MaxRR efficiently unlearns models by splitting and selecting core samples.
problem Efficiently unlearning models while verifying unlearning guarantees.
method Model splitting and core sample selection with a generalized unlearning metric.
result MaxRR achieves efficient unlearning with properties matching full retraining.
DVWU framework improves model performance by considering data value heterogeneity.
problem Existing machine unlearning algorithms ignore data value heterogeneity, potentially degrading model performance.
method Data Value-Weighted Unlearning (DVWU) framework that integrates data values into the unlearning process.
result DVWU achieves superior predictive performance and robustness compared to conventional unlearning approaches.
Unified framework for removing unwanted information from machine learning models.
problem Removing undesirable features or data points from machine learning models while preserving utility.
method Information-theoretic regularization approach for data point and feature unlearning.
result Unified mathematical framework with provable guarantees for both data point and feature unlearning.
Paper shows incorrectness of approximate unlearning definitions and challenges exact unlearning verification.
problem Incorrectness of approximate unlearning definitions and challenges in verifying exact unlearning.
method Analysis of machine unlearning approaches, including exact and approximate methods.
result Unlearning is only well-defined at the algorithmic level, and auditable claims are limited.
New algorithm learns and unlearns from streaming data efficiently.
problem Continuous learning and unlearning from production data streams.
method Translated batch unlearning techniques to online setting using regret, sample complexity, and deletion capacity.
result Achieved logarithmic regret bound of O(lnT) for online unlearning. Paper proposes ManiF-SMC for effective approximate machine unlearning.
problem Limited unlearning effectiveness and potential to undermine original learning objectives.
method Reformulates approximate unlearning as pushing erased samples towards semantic neighbors in retained data, using a margin-based triplet loss.
result Achieves unlearning effectiveness comparable to state-of-the-art methods while operating purely in representation space.
The paper analyzes the efficiency of unlearning methods and establishes bounds for minimax computation times.
problem Efficiency of removing specific data points from a trained model without full retraining.
method Analysis of unlearning methods and establishment of upper and lower bounds on computation times.
result A phase diagram for the unlearning complexity ratio, revealing three regimes of feasibility.
New techniques extend certified unlearning to deep neural networks.
problem Applying certified unlearning to deep neural networks (DNNs) is challenging due to their nonconvex nature.
method Developed simple techniques and an efficient computation method for nonconvex objectives, considering nonconvergence training and sequential unlearning.
result Demonstrated the efficacy of the method on real-world datasets, showing advantages of certified unlearning in DNNs.
Improved unlearning in LLMs using contrastive decoding.
problem Removing specific information from LLMs while preserving overall performance.
method Inference-time unlearning algorithm using contrastive decoding with two auxiliary models.
result Notable gains in forget quality and retained performance compared to prior approaches.
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.
New algorithms help machines forget old data efficiently.
problem Machine learning models can retain old data, hindering new learning.
method Developed TV-stable algorithms based on noisy SGD for convex and non-convex functions.
result Achieved efficient unlearning with upper and lower bounds on risk.
The paper tackles machine unlearning by designing efficient algorithms for adaptive query classes.
problem Designing efficient unlearning algorithms for machine learning models.
method Formalizes the problem and gives efficient unlearning algorithms for linear and prefix-sum query classes.
result Improved guarantees for stochastic convex optimization with reduced unlearning query complexity.
Machine unlearning can compromise privacy, study shows.
problem Machine unlearning may leave data imprints in ML models, risking privacy.
method Proposed a membership inference attack to detect leakage.
result Machine unlearning can lead to unintended privacy risks.
GraphEraser improves unlearning efficiency for graph data.
problem Improving unlearning efficiency for graph data.
method Two novel graph partition algorithms and a learning-based aggregation method.
result Achieves up to 35.94× unlearning time improvement on large datasets.
Paper proposes machine unlearning method to forget user data from neural networks.
problem Memorization of user data in neural networks violates GDPR's right to be forgotten.
method Proposes Forsaken method to measure and achieve high forgetting rates without significant accuracy loss.
result Forsaken method achieves over 90% forgetting rate with less than 5% accuracy loss.
Proposes a new method to unlearn from specific data points in conformal predictors.
problem Challenges of existing unlearning methods in conformal predictors.
method Formalizes conformal unlearning, introduces practical metrics, and presents an optimization algorithm.
result Demonstrates effective removal of targeted information while preserving utility.
This research debiases machine unlearning by using counterfactual examples.
problem Machine unlearning processes can be biased, leading to inaccurate results.
method Intervention-based approach using counterfactual examples to mitigate biases.
result The method outperforms existing baselines on evaluation metrics.
New approach protects privacy of deleted records in machine learning.
problem Privacy of deleted records in machine learning models.
method Sound deletion guarantee and noisy gradient descent algorithm.
result Privacy of existing records is necessary for deleted records' privacy.
A new method for forgetting data from trained models using information theory.
problem Efficiently forgetting private or copyrighted data from trained machine learning models.
method An information-theoretic approach to zero-shot unlearning, minimizing gradient smoothing.
result Our method successfully unlearns data while maintaining model performance.
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.
A framework for certified unlearning in decentralized federated learning.
problem Privacy-preserving machine learning in decentralized federated learning.
method Newton-style updates to quantify and correct data influence, using Fisher information matrices for scalability.
result The proposed framework ensures that the unlearned model is difficult to distinguish from a retrained model without the deleted data.
Paper analyzes fine-tuning methods for machine unlearning, proposing a new strategy to improve forgetting accuracy.
problem Improving fine-tuning methods to effectively forget specific subsets of data in machine learning models.
method Theoretical analysis and a novel Retention-Based Masking (RBM) strategy are proposed.
result RBM significantly improves unlearning accuracy while preserving retaining accuracy.
Second-order optimizers retain residual information after data deletion, affecting machine unlearning.
problem Residual information in second-order optimizers after data deletion.
method Comparison of first-order and second-order learners, eigendecomposition analysis.
result Second-order optimizers retain residual information, not detectable by first-order analysis.
Gradient ascent method successfully removes specific data points from neural networks without retraining.
problem Addressing privacy and ethical concerns by removing specific data points from trained models.
method Gradient ascent approach to unlearning, leveraging the implicit bias of gradient descent towards margin maximization conditions.
result Gradient ascent method can successfully unlearn specific data points from two-layer ReLU neural networks without retraining.
Paper proposes first unlearning algorithm for MCMC models.
problem Enforcing right to be forgotten in AI causes high costs for data deletion.
method Converts MCMC unlearning to explicit optimization problem, designs MCMC influence function.
result MCMC unlearning does not compromise generalizability of models.
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.
New algorithms delete user data from machine learning models efficiently.
problem Deleting user data from machine learning models trained with empirical risk minimization.
method Developed an online unlearning algorithm using the infinitesimal jackknife, targeting non-smooth regularizers.
result Empirically improved runtime while maintaining memory requirements and test accuracy.
New method for certified unlearning reduces noise injection.
problem Achieving formal unlearning guarantees with adaptive noise calibration.
method Adaptive per-instance noise calibration based on individual data point sensitivities.
result Derivation of high-probability per-instance sensitivity bounds for ridge regression.
The study tackles forgery in machine unlearning, showing that forging is limited and can be detected.
problem Adversarial crafting of data to mimic model behavior without removing information.
method Developed a framework to analyze ε-forging sets and proved their measure decay. result The forging set measure decays as ε(d−r)/2, providing evidence against false unlearning claims. The Unlearning algorithm improves neural network performance in memory tasks.
problem Improving neural network performance in memory tasks.
method Simplified Unlearning algorithm, structured training data, and new regularization technique for Boltzmann Machines.
result The Unlearning rule outperforms traditional Boltzmann-Machine learning in learning hidden probability distributions.
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.
Proposes practical kernel tests for f-divergences with theoretical guarantees.
problem Two-sample testing and machine unlearning evaluation.
method Regularized f-divergence kernel tests, adaptive to hyperparameters. result Different f-divergences highlight localized differences. We analyze why some models resist unlearning using linear stability theory.
problem Understanding and predicting when machine learning models resist unlearning.
method Linear stability theory applied to machine learning models, focusing on data coherence and optimization dynamics.
result Data coherence and signal-to-noise ratio (SNR) influence unlearning resistance; lower SNR makes unlearning easier.
Study reveals conditions for neural networks to forget learned features.
problem Understanding feature unlearning in neural networks.
method Infinite-width limit analysis with stochastic gradient descent, fast-slow dynamics.
result Conditions for feature unlearning are determined by the strength of nonlinear terms and initial weights.
A new algorithm for efficiently removing specific classes from a model without retraining.
problem Class forgetting in machine learning models.
method Estimating retain and forget spaces using SVD, removing shared information, and updating weights.
result Achieved up to 1.38% accuracy improvement on ImageNet dataset with minimal samples.
Parallel unlearning framework for inherited models reduces computational overhead.
problem Challenges in unlearning complex, evolving model networks.
method Chronologically Directed Acyclic Graph (DAG) and Fisher Inheritance Unlearning (FIUn) method.
result Significant reduction in computational overhead and efficient parallel unlearning.
New approach removes data influence in high dimensions with single step.
problem Efficiently removing data influence in high-dimensional settings with strong convexity and smoothness assumptions.
method Introduces ε-Gaussian certifiability and analyzes Newton method performance.
result Single Newton step followed by Gaussian noise achieves privacy and accuracy.
Unified framework for unlearning in diffusion models using KL divergence and likelihood constraints.
problem Removing undesirable data or concepts while preserving utility of pretrained models.
method Constrained optimization framework based on reverse and forward KL divergences, and likelihood constraints.
result Our KL-constrained approach achieves superior retention-unlearning tradeoffs compared to weight-based baselines.
AutoAnchor uses cross-attention to improve text-to-image model unlearning.
problem Mitigating harmful or copyrighted content in text-to-image models.
method Two-stage framework that automatically synthesizes manifold-proximal anchors using cross-attention consistency loss.
result Effective robust and unbiased unlearning across various baselines.
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.
SAP corrects model for label noise by identifying and removing noisy samples.
problem Label corruption degrades model performance; acquiring perfect labels is costly.
method SAP uses SVD to identify and project model weights onto a clean activation space.
result SAP improves model generalization by up to 6% on CIFAR dataset with 25% synthetic corruption.
CUTS removes corruption from models without clean data, improving utility and security.
problem Removing corruption from models without access to clean training data.
method CUTS uses a proxy set to amplify corruption and subtract it from model weights.
result CUTS recovers a large fraction of lost utility and nearly eliminates attacks with minimal damage.
Recently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used, and in particular a "right to be forgotten". This poses a challenge to machine learning: how to proceed when an individual retracts permission to use data which has been part of the training process…
The paper tackles approximate unlearning from a subset of training data using variational inference.
problem Unlearning from a small subset of erased training data while maintaining the posterior belief from the full data.
method Formulates unlearning as minimizing KL divergence, equivalent to minimizing an evidence upper bound. Uses variational inference to approximate posterior beliefs and proposes two tricks to handle challenges.
result Demonstrates the effectiveness of the proposed unlearning methods on various Bayesian models.
We propose a framework for verifying data deletion in MLaaS systems.
problem Ensuring compliance with data deletion requests in MLaaS systems.
method Formal framework based on hypothesis testing, novel backdoor-based verification mechanism.
result Demonstrated high confidence in certifying data deletion with minimal impact on ML service accuracy.
NPO method improves LLM unlearning without catastrophic collapse.
problem Efficiently unlearning undesirable data from LLMs without losing model utility.
method Negative Preference Optimization (NPO) method based on alignment.
result NPO-based methods achieve better unlearning results and maintain model utility.
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.
A new model for learning and unlearning predictors efficiently.
problem Learning and unlearning predictors from subsets of data.
method Proposes a ticketed model for learning-unlearning, allowing examples to present tickets for unlearning.
result Space-efficient schemes for various concept classes, including thresholds and parities.