Sergei Vassilvitskii

ORCID

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 88

Top Venues

Publications

R. Preston McAfee, Renato Paes Leme, Balasubramanian Sivan, Sergei Vassilvitskii
Winner-Pays-Bid Auctions Minimize Variance. CoRR (2024)
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
Controlling Tail Risk in Online Ski-Rental. SODA (2024)
Berivan Isik, Natalia Ponomareva, Hussein Hazimeh, Dimitris Paparas, Sergei Vassilvitskii, Sanmi Koyejo
Scaling Laws for Downstream Task Performance of Large Language Models. CoRR (2024)
Paul Dütting, Silvio Lattanzi, Renato Paes Leme, Sergei Vassilvitskii
Secretaries with Advice. Math. Oper. Res. 49 (2) (2024)
Kareem Amin, Alex Bie, Weiwei Kong, Alexey Kurakin, Natalia Ponomareva, Umar Syed, Andreas Terzis, Sergei Vassilvitskii
Private prediction for large-scale synthetic text generation. CoRR (2024)
Sami Davies, Sergei Vassilvitskii, Yuyan Wang
Warm-starting Push-Relabel. CoRR (2024)
Michael Dinitz, Satyen Kale, Silvio Lattanzi, Sergei Vassilvitskii
Improved Differentially Private Densest Subgraph: Local and Purely Additive. CoRR (2023)
Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. CoRR (2023)
Sami Davies, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang
Predictive Flows for Faster Ford-Fulkerson. ICML (2023)
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
Controlling Tail Risk in Online Ski-Rental. CoRR (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) (2023)
Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii
Easy Differentially Private Linear Regression. ICLR (2023)
Sungjin Im, Ravi Kumar, Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii
Massively Parallel Computation: Algorithms and Applications. Found. Trends Optim. 5 (4) (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 (2023)
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. J. Artif. Intell. Res. 77 (2023)
Silvio Lattanzi, Ola Svensson, Sergei Vassilvitskii
Speeding Up Bellman Ford via Minimum Violation Permutations. ICML (2023)
Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii
Learning-augmented private algorithms for multiple quantile release. ICML (2023)
Róbert Istvan Busa-Fekete, Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii
Label differential privacy and private training data release. ICML (2023)
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Thakurta
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. CoRR (2023)
Nicole Megow, Benjamin Moseley, David B. Shmoys, Ola Svensson, Sergei Vassilvitskii, Jens Schlöter
Scheduling (Dagstuhl Seminar 23061). Dagstuhl Reports 13 (2) (2023)
Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. ITCS (2023)
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang
Challenges towards the Next Frontier in Privacy. CoRR (2023)
Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang
How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy. KDD (2023)
Sami Davies, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang
Predictive Flows for Faster Ford-Fulkerson. CoRR (2023)
Kareem Amin, Travis Dick, Mikhail Khodak, Sergei Vassilvitskii
Private Algorithms with Private Predictions. CoRR (2022)
Hossein Esfandiari, Vahab S. Mirrokni, Umar Syed, Sergei Vassilvitskii
Label differential privacy via clustering. AISTATS (2022)
Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab S. Mirrokni, Andres Munoz, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii
Scalable Differentially Private Clustering via Hierarchically Separated Trees. CoRR (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)
Natalia Ponomareva, Jasmijn Bastings, Sergei Vassilvitskii
Training Text-to-Text Transformers with Privacy Guarantees. ACL (Findings) (2022)
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
Algorithms with Prediction Portfolios. CoRR (2022)
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
Algorithms with Prediction Portfolios. NeurIPS (2022)
Hossein Esfandiari, Alessandro Epasto, Vahab S. Mirrokni, Andres Muñoz Medina, Sergei Vassilvitskii
Smooth Anonymity for Sparse Binary Matrices. CoRR (2022)
Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii
Easy Differentially Private Linear Regression. CoRR (2022)
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii
Learning Predictions for Algorithms with Predictions. CoRR (2022)
Misha Khodak, Maria-Florina F. Balcan, Ameet Talwalkar, Sergei Vassilvitskii
Learning Predictions for Algorithms with Predictions. NeurIPS (2022)
Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii
Plume: Differential Privacy at Scale. CoRR (2022)
Michael Mitzenmacher, Sergei Vassilvitskii
Algorithms with predictions. Commun. ACM 65 (7) (2022)
Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii, Ellen Vitercik
Private optimization without constraint violations. AISTATS (2021)
Alessandro Epasto, Andrés Muñoz Medina, Steven Avery, Yijian Bai, Róbert Busa-Fekete, C. J. 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)
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
Faster Matchings via Learned Duals. CoRR (2021)
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
Faster Matchings via Learned Duals. NeurIPS (2021)
Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang
Hierarchical Clustering in General Metric Spaces using Approximate Nearest Neighbors. AISTATS (2021)
Hossein Esfandiari, Vahab S. Mirrokni, Umar Syed, Sergei Vassilvitskii
Label differential privacy via clustering. CoRR (2021)
Thodoris Lykouris, Sergei Vassilvitskii
Competitive Caching with Machine Learned Advice. J. ACM 68 (4) (2021)
Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou
Robust Online Correlation Clustering. NeurIPS (2021)
Paul Dütting, Silvio Lattanzi, Renato Paes Leme, Sergei Vassilvitskii
Secretaries with Advice. EC (2021)
Paul Dütting, Silvio Lattanzi, Renato Paes Leme, Sergei Vassilvitskii
Secretaries with Advice. CoRR (2020)
Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii, Ellen Vitercik
Private Optimization Without Constraint Violations. CoRR (2020)
Michael Mitzenmacher, Sergei Vassilvitskii
Algorithms with Predictions. CoRR (2020)
Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang
Fair Hierarchical Clustering. NeurIPS (2020)
Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang
Fair Hierarchical Clustering. CoRR (2020)
Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam
Sliding Window Algorithms for k-Clustering Problems. CoRR (2020)
Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam
Sliding Window Algorithms for k-Clustering Problems. NeurIPS (2020)
Silvio Lattanzi, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
Online Scheduling via Learned Weights. SODA (2020)
Aditya Bhaskara, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam
Residual Based Sampling for Online Low Rank Approximation. ITA (2020)
Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii
Differentially Private Covariance Estimation. NeurIPS (2019)
Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. ICML (2019)
Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. ICML (2019)
Aditya Bhaskara, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam
Residual Based Sampling for Online Low Rank Approximation. FOCS (2019)
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii
Matroids, Matchings, and Fairness. AISTATS (2019)
Mohammad Reza Karimi Jaghargh, Andreas Krause, Silvio Lattanzi, Sergei Vassilvitskii
Consistent Online Optimization: Convex and Submodular. AISTATS (2019)
Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam
Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives. PODS (2019)
Andrés Muñoz Medina, Sergei Vassilvitskii, Dong Yin
Online Learning for Non-Stationary A/B Tests. CoRR (2018)
Andrés Muñoz Medina, Sergei Vassilvitskii, Dong Yin
Online Learning for Non-Stationary A/B Tests. CIKM (2018)
Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet
Maximizing Induced Cardinality Under a Determinantal Point Process. NeurIPS (2018)
Sébastien Lahaie, Andrés Muñoz Medina, Balasubramanian Sivan, Sergei Vassilvitskii
Testing Incentive Compatibility in Display Ad Auctions. WWW (2018)
Tim Roughgarden, Sergei Vassilvitskii, Joshua R. Wang
Shuffles and Circuits (On Lower Bounds for Modern Parallel Computation). J. ACM 65 (6) (2018)
Thodoris Lykouris, Sergei Vassilvitskii
Competitive caching with machine learned advice. CoRR (2018)
Thodoris Lykouris, Sergei Vassilvitskii
Competitive Caching with Machine Learned Advice. ICML (2018)
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii
Fair Clustering Through Fairlets. CoRR (2018)
Eric Balkanski, Umar Syed, Sergei Vassilvitskii
Statistical Cost Sharing. CoRR (2017)
Umar Syed, Sergei Vassilvitskii
SQML: large-scale in-database machine learning with pure SQL. SoCC (2017)
Silvio Lattanzi, Sergei Vassilvitskii
Consistent k-Clustering. ICML (2017)
Andrés Muñoz Medina, Sergei Vassilvitskii
Revenue Optimization with Approximate Bid Predictions. CoRR (2017)
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii
Fair Clustering Through Fairlets. NIPS (2017)
Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam
Submodular Optimization Over Sliding Windows. WWW (2017)
Aaron Archer, Silvio Lattanzi, Peter Likarish, Sergei Vassilvitskii
Indexing Public-Private Graphs. WWW (2017)
Shalmoli Gupta, Ravi Kumar, Kefu Lu, Benjamin Moseley, Sergei Vassilvitskii
Local Search Methods for k-Means with Outliers. Proc. VLDB Endow. 10 (7) (2017)
Eric Balkanski, Umar Syed, Sergei Vassilvitskii
Statistical Cost Sharing. NIPS (2017)
Andres Muñoz Medina, Sergei Vassilvitskii
Revenue Optimization with Approximate Bid Predictions. NIPS (2017)
Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian
Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces. AISTATS (2016)
Patrick Hummel, R. Preston McAfee, Sergei Vassilvitskii
Incentivizing advertiser networks to submit multiple bids. Int. J. Game Theory 45 (4) (2016)
Renato Paes Leme, Martin Pal, Sergei Vassilvitskii
A Field Guide to Personalized Reserve Prices. CoRR (2016)
Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii
On Mixtures of Markov Chains. NIPS (2016)
Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod
Pricing a Low-regret Seller. ICML (2016)
Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam
Submodular Optimization over Sliding Windows. CoRR (2016)
Tim Roughgarden, Sergei Vassilvitskii, Joshua R. Wang
Shuffles and Circuits: (On Lower Bounds for Modern Parallel Computation). SPAA (2016)
Renato Paes Leme, Martin Pál, Sergei Vassilvitskii
A Field Guide to Personalized Reserve Prices. WWW (2016)
Mohammad Mahdian, Okke Schrijvers, Sergei Vassilvitskii
Algorithmic Cartography: Placing Points of Interest and Ads on Maps. KDD (2015)
Ravi Kumar, Andrew Tomkins, Sergei Vassilvitskii, Erik Vee
Inverting a Steady-State. WSDM (2015)
Arpita Ghosh, Mohammad Mahdian, R. Preston McAfee, Sergei Vassilvitskii
To Match or Not to Match: Economics of Cookie Matching in Online Advertising. ACM Trans. Economics and Comput. 3 (2) (2015)
Ravi Kumar, Mohammad Mahdian, Bo Pang, Andrew Tomkins, Sergei Vassilvitskii
Driven by Food: Modeling Geographic Choice. WSDM (2015)
Ruggiero Cavallo, R. Preston McAfee, Sergei Vassilvitskii
Display Advertising Auctions with Arbitrage. ACM Trans. Economics and Comput. 3 (3) (2015)
Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian
Sketching, Embedding, and Dimensionality Reduction for Information Spaces. CoRR (2015)
Ravi Kumar, Benjamin Moseley, Sergei Vassilvitskii, Andrea Vattani
Fast Greedy Algorithms in MapReduce and Streaming. ACM Trans. Parallel Comput. 2 (3) (2015)
Samuel Ieong, Mohammad Mahdian, Sergei Vassilvitskii
Advertising in a stream. WWW (2014)
Kshipra Bhawalkar, Patrick Hummel, Sergei Vassilvitskii
Value of Targeting. CoRR (2014)
Andrew McGregor, Gopal Pandurangan, Sergei Vassilvitskii
Algorithms for Large Scale Graphs (NII Shonan Meeting 2014-12). NII Shonan Meet. Rep. 2014 (2014)
Andrei Z. Broder, Lluis Garcia Pueyo, Vanja Josifovski, Sergei Vassilvitskii, Srihari Venkatesan
Scalable K-Means by ranked retrieval. WSDM (2014)
Ashton Anderson, Ravi Kumar, Andrew Tomkins, Sergei Vassilvitskii
The dynamics of repeat consumption. WWW (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Sergei Vassilvitskii