermitian structure with the help of a base point and of an Euler str…
The paper proves removable singularity for nonlocal minimal graphs.
problem Proving removable singularities for nonlocal minimal graphs.
method Analyzing -capacity zero compact sets to ensure graphs are minimal in the entire domain.
result Nonlocal minimal graphs are removable in the entire domain if they are minimal in a set of -capacity zero.
Unified framework for model explanation methods based on feature removal.
problem Unclear relationships and preferences among various model explanation methods.
method Characterizes removal-based explanations along three dimensions.
result Unified 26 existing methods, including widely used approaches.
Proposes a new k-NN algorithm to improve classification accuracy by removing noise and pseudo-neighbours.
problem Noise and pseudo-neighbours in large-scale databases affect k-NN performance.
method Introduces a weighted mutual k-Nearest Neighbour algorithm to detect and remove noise, and minimize distant neighbours' influence.
result The proposed algorithm provides comparative better results compared to standard k-NN.
Removing spurious features can hurt model accuracy and disproportionately affect different groups.
problem Interference from spurious features in robust model performance across different groups.
method Characterization and analysis of spurious feature removal in noiseless overparameterized linear regression.
result Removal of spurious features can decrease accuracy and disproportionately affect different groups, even in balanced datasets.
Bayesian inference forgetting framework removes influence of single data points.
problem Enforcement of the right to be forgotten in machine learning causes high costs for companies.
method Develops forgetting algorithms for variational and Markov chain Monte Carlo in Bayesian inference.
result Proves removal of influence of single datums on learned models with guaranteed generalizability.
Removes singularities for Yang-Mills-Higgs fields in higher dimensions.
problem Yang-Mills-Higgs fields with isolated singularities.
method Establishes decay estimates and conformally invariant energy bounds.
result Removable singularity theorem for Yang-Mills-Higgs fields.
Obtaining the state of the art performance of deep learning models imposes a high cost to model generators, due to the tedious data preparation and the substantial processing requirements. To protect the model from unauthorized re-distribution, watermarking approaches have been introduced in the past couple of years. W…
Algorithm removes backdoor watermarks from neural networks robustly.
problem Protecting neural networks from backdoor attacks.
method Neural network laundering algorithm.
result Effective removal of backdoor watermarks with high test accuracy.
New algorithm for mean estimation in add-remove model achieves optimal error.
problem Mean estimation in add-remove model of differential privacy.
method Proposed new algorithm achieving min-max optimality.
result Achieves best possible constant in mean squared error for all ε.
Improves GAN performance by identifying and removing harmful training instances.
problem Challenges in applying previous instance removal methods to GANs.
method Proposes influence estimation methods using Jacobian of generator's gradient and instance evaluation scheme based on GAN evaluation metrics.
result Removing harmful instances significantly improves GAN performance.
We call a singularity of a presymplectic form removable in its graph if its graph extends to a smooth Dirac structure over the singularity. An example for this is the symplectic form of a magnetic monopole. A criterion for the removability of singularities is given in terms of regularizing functions for pure spinor…