Epasto, Alessandro

24 publications

ICML 2025 Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model Rachel Cummings, Alessandro Epasto, Jieming Mao, Tamalika Mukherjee, Tingting Ou, Peilin Zhong

ICML 2025 Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures Alina Ene, Alessandro Epasto, Vahab Mirrokni, Hoai-An Nguyen, Huy Nguyen, David Woodruff, Peilin Zhong

ICML 2025 Retraining with Predicted Hard Labels Provably Increases Model Accuracy Rudrajit Das, Inderjit S Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong

ICML 2025 Scalable Private Partition Selection via Adaptive Weighting Justin Y. Chen, Vincent Cohen-Addad, Alessandro Epasto, Morteza Zadimoghaddam

NeurIPS 2025 Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Adel Javanmard, Rudrajit Das, Alessandro Epasto, Vahab Mirrokni

AISTATS 2024 A Scalable Algorithm for Individually Fair K-Means Clustering MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi

ICML 2024 Perturb-and-Project: Differentially Private Similarities and Marginals Vincent Cohen-Addad, Tommaso D’Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong

NeurIPS 2023 $k$-Means Clustering with Distance-Based Privacy Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong

NeurIPSW 2023 A New Framework for Measuring Re-Identification Risk Cj Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Munoz Medina, Vahab Mirrokni, Gabriel Nunes, Sergei Vassilvitskii, Peilin Zhong

ICMLW 2023 Differentially Private Clustering in Data Streams Alessandro Epasto, Tamalika Mukherjee, Peilin Zhong

ICML 2023 Differentially Private Hierarchical Clustering with Provable Approximation Guarantees Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni

ICMLW 2023 K-Means Clustering with Distance-Based Privacy Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong

NeurIPS 2023 Private Estimation Algorithms for Stochastic Block Models and Mixture Models Hongjie Chen, Vincent Cohen-Addad, Tommaso d’Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel

NeurIPS 2022 Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong

NeurIPSW 2022 Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong

NeurIPS 2022 Near-Optimal Private and Scalable $k$-Clustering Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong

NeurIPSW 2022 Scalable and Improved Algorithms for Individually Fair Clustering Mohammadhossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi

AISTATS 2020 Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection Vaggos Chatziafratis, Grigory Yaroslavtsev, Euiwoong Lee, Konstantin Makarychev, Sara Ahmadian, Alessandro Epasto, Mohammad Mahdian

AISTATS 2020 Fair Correlation Clustering Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian

NeurIPS 2020 Fair Hierarchical Clustering Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang

NeurIPS 2020 Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Emmanouil Zampetakis

NeurIPS 2020 Sliding Window Algorithms for K-Clustering Problems Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam

NeurIPS 2020 Smoothly Bounding User Contributions in Differential Privacy Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Lijie Ren

AAAI 2016 Reconstructing Hidden Permutations Using the Average-Precision (AP) Correlation Statistic Lorenzo De Stefani, Alessandro Epasto, Eli Upfal, Fabio Vandin