Tackling Fake Forgetting through Uncertainty Quantification

Kaize Ding; Ren Wang; Sijia Liu; Yingdan Shi

arxiv: 2501.19403 · v3 · submitted 2025-01-31 · 💻 cs.LG · cs.AI

Tackling Fake Forgetting through Uncertainty Quantification

Yingdan Shi , Sijia Liu , Kaize Ding , Ren Wang This is my paper

Pith reviewed 2026-05-23 03:54 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords machine unlearningconformal predictionfake forgettinguncertainty quantificationCarlini Wagner attackimage classification

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The pith

Unlearning accuracy misses fake forgetting because ground truth labels remain in conformal prediction sets.

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

Machine unlearning removes specified data influence from models, but standard accuracy metrics fail to confirm true forgetting. The paper shows that points misclassified after unlearning often keep their correct labels inside conformal prediction sets, a problem called fake forgetting. This observation comes from an uncertainty quantification view. The authors introduce the CR metric, drawn from conformal prediction ideas, to judge forgetting quality more reliably. They also build the CPU framework that folds conformal prediction into an adversarial attack loss so the ground truth label gets removed from the prediction set.

Core claim

Forgetting data points that appear removed by unlearning accuracy still have their ground truth labels included in the conformal prediction set, which the authors term fake forgetting. The CR metric provides a more reliable assessment of forgetting quality. The CPU framework incorporates conformal prediction into the Carlini & Wagner adversarial attack loss, enabling the ground truth label to be effectively removed from the conformal prediction set.

What carries the argument

The conformal prediction set, whose inclusion of the ground truth label after unlearning signals that the point has not been truly forgotten.

If this is right

Unlearning performance can be assessed with the CR metric rather than accuracy alone.
The CPU framework achieves forgetting where the ground truth label is removed from the conformal prediction set.
The approach applies to image classification tasks and yields measurable improvements in forgetting quality.
Conformal prediction can be integrated into adversarial unlearning losses to enforce stronger removal conditions.

Where Pith is reading between the lines

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

Models trained with CPU may offer stronger practical privacy guarantees against data reconstruction attacks that rely on retained label information.
The CR metric could serve as a diagnostic tool in other unlearning pipelines that currently rely only on accuracy or membership inference.
Extending the same conformal-set removal idea beyond image tasks might reveal similar fake-forgetting issues in language or tabular models.

Load-bearing premise

Inclusion of the ground truth label in the conformal prediction set after unlearning means the data point has not been truly forgotten.

What would settle it

A direct test on held-out forgetting points showing whether the CPU framework produces conformal sets that exclude the ground truth label at a higher rate than standard unlearning methods while preserving model utility on other data.

read the original abstract

Machine unlearning seeks to remove the influence of specified data from a trained model. While the unlearning accuracy provides a widely used metric for assessing unlearning performance, it falls short in assessing the reliability of forgetting. In this paper, we find that the forgetting data points misclassified by unlearning accuracy still have their ground truth labels included in the conformal prediction set from the uncertainty quantification perspective, leading to a phenomenon we term fake forgetting. To address this issue, we propose a novel metric CR, inspired by conformal prediction, that offers a more reliable assessment of forgetting quality. Building on these insights, we further propose an unlearning framework CPU that incorporates conformal prediction into the Carlini & Wagner adversarial attack loss, enabling the ground truth label to be effectively removed from the conformal prediction set. Through extensive experiments on image classification tasks, we demonstrate both the effectiveness of our proposed metric and the superior forgetting quality achieved by our framework. Code is available at https://github.com/TIML-Group/Conformal-Prediction-Unlearning.

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.

Desk Editor's Note private letter to a colleague

The abstract flags that unlearning accuracy can miss cases where labels stay in conformal sets after deletion, but offers no details to verify the proposed fix.

read the letter

This paper's main observation is that unlearning accuracy falls short because misclassified points can still keep their ground truth label inside the conformal prediction set. The authors call this fake forgetting and introduce a CR metric plus a CPU framework that folds conformal prediction into the Carlini-Wagner loss to push the label out of the set. The abstract presents both the observation and the proposals as new. It does a reasonable job highlighting why accuracy alone may not give reliable evidence of data removal, which matters when unlearning is used for privacy compliance. The connection to conformal prediction is a straightforward way to strengthen the check. The soft spots are straightforward. The abstract supplies no definition of the CR metric, no equations for how conformal scores modify the adversarial loss, no calibration procedure, and no experimental results or controls. Without those pieces it is impossible to tell whether the metric actually improves assessment or whether the framework delivers better forgetting. The core premise that label inclusion in the set equals incomplete forgetting is stated but not shown. This work is aimed at researchers in machine unlearning who care about verifiable deletion. A reader already familiar with conformal prediction or privacy regulations could pick up the idea quickly if the full paper supplies the missing steps. It deserves a serious referee to examine the derivations and experiments once they are available.

Referee Report

1 major / 0 minor

Summary. The manuscript identifies a limitation in standard unlearning accuracy for machine unlearning: misclassified forgetting data points may still include their ground-truth labels in conformal prediction sets, a phenomenon termed 'fake forgetting'. It proposes a new metric CR inspired by conformal prediction for more reliable forgetting assessment and an unlearning framework CPU that incorporates conformal prediction into the Carlini & Wagner adversarial attack loss to exclude the ground-truth label from the conformal set. The abstract states that extensive experiments on image classification tasks demonstrate the effectiveness of CR and the superior forgetting quality of CPU, with code released.

Significance. If the empirical observations and methods hold under full scrutiny, the work could strengthen machine unlearning evaluation by integrating uncertainty quantification, offering a more robust alternative or complement to accuracy-based metrics. The public code release supports reproducibility. However, the abstract alone provides no equations, derivations, experimental protocols, baselines, or quantitative results, so the potential impact cannot be assessed beyond the conceptual framing.

major comments (1)

[Abstract] Abstract: The central claim that misclassified unlearned points exhibit 'fake forgetting' via retention of ground-truth labels in conformal sets, and that CR and CPU resolve this, cannot be evaluated because the abstract supplies neither the definition of the CR metric, the exact integration of conformal scores into the C&W loss, the calibration procedure, nor any experimental details such as datasets, baselines, or quantitative improvements.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and for highlighting the need for clarity in evaluating the central claims. We address the comment on the abstract below.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that misclassified unlearned points exhibit 'fake forgetting' via retention of ground-truth labels in conformal sets, and that CR and CPU resolve this, cannot be evaluated because the abstract supplies neither the definition of the CR metric, the exact integration of conformal scores into the C&W loss, the calibration procedure, nor any experimental details such as datasets, baselines, or quantitative improvements.

Authors: We agree that the abstract, as a concise summary, does not contain the full technical definitions, equations, calibration details, or experimental protocols. These elements are developed in the main manuscript: the CR metric is formally defined in Section 3, the incorporation of conformal prediction scores into the Carlini-Wagner loss is specified in Section 4, the calibration procedure is described in Section 3.2, and the experimental setup (datasets, baselines, and quantitative results) appears in Section 5. This separation follows standard academic practice to keep the abstract accessible while providing complete details in the body. If the referee finds the abstract insufficient even as a high-level overview, we can revise it to include one or two additional sentences on the key mechanisms. revision: no

Circularity Check

0 steps flagged

No significant circularity

full rationale

Only the abstract is available, which contains no equations, derivations, fitted parameters, or self-citations. The CR metric is described as 'inspired by conformal prediction' rather than constructed from the same unlearning data or reducing to a fit by definition. The 'fake forgetting' label is attached to an empirical observation about conformal sets but does not create a self-definitional loop or rename a known result as a new derivation. The CPU framework is presented as an incorporation of existing conformal ideas into an adversarial loss, with no load-bearing premise that collapses to the paper's own inputs. This is the normal case of a self-contained proposal without internal circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the central claims rest on the unstated premise that conformal prediction sets provide an independent signal of retained information.

pith-pipeline@v0.9.0 · 5679 in / 1111 out tokens · 66754 ms · 2026-05-23T03:54:18.009933+00:00 · methodology

discussion (0)

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