{"application": "software-mentions", "version": "0.9.0-SNAPSHOT", "revision": "54efbc6", "date": "2026-10-14T14:45+0000", "md5": "2DA8B66042F50BFCD51FAB4A4BDBFE54", "biblio": {"title": "Certified Per-Instance Unlearning Using Individual Sensitivity Bounds", "authors": "Hanna Benarroch, Jamal Atif, Olivier Capp\u00e9"}, "mentions": [{"type": "software", "software-type": "software", "software-name": {"rawForm": "Writing", "normalizedForm": "Writing", "offsetStart": 0, "offsetEnd": 7}, "context": "Writing", "mentionContextAttributes": {"used": {"value": false, "score": 0.003923356533050537}, "created": {"value": false, "score": 0.002301514148712158}, "shared": {"value": false, "score": 1.5497207641601562e-06}}, "documentContextAttributes": {"used": {"value": false, "score": 0.3017733693122864}, "created": {"value": false, "score": 0.002301514148712158}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "Writing", "normalizedForm": "Writing", "offsetStart": 0, "offsetEnd": 7}, "context": "Writing \u03b8 k for the parameter at iteration k, the update takes the form", "mentionContextAttributes": {"used": {"value": false, "score": 0.3017733693122864}, "created": {"value": false, "score": 5.078315734863281e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": false, "score": 0.3017733693122864}, "created": {"value": false, "score": 0.002301514148712158}, "shared": {"value": false, "score": 1.5497207641601562e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MNIST", "normalizedForm": "MNIST", "offsetStart": 3, "offsetEnd": 8}, "context": "On MNIST, a single run of Algorithm 1 with n = 4000, T = 300, and K = 20 takes approximately 0.55 seconds wall-clock (\u2248 9.1 \u00d7 10 -3 minutes).", "mentionContextAttributes": {"used": {"value": false, "score": 0.021118879318237305}, 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"context": "for the code; CC-BY-4.0 for the released model assets), and a torchvision ResNet-50 pre-trained on ImageNet (BSD-3-Clause; ImageNet terms of use for research).", "mentionContextAttributes": {"used": {"value": true, "score": 0.9886333346366882}, "created": {"value": false, "score": 1.3947486877441406e-05}, "shared": {"value": false, "score": 5.960464477539062e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9886333346366882}, "created": {"value": false, "score": 1.3947486877441406e-05}, "shared": {"value": false, "score": 5.960464477539062e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MNIST", "normalizedForm": "MNIST", "offsetStart": 11, "offsetEnd": 16}, "context": "We use the MNIST dataset, consisting of 60,000 training images and 10,000 test images of handwritten digits in 10 classes.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9993098974227905}, "created": {"value": false, "score": 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"software", "software-type": "software", "software-name": {"rawForm": "PILU", "normalizedForm": "PILU", "offsetStart": 15, "offsetEnd": 19}, "context": "In contrast to PILU, this baseline does not adapt the privacy cost to the specific point being removed.", "mentionContextAttributes": {"used": {"value": false, "score": 0.00016260147094726562}, "created": {"value": false, "score": 0.00010633468627929688}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999426603317261}, "created": {"value": false, "score": 0.14887398481369019}, "shared": {"value": false, "score": 4.76837158203125e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "PILU", "normalizedForm": "PILU", "offsetStart": 27, "offsetEnd": 31}, "context": "We then proceed to compare PILU against two complementary baselines.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9999426603317261}, "created": {"value": false, "score": 1.4662742614746094e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999426603317261}, "created": {"value": false, "score": 0.14887398481369019}, "shared": {"value": false, "score": 4.76837158203125e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "PILU", "normalizedForm": "PILU", "offsetStart": 30, "offsetEnd": 34}, "context": "For a fair comparison, we use PILU with the same training horizon T , only one unlearning step (K = 1) and no additional noise (\u03c3 learn = \u03c3 unlearn ). Figure 5b, which compares the final unlearning guarantees \u03b5 at fixed \u03b4 = 1/n, shows that the two methods have opposite behaviors as T varies: the Newton baseline becomes competitive only for sufficiently large T , consistent with the fact that its correction is exact only at the LS solution (T \u2192 \u221e).", "mentionContextAttributes": {"used": {"value": true, "score": 0.9979625940322876}, "created": {"value": false, "score": 3.2186508178710938e-06}, "shared": {"value": false, "score": 4.76837158203125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999426603317261}, "created": {"value": false, "score": 0.14887398481369019}, "shared": {"value": false, "score": 4.76837158203125e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "PILU", "normalizedForm": "PILU", "offsetStart": 34, "offsetEnd": 38}, "context": "The cyclic minibatch extension of PILU with batch size b = 512 achieves a similar privacy-utility trade-off with an unlearning budget of only slightly more than one pass over the dataset, highlighting the computational advantage of minibatching; see Figure 10b in Appendix F.", "mentionContextAttributes": {"used": {"value": false, "score": 0.00027316808700561523}, "created": {"value": false, "score": 1.6450881958007812e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999426603317261}, "created": {"value": false, "score": 0.14887398481369019}, "shared": {"value": false, "score": 4.76837158203125e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MNIST", "normalizedForm": "MNIST", "offsetStart": 38, "offsetEnd": 43}, "context": "Figure 12: Per-instance unlearning on MNIST.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9916479587554932}, "created": {"value": false, "score": 1.1444091796875e-05}, "shared": {"value": false, "score": 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39910, "offsetEnd": 39930}]}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "PILU", "normalizedForm": "PILU", "offsetStart": 49, "offsetEnd": 53}, "context": "Figure 3b shows the privacy-utility trade-off of PILU.", "mentionContextAttributes": {"used": {"value": true, "score": 0.5927920341491699}, "created": {"value": false, "score": 3.123283386230469e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999426603317261}, "created": {"value": false, "score": 0.14887398481369019}, "shared": {"value": false, "score": 4.76837158203125e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "PILU", "normalizedForm": "PILU", "offsetStart": 71, "offsetEnd": 75}, "context": "As shown in Figure 5a, DP-GD yields a single global trade-off, whereas PILU produces markedly different curves across removed points: for easy-to-unlearn points, it achieves 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"software", "software-type": "software", "software-name": {"rawForm": "PILU", "normalizedForm": "PILU", "offsetStart": 88, "offsetEnd": 92}, "context": "Against uniform DP-GD, we recover the same qualitative conclusion as in the main paper: PILU achieves a better privacy-utility tradeoff, with the largest gains on the most influential points.", "mentionContextAttributes": {"used": {"value": false, "score": 0.03509300947189331}, "created": {"value": false, "score": 0.14887398481369019}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999426603317261}, "created": {"value": false, "score": 0.14887398481369019}, "shared": {"value": false, "score": 4.76837158203125e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "DINOv2", "normalizedForm": "DINOv2", "offsetStart": 115, "offsetEnd": 121}, "context": "We consider dense depth regression on NYU-Depth V2 (Silberman et al., 2012) with a fixed-feature pipeline based on DINOv2 patch and class tokens (Oquab et al., 2024).", "mentionContextAttributes": {"used": {"value": false, "score": 0.005176186561584473}, "created": {"value": false, "score": 7.62939453125e-06}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9950425624847412}, "created": {"value": false, "score": 7.62939453125e-06}, "shared": {"value": false, "score": 2.0265579223632812e-06}}, "references": [{"label": "(Oquab et al., 2024)", "normalizedForm": "Oquab et al., 2024", "refKey": 29}]}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "PILU", "normalizedForm": "PILU", "offsetStart": 141, "offsetEnd": 145}, "context": "Using Theorem 3.3 yields an explicit and fully implementable procedure for per-instance Langevin unlearning in the case of ridge regression (PILU).", "mentionContextAttributes": {"used": {"value": false, "score": 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"documentContextAttributes": {"used": {"value": true, "score": 0.9999426603317261}, "created": {"value": false, "score": 0.14887398481369019}, "shared": {"value": false, "score": 4.76837158203125e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "MNIST", "normalizedForm": "MNIST", "offsetStart": 197, "offsetEnd": 202}, "context": "Existing assets are credited in the paper and in this appendix: NYU-Depth (Silberman et al., 2012) (original dataset; the official dataset page does not specify a standard redistribution license), MNIST (Lecun et al., 1998) (CC BY-SA 3.0), CIFAR-10 ( Krizhevsky and Hinton, 2009) (research use, no formal license), DINOv2 (Oquab et al., 2024) (Apache-2.0", "mentionContextAttributes": {"used": {"value": true, "score": 0.992348313331604}, "created": {"value": false, "score": 2.384185791015625e-06}, "shared": {"value": false, "score": 8.344650268554688e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 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   (research use, no formal license), DINOv2                      (Apache-2.0", "mentionContextAttributes": {"used": {"value": true, "score": 0.9305328726768494}, "created": {"value": false, "score": 2.384185791015625e-06}, "shared": {"value": false, "score": 2.0265579223632812e-06}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9305328726768494}, "created": {"value": false, "score": 2.384185791015625e-06}, "shared": {"value": false, "score": 2.0265579223632812e-06}}}], "references": [{"refKey": 22, "tei": "<biblStruct status=\"consolidated\" source=\"crossref\" xml:id=\"b22\">\n\t<analytic>\n\t\t<title level=\"a\" type=\"main\">Gradient-based learning applied to document recognition</title>\n\t\t<author>\n\t\t\t<persName><forename type=\"first\">Yann</forename><surname>Lecun</surname></persName>\n\t\t</author>\n\t\t<author>\n\t\t\t<persName><forename 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