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版本更新 · v2arXiv:2605.05871

Retain-Neutral Surrogates for Min-Max Unlearning

Junhao Cai, Dohun Kim, Dowon Kim, Sung Il Choi, Chengjun Jin, Juhyun Park, Changhee Joo

原文摘要

Machine unlearning aims to make a model forget designated training data while retaining performance on the rest. In min-max unlearning, an inner adversary perturbs the model to maximize the forget loss, and the model is updated to minimize the retain loss at the resulting surrogate point. When the forget and retain gradients are positively coupled, the standard perturbation also moves toward higher retain loss, and the retain step begins by recovering that loss. We propose Retain-Orthogonal Surrogate Unlearning (ROSU), which redefines the adversary to admit only perturbations orthogonal to the retain gradient. The one that raises the forget loss fastest has a closed form, and ROSU uses it to set the surrogate point and amplifies it into the forgetting step. Under positive coupling the ROSU surrogate avoids the retain cost that coupling induces in the standard surrogate, and the difference shrinks as the gradients approach orthogonality. ROSU has the smallest accuracy gap to retraining in every vision setting (CIFAR-10, CIFAR-100, Tiny-ImageNet) and is competitive with the baselines on language benchmarks (TOFU, WMDP). The redefinition helps most under high positive coupling. Code is available at https://github.com/unlearn-ml/machine-unlearning.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13491

Data-Efficient Unlearning-Based Backdoor Detection in Federated Learning

Dowon Kim, Junhao Cai, Sung Il Choi, Dohun Kim, Juhyun Park, Chengjun Jin, Changhee Joo

原文摘要

Detecting backdoor attacks in federated learning (FL) is challenging because malicious clients can manipulate the global model while preserving main-task performance. Static model-update statistics may therefore fail to clearly distinguish malicious from benign clients. Unlearning-based detectors instead probe client models through loss ascent on clean server data, but detection performance degrades with scarce proxy data. We propose Screening via Unlearning Response Analysis (SURA), a data-efficient detector. SURA reuses the same small clean proxy across multiple train-mode loss-ascent steps, strengthening client responses without additional clean data. For federated vision models with batch normalization (BN), repeated probing also changes BN running statistics and can confound endpoint measurements. SURA addresses this by restoring the shared pre-probe BN state only at readout while preserving the parameter changes induced by probing. This separates repeated probing from response measurement and removes BN drift and the interaction between parameter and BN-state changes from the readout. Across six backdoor attacks, SURA uses only 10 clean server images, outperforms methods under the same data budget, and approaches the robustness obtained with much larger proxy sets.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01724

Beyond Imputation: Mask-Adaptive Conformal Prediction via Tree Embeddings on General Missing Data Mechanisms

Jiarong Fan, Juhyun Park, Thi Phuong Thuy Vo, Nicolas J-B. Brunel

原文摘要

Conformal prediction (CP) provides a distribution-free framework for uncertainty quantification with finite-sample marginal coverage guarantees. Yet, missing values introduce distributional heterogeneity, where marginal coverage can hide systematic undercoverage for certain missingness patterns. At the same time, existing methods based on distances or smoothness require a continuous representation of partially observed covariates. Although imputation restores continuity, it obscures the missingness patterns and introduces task-dependent overhead. We develop a new CP framework that is \emph{adaptive to missingness patterns}, without imputing covariates. We rely on data augmentation by feature embedding that accounts for missingness. Our framework represents each input with missing values by a tree embedding together with its raw missingness mask, and then learns a regularized quantile score function. Varying the choices for the penalty function and the augmentation enables us to characterize three practically relevant validity levels: exact coordinate-wise validity, exact validity on observed masks, and approximate missingness-conditional guarantees for general mechanisms (MCAR, MAR, MNAR). Experiments on synthetic and real datasets support these guarantees and show that the proposed method reduces miscoverage risk while maintaining informative intervals, especially in non-MCAR settings. These results highlight the practical value of uncertainty quantification that adapts to missingness.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.14221

Mask-Conditional Conformal Prediction: Valid Uncertainty For All Missing Data Mechanisms

Jiarong Fan, Juhyun Park. Thi Phuong Thuy Vo, Nicolas Brunel

原文摘要

Conformal prediction (CP) offers a principled framework for uncertainty quantification, but it fails to guarantee mask-conditional coverage when faced with missing covariates. In addressing the heterogeneity induced by various missing patterns, Mask-Conditional Valid (MCV) Coverage has emerged as a more desirable property than Marginal Coverage. In this work, we adapt split CP to handle missing values by proposing a preimpute-mask-then-correct framework that can offer valid coverage. We show that our method provides guaranteed Marginal Coverage and Mask-Conditional Validity for general missing data mechanisms. A key component of our approach is a reweighted conformal prediction procedure that corrects the prediction sets after distributional imputation (multiple imputation) of the calibration dataset, making our method compatible with standard imputation pipelines. We derive two algorithms and prove that they achieve both marginal validity and MCV. We evaluate them on synthetic and real-world datasets. It reduces significantly the width of prediction intervals w.r.t standard MCV methods, while maintaining the target guarantees.

stat.ML · cs.LG

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arXiv ↗PDF ↗