On a Model for Integrated Information - Alessandro Epasto; Enrico Nardelli (CoRR, 2010)

Signals from the crowd: uncovering social relationships through smartphone probes. - Marco Valerio Barbera; Alessandro Epasto; Alessandro Mei; Vasile Claudiu Perta; Julinda Stefa (Internet Measurement Conference, 2013)

SoK: The Evolution of Sybil Defense via Social Networks. - Lorenzo Alvisi; Allen Clement; Alessandro Epasto; Silvio Lattanzi; Alessandro Panconesi (IEEE Symposium on Security and Privacy, 2013)

Spreading rumours without the network. - Pawel Brach; Alessandro Epasto; Alessandro Panconesi; Piotr Sankowski (COSN, 2014)

Reduce and aggregate: similarity ranking in multi-categorical bipartite graphs. - Alessandro Epasto; Jon Feldman; Silvio Lattanzi; Stefano Leonardi 001; Vahab S. Mirrokni (WWW, 2014)

Communities, Random Walks, and Social Sybil Defense. - Lorenzo Alvisi; Allen Clement; Alessandro Epasto; Silvio Lattanzi; Alessandro Panconesi (Internet Math., 2014)

Efficient Algorithms for Public-Private Social Networks. - Flavio Chierichetti; Alessandro Epasto; Ravi Kumar 001; Silvio Lattanzi; Vahab S. Mirrokni (KDD, 2015)

Efficient Densest Subgraph Computation in Evolving Graphs. - Alessandro Epasto; Silvio Lattanzi; Mauro Sozio (WWW, 2015)

Ego-net Community Mining Applied to Friend Suggestion. - Alessandro Epasto; Silvio Lattanzi; Vahab S. Mirrokni; Ismail Sebe; Ahmed Taei; Sunita Verma (Proc. VLDB Endow., 2015)

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

Real-Time Targeted-Influence Queries over Large Graphs. - Alessandro Epasto; Ahmad Mahmoody; Eli Upfal (ASONAM, 2017)

Ego-Splitting Framework: from Non-Overlapping to Overlapping Clusters. - Alessandro Epasto; Silvio Lattanzi; Renato Paes Leme (KDD, 2017)

Bicriteria Distributed Submodular Maximization in a Few Rounds. - Alessandro Epasto; Vahab S. Mirrokni; Morteza Zadimoghaddam (SPAA, 2017)

Submodular Optimization Over Sliding Windows. - Alessandro Epasto; Silvio Lattanzi; Sergei Vassilvitskii; Morteza Zadimoghaddam (WWW, 2017)

The Spread of Physical Activity Through Social Networks. - David Stück; Haraldur Tómas Hallgrímsson; Greg Ver Steeg; Alessandro Epasto; Luca Foschini 002 (WWW, 2017)

TRIÈST: Counting Local and Global Triangles in Fully Dynamic Streams with Fixed Memory Size. - Lorenzo De Stefani; Alessandro Epasto; Matteo Riondato; Eli Upfal (ACM Trans. Knowl. Discov. Data, 2017)

Incentive-Aware Learning for Large Markets. - Alessandro Epasto; Mohammad Mahdian; Vahab S. Mirrokni; Song Zuo (WWW, 2018)

Efficient Approximation for Restricted Biclique Cover Problems. - Alessandro Epasto; Eli Upfal (Algorithms, 2018)

Clustering without Over-Representation. - Sara Ahmadian; Alessandro Epasto; Ravi Kumar 001; Mohammad Mahdian (KDD, 2019)

Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives. - Michele Borassi; Alessandro Epasto; Silvio Lattanzi; Sergei Vassilvitskii; Morteza Zadimoghaddam (PODS, 2019)

Scalable Diversity Maximization via Small-size Composable Core-sets (Brief Announcement). - Alessandro Epasto; Vahab S. Mirrokni; Morteza Zadimoghaddam (SPAA, 2019)

On-Device Algorithms for Public-Private Data with Absolute Privacy. - Alessandro Epasto; Hossein Esfandiari; Vahab S. Mirrokni (WWW, 2019)

Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts. - Alessandro Epasto; Bryan Perozzi (WWW, 2019)

Fair Correlation Clustering. - Sara Ahmadian; Alessandro Epasto; Ravi Kumar 001; Mohammad Mahdian (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 Hierarchical Clustering. - Sara Ahmadian; Alessandro Epasto; Marina Knittel; Ravi Kumar 001; Mohammad Mahdian; Benjamin Moseley; Philip Pham; Sergei Vassilvitskii; Yuyan Wang (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 S. Mirrokni; Lijie Ren (NeurIPS, 2020)

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

Clustering for Private Interest-based Advertising. - 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; et al. (KDD, 2021)

Scalable Differentially Private Clustering via Hierarchically Separated Trees. - Vincent Cohen-Addad; Alessandro Epasto; Silvio Lattanzi; Vahab Mirrokni; Andres Muñoz Medina; David Saulpic; Chris Schwiegelshohn; Sergei Vassilvitskii (KDD, 2022)

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

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

Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. - Alessandro Epasto; Mohammad Mahdian; Vahab S. Mirrokni; Peilin Zhong (SODA, 2022)

Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. - Alessandro Epasto; Mohammad Mahdian; Vahab S. Mirrokni; Peilin Zhong (SODA, 2022)

k-Clustering with Fair Outliers. - Matteo Almanza; Alessandro Epasto; Alessandro Panconesi; Giuseppe Re (WSDM, 2022)

Smooth Anonymity for Sparse Binary Matrices. - Hossein Esfandiari; Alessandro Epasto; Vahab S. Mirrokni; Andres Muñoz Medina; Sergei Vassilvitskii (CoRR, 2022)

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

Differentially Private Continual Releases of Streaming Frequency Moment Estimations. - Alessandro Epasto; Jieming Mao; Andres Muñoz Medina; Vahab Mirrokni; Sergei Vassilvitskii; Peilin Zhong (ITCS, 2023)

Private estimation algorithms for stochastic block models and mixture models. - Hongjie Chen 004; Vincent Cohen-Addad; Tommaso d'Orsi; Alessandro Epasto; Jacob Imola; David Steurer; Stefan Tiegel (NeurIPS, 2023)

k-Means Clustering with Distance-Based Privacy. - Alessandro Epasto; Vahab Mirrokni; Shyam Narayanan; Peilin Zhong (NeurIPS, 2023)

Differentially Private Clustering in Data Streams. - Alessandro Epasto; Tamalika Mukherjee; Peilin Zhong (CoRR, 2023)

Measuring Re-identification Risk. - CJ Carey; Travis Dick; Alessandro Epasto; Adel Javanmard; Josh Karlin; Shankar Kumar; Andres Muñoz Medina; Vahab Mirrokni; Gabriel Henrique Nunes; Sergei Vassilvitskii; Peilin Zhong (Proc. ACM Manag. Data, 2023)

A Scalable Algorithm for Individually Fair k-Means Clustering. - MohammadHossein Bateni; Vincent Cohen-Addad; Alessandro Epasto; Silvio Lattanzi (AISTATS, 2024)

Perturb-and-Project: Differentially Private Similarities and Marginals. - Vincent Cohen-Addad; Tommaso d'Orsi; Alessandro Epasto; Vahab Mirrokni; Peilin Zhong (ICML, 2024)

Smooth Anonymity for Sparse Graphs. - Alessandro Epasto; Hossein Esfandiari; Vahab Mirrokni; Andrés Muñoz Medina (WWW (Companion Volume), 2024)

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 (CoRR, 2024)

The Power of Graph Sparsification in the Continual Release Model. - Alessandro Epasto; Quanquan C. Liu; Tamalika Mukherjee; Felix Zhou 002 (CoRR, 2024)

Sublinear Space Graph Algorithms in the Continual Release Model. - Alessandro Epasto; Quanquan C. Liu; Tamalika Mukherjee; Felix Zhou 002 (APPROX/RANDOM, 2025)

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

Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures. - Alina Ene; Alessandro Epasto; Vahab Mirrokni; Hoai-An Nguyen; Huy L. Nguyen; David P. Woodruff; Peilin Zhong (CoRR, 2025)

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing. - Adel Javanmard; Rudrajit Das; Alessandro Epasto; Vahab Mirrokni (CoRR, 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 (CoRR, 2025)

Private Training & Data Generation by Clustering Embeddings. - Felix Zhou 002; Samson Zhou; Vahab Mirrokni; Alessandro Epasto; Vincent Cohen-Addad (CoRR, 2025)

Differentially Private Synthetic Data Release for Topics API Outputs. - Travis Dick; Alessandro Epasto; Adel Javanmard; Josh Karlin; Andrés Muñoz Medina; Vahab Mirrokni; Sergei Vassilvitskii; Peilin Zhong (CoRR, 2025)

Scalable contribution bounding to achieve privacy. - Vincent Cohen-Addad; Alessandro Epasto; Jason Lee; Morteza Zadimoghaddam (CoRR, 2025)