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Proof-of-Learning: Definitions and Practice., , , , , , and . SP, page 1039-1056. IEEE, (2021)Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD., , , , and . USENIX Security Symposium, USENIX Association, (2024)Training Private Models That Know What They Don't Know., , , , and . CoRR, (2023)On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning., , , and . USENIX Security Symposium, page 4007-4022. USENIX Association, (2022)Unlearnable Algorithms for In-context Learning., , , and . CoRR, (2024)On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning., , , , , , , and . CoRR, (2022)Better Sparsifiers for Directed Eulerian Graphs., , and . CoRR, (2023)Better Sparsifiers for Directed Eulerian Graphs., , and . ICALP, volume 297 of LIPIcs, page 119:1-119:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2024)Proof-of-Learning is Currently More Broken Than You Think., , , , , , , and . EuroS&P, page 797-816. IEEE, (2023)Unrolling SGD: Understanding Factors Influencing Machine Unlearning., , , and . EuroS&P, page 303-319. IEEE, (2022)