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ddefb60 | 1 | {"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": 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"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 substantially higher utility at the same target \u03b5, while remaining 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"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": 0.00026530027389526367}, "created": {"value": false, "score": 9.179115295410156e-06}, "shared": 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