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- Alessandro Epasto
Google, New York, NY, USA
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Publication search results
found 66 matches
- 2024
- MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi:
A Scalable Algorithm for Individually Fair k-Means Clustering. AISTATS 2024: 3151-3159 - Alessandro Epasto
, Hossein Esfandiari
, Vahab Mirrokni
, Andrés Muñoz Medina
:
Smooth Anonymity for Sparse Graphs. WWW (Companion Volume) 2024: 621-624 - MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi:
A Scalable Algorithm for Individually Fair K-means Clustering. CoRR abs/2402.06730 (2024) - Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong:
Perturb-and-Project: Differentially Private Similarities and Marginals. CoRR abs/2406.04868 (2024) - Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong:
Retraining with Predicted Hard Labels Provably Increases Model Accuracy. CoRR abs/2406.11206 (2024) - 2023
- CJ Carey
, Travis Dick
, Alessandro Epasto
, Adel Javanmard
, Josh Karlin
, Shankar Kumar
, Andres Muñoz Medina
, Vahab Mirrokni
, Gabriel Henrique Nunes
, Sergei Vassilvitskii
, Peilin Zhong
:
Measuring Re-identification Risk. Proc. ACM Manag. Data 1(2): 149:1-149:26 (2023) - Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. ICML 2023: 14353-14375 - Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong:
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. ITCS 2023: 48:1-48:24 - Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel:
Private estimation algorithms for stochastic block models and mixture models. NeurIPS 2023 - Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
k-Means Clustering with Distance-Based Privacy. NeurIPS 2023 - Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel:
Private estimation algorithms for stochastic block models and mixture models. CoRR abs/2301.04822 (2023) - Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong:
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. CoRR abs/2301.05605 (2023) - Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees. CoRR abs/2302.00037 (2023) - CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andrés Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong:
Measuring Re-identification Risk. CoRR abs/2304.07210 (2023) - Alessandro Epasto, Tamalika Mukherjee, Peilin Zhong:
Differentially Private Clustering in Data Streams. CoRR abs/2307.07449 (2023) - 2022
- Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii:
Scalable Differentially Private Clustering via Hierarchically Separated Trees. KDD 2022: 221-230 - Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
Near-Optimal Private and Scalable $k$-Clustering. NeurIPS 2022 - Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. NeurIPS 2022 - Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. SODA 2022: 1613-1660 - Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. SODA 2022: 3005-3042 - Matteo Almanza, Alessandro Epasto, Alessandro Panconesi, Giuseppe Re:
k-Clustering with Fair Outliers. WSDM 2022: 5-15 - Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab S. Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii:
Scalable Differentially Private Clustering via Hierarchically Separated Trees. CoRR abs/2206.08646 (2022) - Hossein Esfandiari, Alessandro Epasto, Vahab S. Mirrokni, Andres Muñoz Medina, Sergei Vassilvitskii:
Smooth Anonymity for Sparse Binary Matrices. CoRR abs/2207.06358 (2022) - Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. CoRR abs/2207.06944 (2022) - 2021
- Alessandro Epasto, Andrés Muñoz Medina, Steven Avery, Yijian Bai, Róbert Busa-Fekete, CJ Carey, Ya Gao, David Guthrie, Subham Ghosh, James Ioannidis, Junyi Jiao, Jakub Lacki, Jason Lee, Arne Mauser, Brian Milch, Vahab S. Mirrokni, Deepak Ravichandran, Wei Shi, Max Spero, Yunting Sun, Umar Syed, Sergei Vassilvitskii, Shuo Wang:
Clustering for Private Interest-based Advertising. KDD 2021: 2802-2810 - Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. CoRR abs/2106.02685 (2021) - Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. CoRR abs/2110.15533 (2021) - 2020
- Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian:
Fair Correlation Clustering. AISTATS 2020: 4195-4205 - Vaggos Chatziafratis, Grigory Yaroslavtsev, Euiwoong Lee, Konstantin Makarychev, Sara Ahmadian, Alessandro Epasto, Mohammad Mahdian:
Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection. AISTATS 2020: 3121-3132 - Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang:
Fair Hierarchical Clustering. NeurIPS 2020
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