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arxiv: 2411.02622 · v3 · submitted 2024-11-04 · 💻 cs.LG · cs.AI

AdaProb: Efficient Machine Unlearning via Adaptive Probability

Zihao Zhao , Yuchen Yang , Anjalie Field , Yinzhi Cao This is my paper

Pith reviewed 2026-05-23 17:10 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords machine unlearningadaptive probabilitypseudo-probabilitiesmembership inference attacksforgetting errorprivacy preservationneural network weightsdata removal
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The pith

AdaProb replaces final-layer probabilities with optimized uniform pseudo-probabilities to enable efficient machine unlearning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces AdaProb to solve two problems in machine unlearning: leftover information about removed data and high computational cost. It works by swapping the final output probabilities for data to be forgotten with uniform pseudo-probabilities that are tuned to match the model's overall distribution. Weights are then updated to reflect this change. Experiments show this yields over 20 percent better forgetting error, stronger resistance to membership inference attacks, and under half the runtime of prior methods.

Core claim

By first replacing the neural network's final-layer output probabilities with pseudo-probabilities for data to be forgotten that follow a uniform distribution optimized to align with the model's overall distribution, and then updating the model's weights accordingly, AdaProb achieves effective data forgetting in a computationally efficient and privacy-preserving manner.

What carries the argument

Adaptive pseudo-probabilities: uniform distributions substituted for forgotten data and optimized to the model's distribution to guide weight updates without full retraining.

If this is right

  • Forgetting error improves by more than 20 percent over state-of-the-art unlearning methods.
  • Protection against membership inference attacks increases compared with prior approaches.
  • Computational time drops below 50 percent of existing methods while avoiding full retraining.
  • The approach satisfies privacy regulations such as GDPR right-to-be-forgotten requests without retraining from scratch.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The technique could apply to models trained on mixed public and private data where selective removal is needed after deployment.
  • If the optimization step scales linearly, AdaProb might support unlearning requests in large-scale production systems without dedicated hardware.
  • Combining the probability swap with existing regularization during initial training could further reduce the need for post-hoc unlearning.
  • Testing the method on transformer-based models would reveal whether the final-layer substitution generalizes beyond the architectures evaluated.

Load-bearing premise

Substituting final-layer probabilities with uniform pseudo-probabilities optimized only to match the model's overall distribution is sufficient to remove residual information about the forgotten data.

What would settle it

A membership inference attack achieving success rates above random guessing on data the model was instructed to forget after AdaProb is applied would show the method failed to remove residual information.

Figures

Figures reproduced from arXiv: 2411.02622 by Anjalie Field, Yinzhi Cao, Yuchen Yang, Zihao Zhao.

Figure 1
Figure 1. Figure 1: This is an overview of Pseudo-Probability Unlearning (PPU). In this approach, we extract [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Forget set error on selective unlearning with ALL-CNN on CIFAR-10 [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Time needed for the unlearning method (measured over 5 runs) [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
read the original abstract

Machine unlearning, enabling a trained model to forget specific data, is crucial for addressing erroneous data and adhering to privacy regulations like the General Data Protection Regulation (GDPR)'s "right to be forgotten". Despite recent progress, existing methods face two key challenges: residual information may persist in the model even after unlearning, and the computational overhead required for effective data removal is often high. To address these issues, we propose Adaptive Probability Approximate Unlearning (AdaProb), a novel method that enables models to forget data efficiently and in a privacy-preserving manner. Our method firstly replaces the neural network's final-layer output probabilities with pseudo-probabilities for data to be forgotten. These pseudo-probabilities follow a uniform distribution to maximize unlearning, and they are optimized to align with the model's overall distribution to enhance privacy and reduce the risk of membership inference attacks. Then, the model's weights are updated accordingly. Through comprehensive experiments, our method outperforms state-of-the-art approaches with over 20% improvement in forgetting error, better protection against membership inference attacks, and less than 50% of the computational time.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes AdaProb, an approximate machine unlearning method that first replaces the final-layer softmax probabilities for samples to be forgotten with optimized uniform pseudo-probabilities (chosen to match the model's global output distribution) and then performs a weight update. It claims this yields over 20% improvement in forgetting error versus SOTA, stronger resistance to membership inference attacks, and under 50% of the compute time of alternatives.

Significance. If the central performance claims are supported by rigorous experiments and the final-layer substitution is shown to be sufficient, the method could offer a practical, low-overhead unlearning technique for deep networks that balances privacy and efficiency. The distribution-matching step for pseudo-probabilities is a reasonable attempt to mitigate distribution-shift artifacts that could aid attacks.

major comments (2)
  1. [Method section] Method section (core procedure): replacing only final-layer outputs with uniform pseudo-probabilities implicitly assumes residual information about forgotten points resides solely in the output layer. No analysis or ablation is provided showing that hidden-layer representations are also altered or that membership inference via intermediate activations is prevented; this assumption is load-bearing for the privacy and forgetting-error claims.
  2. [Experiments section] Experimental evaluation (results tables/figures): the abstract states quantitative gains (>20% forgetting-error improvement, <50% compute time) but the provided text supplies no protocol details, baseline implementations, error bars, or statistical tests. Without these, the cross-method superiority claim cannot be evaluated.
minor comments (2)
  1. [Method section] Define the exact optimization objective and stopping criterion used to generate the pseudo-probabilities; the current description is high-level.
  2. Clarify the precise definition of 'forgetting error' metric and how it differs from standard unlearning metrics in the literature.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We respond point-by-point to the major comments below and indicate planned revisions.

read point-by-point responses

  1. Referee: [Method section] Method section (core procedure): replacing only final-layer outputs with uniform pseudo-probabilities implicitly assumes residual information about forgotten points resides solely in the output layer. No analysis or ablation is provided showing that hidden-layer representations are also altered or that membership inference via intermediate activations is prevented; this assumption is load-bearing for the privacy and forgetting-error claims.

    Authors: The method substitutes final-layer probabilities and then performs gradient-based weight updates on the resulting loss; these updates necessarily modify parameters in all preceding layers. We agree, however, that explicit verification is warranted. In revision we will add an ablation quantifying changes to hidden-layer activations and membership-inference performance when attacks are mounted on intermediate features. revision: yes

  2. Referee: [Experiments section] Experimental evaluation (results tables/figures): the abstract states quantitative gains (>20% forgetting-error improvement, <50% compute time) but the provided text supplies no protocol details, baseline implementations, error bars, or statistical tests. Without these, the cross-method superiority claim cannot be evaluated.

    Authors: We will expand the experimental section to include full protocol descriptions, baseline implementation details, error bars from multiple independent runs, and statistical significance tests supporting the reported gains. revision: yes

Circularity Check

0 steps flagged

No circularity detected; method is heuristic with empirical validation

full rationale

The paper describes a heuristic unlearning procedure (final-layer probability substitution followed by weight update) validated through experiments on forgetting error, MIA resistance, and runtime. No derivation chain, first-principles predictions, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. Claims rest on external experimental benchmarks rather than internal reductions, satisfying the self-contained criterion.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no equations, loss functions, or implementation details are provided, so no concrete free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.0 · 5724 in / 1137 out tokens · 58488 ms · 2026-05-23T17:10:03.689049+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · 2 internal anchors

  1. [1]

    Machine unlearning

    Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. Machine unlearning. In 2021 IEEE Symposium on Security and Privacy (SP), pp. 141–159. IEEE,

    work page 2021

  2. [2]

    Vggface2: A dataset for recognising faces across pose and age

    Qiong Cao, Li Shen, Weidi Xie, Omkar M Parkhi, and Andrew Zisserman. Vggface2: A dataset for recognising faces across pose and age. In 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018), pp. 67–74. IEEE,

    work page 2018

  3. [3]

    Towards making systems forget with machine unlearning

    Yinzhi Cao and Junfeng Yang. Towards making systems forget with machine unlearning. In 2015 IEEE symposium on security and privacy, pp. 463–480. IEEE,

    work page 2015

  4. [4]

    Knowledge removal in sampling-based bayesian inference

    Shaopeng Fu, Fengxiang He, and Dacheng Tao. Knowledge removal in sampling-based bayesian inference. arXiv preprint arXiv:2203.12964,

    work page arXiv

  5. [5]

    Towards adversarial evaluations for inexact machine unlearning

    Shashwat Goel, Ameya Prabhu, Amartya Sanyal, Ser-Nam Lim, Philip Torr, and Ponnurangam Kumaraguru. Towards adversarial evaluations for inexact machine unlearning. arXiv preprint arXiv:2201.06640,

    work page arXiv

  6. [6]

    Cer- tified data removal from machine learning models,

    Aditya Golatkar, Alessandro Achille, and Stefano Soatto. Eternal sunshine of the spotless net: Selective forgetting in deep networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9304–9312, 2020a. 10 Aditya Golatkar, Alessandro Achille, and Stefano Soatto. Forgetting outside the box: Scrubbing deep networks of ...

    work page arXiv 2020

  7. [7]

    Learn what you want to unlearn: Un- learning inversion attacks against machine unlearning

    Hongsheng Hu, Shuo Wang, Tian Dong, and Minhui Xue. Learn what you want to unlearn: Un- learning inversion attacks against machine unlearning. arXiv preprint arXiv:2404.03233,

    work page arXiv

  8. [8]

    Approximate data dele- tion from machine learning models

    Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou. Approximate data dele- tion from machine learning models. In International Conference on Artificial Intelligence and Statistics, pp. 2008–2016. PMLR,

    work page 2008

  9. [9]

    Ma- nipulating machine learning: Poisoning attacks and countermeasures for regression learning

    Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li. Ma- nipulating machine learning: Poisoning attacks and countermeasures for regression learning. In 2018 IEEE symposium on security and privacy (SP), pp. 19–35. IEEE,

    work page 2018

  10. [10]

    Deep unlearning: Fast and efficient training- free approach to controlled forgetting

    Sangamesh Kodge, Gobinda Saha, and Kaushik Roy. Deep unlearning: Fast and efficient training- free approach to controlled forgetting. arXiv preprint arXiv:2312.00761,

    work page arXiv

  11. [11]

    An Introduction to Convolutional Neural Networks

    Keiron O’Shea and Ryan Nash. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458,

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    Hedgecut: Maintaining randomised trees for low-latency machine unlearning

    Sebastian Schelter, Stefan Grafberger, and Ted Dunning. Hedgecut: Maintaining randomised trees for low-latency machine unlearning. In Proceedings of the 2021 International Conference on Management of Data, pp. 1545–1557,

    work page 2021

  13. [13]

    Unlearning via sparse representations

    Vedant Shah, Frederik Tr ¨auble, Ashish Malik, Hugo Larochelle, Michael Mozer, Sanjeev Arora, Yoshua Bengio, and Anirudh Goyal. Unlearning via sparse representations. arXiv preprint arXiv:2311.15268,

    work page arXiv

  14. [14]

    Striving for Simplicity: The All Convolutional Net

    11 Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806,

    work page internal anchor Pith review Pith/arXiv arXiv

  15. [15]

    Machine unlearning: Solutions and challenges

    Jie Xu, Zihan Wu, Cong Wang, and Xiaohua Jia. Machine unlearning: Solutions and challenges. arXiv preprint arXiv:2308.07061,

    work page arXiv