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Differentially Private Call Auctions and Market Impact., , , , , and . EC, page 541-583. ACM, (2020)A New Analysis of Differential Privacy's Generalization Guarantees, , , , , and . (2019)cite arxiv:1909.03577.Descent-to-Delete: Gradient-Based Methods for Machine Unlearning., , and . ALT, volume 132 of Proceedings of Machine Learning Research, page 931-962. PMLR, (2021)Adaptive Machine Unlearning., , , , , and . NeurIPS, page 16319-16330. (2021)A New Analysis of Differential Privacy's Generalization Guarantees., , , , , and . ITCS, volume 151 of LIPIcs, page 31:1-31:17. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2020)Algorithms and Learning for Fair Portfolio Design., , , , , , , and . EC, page 371-389. ACM, (2021)A new analysis of differential privacy's generalization guarantees (invited paper)., , , , , and . STOC, page 9. ACM, (2021)Differentially Private Fair Learning., , , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 3000-3008. PMLR, (2019)Analytical studies of fragmented-spectrum multi-level OFDM-CDMA technique in cognitive radio networks., , , and . UEMCON, page 1-6. IEEE, (2016)Average Individual Fairness: Algorithms, Generalization and Experiments., , and . NeurIPS, page 8240-8249. (2019)