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ORCIDSergei Vassilvitskii
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- affiliation: Google, New York, NY, USA
- affiliation (PhD): Stanford University, USA
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2020 – today
- 2024
[j23]Paul Dütting
, Silvio Lattanzi, Renato Paes Leme, Sergei Vassilvitskii:
Secretaries with Advice. Math. Oper. Res. 49(2): 856-879 (2024)- [c100]Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii:
Controlling Tail Risk in Online Ski-Rental. SODA 2024: 4247-4263 - [e1]Luz Angelica Caudillo-Mata, Silvio Lattanzi, Andrés Muñoz Medina, Leman Akoglu, Aristides Gionis, Sergei Vassilvitskii:
Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024. ACM 2024 [contents] - [i44]Berivan Isik, Natalia Ponomareva, Hussein Hazimeh, Dimitris Paparas, Sergei Vassilvitskii, Sanmi Koyejo:
Scaling Laws for Downstream Task Performance of Large Language Models. CoRR abs/2402.04177 (2024) - [i43]R. Preston McAfee, Renato Paes Leme, Balasubramanian Sivan, Sergei Vassilvitskii:
Winner-Pays-Bid Auctions Minimize Variance. CoRR abs/2403.04856 (2024) - [i42]Sami Davies, Sergei Vassilvitskii, Yuyan Wang:
Warm-starting Push-Relabel. CoRR abs/2405.18568 (2024) - [i41]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 abs/2407.12108 (2024) - 2023
- [j22]Sungjin Im, Ravi Kumar, Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii:
Massively Parallel Computation: Algorithms and Applications. Found. Trends Optim. 5(4): 340-417 (2023) - [j21]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: 1113-1201 (2023) - [j20]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) - [c99]Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii:
Easy Differentially Private Linear Regression. ICLR 2023 - [c98]Róbert Istvan Busa-Fekete, Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii:
Label differential privacy and private training data release. ICML 2023: 3233-3251 - [c97]Sami Davies, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang:
Predictive Flows for Faster Ford-Fulkerson. ICML 2023: 7231-7248 - [c96]Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii:
Learning-augmented private algorithms for multiple quantile release. ICML 2023: 16344-16376 - [c95]Silvio Lattanzi, Ola Svensson, Sergei Vassilvitskii:
Speeding Up Bellman Ford via Minimum Violation Permutations. ICML 2023: 18584-18598 - [c94]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 - [c93]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: 5823-5824 - [i40]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) - [i39]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 abs/2303.00654 (2023) - [i38]Sami Davies, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang:
Predictive Flows for Faster Ford-Fulkerson. CoRR abs/2303.00837 (2023) - [i37]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 abs/2304.06929 (2023) - [i36]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) - [i35]Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii:
Controlling Tail Risk in Online Ski-Rental. CoRR abs/2308.05067 (2023) - [i34]Michael Dinitz, Satyen Kale, Silvio Lattanzi, Sergei Vassilvitskii:
Improved Differentially Private Densest Subgraph: Local and Purely Additive. CoRR abs/2308.10316 (2023) - [i33]Nicole Megow, Benjamin Moseley, David B. Shmoys, Ola Svensson, Sergei Vassilvitskii, Jens Schlöter:
Scheduling (Dagstuhl Seminar 23061). Dagstuhl Reports 13(2): 1-19 (2023) - 2022
- [j19]Michael Mitzenmacher, Sergei Vassilvitskii:
Algorithms with predictions. Commun. ACM 65(7): 33-35 (2022) - [c92]Natalia Ponomareva, Jasmijn Bastings, Sergei Vassilvitskii:
Training Text-to-Text Transformers with Privacy Guarantees. ACL (Findings) 2022: 2182-2193 - [c91]Hossein Esfandiari, Vahab S. Mirrokni, Umar Syed, Sergei Vassilvitskii:
Label differential privacy via clustering. AISTATS 2022: 7055-7075 - [c90]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 - [c89]Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii:
Algorithms with Prediction Portfolios. NeurIPS 2022 - [c88]Misha Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii:
Learning Predictions for Algorithms with Predictions. NeurIPS 2022 - [i32]Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii:
Plume: Differential Privacy at Scale. CoRR abs/2201.11603 (2022) - [i31]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii:
Learning Predictions for Algorithms with Predictions. CoRR abs/2202.09312 (2022) - [i30]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) - [i29]Hossein Esfandiari, Alessandro Epasto, Vahab S. Mirrokni, Andres Muñoz Medina, Sergei Vassilvitskii:
Smooth Anonymity for Sparse Binary Matrices. CoRR abs/2207.06358 (2022) - [i28]Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii:
Easy Differentially Private Linear Regression. CoRR abs/2208.07353 (2022) - [i27]Kareem Amin, Travis Dick, Mikhail Khodak, Sergei Vassilvitskii:
Private Algorithms with Private Predictions. CoRR abs/2210.11222 (2022) - [i26]Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii:
Algorithms with Prediction Portfolios. CoRR abs/2210.12438 (2022) - 2021
- [j18]Thodoris Lykouris, Sergei Vassilvitskii:
Competitive Caching with Machine Learned Advice. J. ACM 68(4): 24:1-24:25 (2021) - [c87]Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang:
Hierarchical Clustering in General Metric Spaces using Approximate Nearest Neighbors. AISTATS 2021: 2440-2448 - [c86]Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii, Ellen Vitercik:
Private optimization without constraint violations. AISTATS 2021: 2557-2565 - [c85]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 - [c84]Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou:
Robust Online Correlation Clustering. NeurIPS 2021: 4688-4698 - [c83]Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii:
Faster Matchings via Learned Duals. NeurIPS 2021: 10393-10406 - [c82]Paul Dütting, Silvio Lattanzi, Renato Paes Leme, Sergei Vassilvitskii:
Secretaries with Advice. EC 2021: 409-429 - [i25]Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii:
Faster Matchings via Learned Duals. CoRR abs/2107.09770 (2021) - [i24]Hossein Esfandiari, Vahab S. Mirrokni, Umar Syed, Sergei Vassilvitskii:
Label differential privacy via clustering. CoRR abs/2110.02159 (2021) - 2020
- [c81]Aditya Bhaskara, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam:
Residual Based Sampling for Online Low Rank Approximation. ITA 2020: 1-19 - [c80]Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang:
Fair Hierarchical Clustering. NeurIPS 2020 - [c79]Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam:
Sliding Window Algorithms for k-Clustering Problems. NeurIPS 2020 - [c78]Silvio Lattanzi, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii:
Online Scheduling via Learned Weights. SODA 2020: 1859-1877 - [p1]Michael Mitzenmacher, Sergei Vassilvitskii:
Algorithms with Predictions. Beyond the Worst-Case Analysis of Algorithms 2020: 646-662 - [i23]Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam:
Sliding Window Algorithms for k-Clustering Problems. CoRR abs/2006.05850 (2020) - [i22]Michael Mitzenmacher, Sergei Vassilvitskii:
Algorithms with Predictions. CoRR abs/2006.09123 (2020) - [i21]Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang:
Fair Hierarchical Clustering. CoRR abs/2006.10221 (2020) - [i20]Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii, Ellen Vitercik:
Private Optimization Without Constraint Violations. CoRR abs/2007.01181 (2020) - [i19]Paul Dütting, Silvio Lattanzi, Renato Paes Leme, Sergei Vassilvitskii:
Secretaries with Advice. CoRR abs/2011.06726 (2020)
2010 – 2019
- 2019
- [c77]Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii:
Matroids, Matchings, and Fairness. AISTATS 2019: 2212-2220 - [c76]Mohammad Reza Karimi Jaghargh, Andreas Krause, Silvio Lattanzi, Sergei Vassilvitskii:
Consistent Online Optimization: Convex and Submodular. AISTATS 2019: 2241-2250 - [c75]Aditya Bhaskara, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam:
Residual Based Sampling for Online Low Rank Approximation. FOCS 2019: 1596-1614 - [c74]Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii:
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. ICML 2019: 263-271 - [c73]Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii:
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. ICML 2019: 2260-2268 - [c72]Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii:
Differentially Private Covariance Estimation. NeurIPS 2019: 14190-14199 - [c71]Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam:
Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives. PODS 2019: 254-268 - 2018
- [j17]Tim Roughgarden, Sergei Vassilvitskii, Joshua R. Wang:
Shuffles and Circuits (On Lower Bounds for Modern Parallel Computation). J. ACM 65(6): 41:1-41:24 (2018) - [c70]Andrés Muñoz Medina, Sergei Vassilvitskii, Dong Yin:
Online Learning for Non-Stationary A/B Tests. CIKM 2018: 317-326 - [c69]Thodoris Lykouris, Sergei Vassilvitskii:
Competitive Caching with Machine Learned Advice. ICML 2018: 3302-3311 - [c68]Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet:
Maximizing Induced Cardinality Under a Determinantal Point Process. NeurIPS 2018: 6911-6920 - [c67]Sébastien Lahaie, Andrés Muñoz Medina, Balasubramanian Sivan, Sergei Vassilvitskii:
Testing Incentive Compatibility in Display Ad Auctions. WWW 2018: 1419-1428 - [i18]Andrés Muñoz Medina, Sergei Vassilvitskii, Dong Yin:
Online Learning for Non-Stationary A/B Tests. CoRR abs/1802.05315 (2018) - [i17]Thodoris Lykouris, Sergei Vassilvitskii:
Competitive caching with machine learned advice. CoRR abs/1802.05399 (2018) - [i16]Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii:
Fair Clustering Through Fairlets. CoRR abs/1802.05733 (2018) - 2017
- [j16]Shalmoli Gupta, Ravi Kumar, Kefu Lu, Benjamin Moseley, Sergei Vassilvitskii:
Local Search Methods for k-Means with Outliers. Proc. VLDB Endow. 10(7): 757-768 (2017) - [c66]Umar Syed, Sergei Vassilvitskii:
SQML: large-scale in-database machine learning with pure SQL. SoCC 2017: 659 - [c65]Silvio Lattanzi, Sergei Vassilvitskii:
Consistent k-Clustering. ICML 2017: 1975-1984 - [c64]Andres Muñoz Medina, Sergei Vassilvitskii:
Revenue Optimization with Approximate Bid Predictions. NIPS 2017: 1858-1866 - [c63]Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii:
Fair Clustering Through Fairlets. NIPS 2017: 5029-5037 - [c62]Eric Balkanski, Umar Syed, Sergei Vassilvitskii:
Statistical Cost Sharing. NIPS 2017: 6221-6230 - [c61]Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam:
Submodular Optimization Over Sliding Windows. WWW 2017: 421-430 - [c60]Aaron Archer, Silvio Lattanzi, Peter Likarish, Sergei Vassilvitskii:
Indexing Public-Private Graphs. WWW 2017: 1461-1470 - [i15]Eric Balkanski, Umar Syed, Sergei Vassilvitskii:
Statistical Cost Sharing. CoRR abs/1703.03111 (2017) - [i14]Andrés Muñoz Medina, Sergei Vassilvitskii:
Revenue Optimization with Approximate Bid Predictions. CoRR abs/1706.04732 (2017) - 2016
- [j15]Patrick Hummel, R. Preston McAfee, Sergei Vassilvitskii:
Incentivizing advertiser networks to submit multiple bids. Int. J. Game Theory 45(4): 1031-1052 (2016) - [c59]Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian:
Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces. AISTATS 2016: 948-956 - [c58]Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod:
Pricing a Low-regret Seller. ICML 2016: 2559-2567 - [c57]Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii:
On Mixtures of Markov Chains. NIPS 2016: 3441-3449 - [c56]Tim Roughgarden, Sergei Vassilvitskii, Joshua R. Wang:
Shuffles and Circuits: (On Lower Bounds for Modern Parallel Computation). SPAA 2016: 1-12 - [c55]Renato Paes Leme, Martin Pál, Sergei Vassilvitskii:
A Field Guide to Personalized Reserve Prices. WWW 2016: 1093-1102 - [i13]Renato Paes Leme, Martin Pal, Sergei Vassilvitskii:
A Field Guide to Personalized Reserve Prices. CoRR abs/1602.07720 (2016) - [i12]Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam:
Submodular Optimization over Sliding Windows. CoRR abs/1610.09984 (2016) - 2015
- [j14]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): 12:1-12:18 (2015) - [j13]Ruggiero Cavallo, R. Preston McAfee, Sergei Vassilvitskii:
Display Advertising Auctions with Arbitrage. ACM Trans. Economics and Comput. 3(3): 15:1-15:23 (2015) - [j12]Ravi Kumar, Benjamin Moseley, Sergei Vassilvitskii, Andrea Vattani:
Fast Greedy Algorithms in MapReduce and Streaming. ACM Trans. Parallel Comput. 2(3): 14:1-14:22 (2015) - [c54]Mohammad Mahdian, Okke Schrijvers, Sergei Vassilvitskii:
Algorithmic Cartography: Placing Points of Interest and Ads on Maps. KDD 2015: 755-764 - [c53]Ravi Kumar, Mohammad Mahdian, Bo Pang, Andrew Tomkins, Sergei Vassilvitskii:
Driven by Food: Modeling Geographic Choice. WSDM 2015: 213-222 - [c52]Ravi Kumar, Andrew Tomkins, Sergei Vassilvitskii, Erik Vee:
Inverting a Steady-State. WSDM 2015: 359-368 - [i11]Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian:
Sketching, Embedding, and Dimensionality Reduction for Information Spaces. CoRR abs/1503.05225 (2015) - 2014
- [j11]Sujith Ravi, Sergei Vassilvitskii, Vibhor Rastogi:
Parallel Algorithms for Unsupervised Tagging. Trans. Assoc. Comput. Linguistics 2: 105-118 (2014) - [c51]Raimondas Kiveris, Silvio Lattanzi, Vahab S. Mirrokni, Vibhor Rastogi, Sergei Vassilvitskii:
Connected Components in MapReduce and Beyond. SoCC 2014: 18:1-18:13 - [c50]Kshipra Bhawalkar, Patrick Hummel, Sergei Vassilvitskii:
Value of Targeting. SAGT 2014: 194-205 - [c49]Andrei Z. Broder, Lluis Garcia Pueyo, Vanja Josifovski, Sergei Vassilvitskii, Srihari Venkatesan:
Scalable K-Means by ranked retrieval. WSDM 2014: 233-242 - [c48]Samuel Ieong, Mohammad Mahdian, Sergei Vassilvitskii:
Advertising in a stream. WWW 2014: 29-38 - [c47]Ashton Anderson, Ravi Kumar, Andrew Tomkins, Sergei Vassilvitskii:
The dynamics of repeat consumption. WWW 2014: 419-430 - [i10]Kshipra Bhawalkar, Patrick Hummel, Sergei Vassilvitskii:
Value of Targeting. CoRR abs/1407.3338 (2014) - [i9]Andrew McGregor, Gopal Pandurangan, Sergei Vassilvitskii:
Algorithms for Large Scale Graphs (NII Shonan Meeting 2014-12). NII Shonan Meet. Rep. 2014 (2014) - 2013
- [c46]Ravi Kumar, Daniel Lokshtanov, Sergei Vassilvitskii, Andrea Vattani:
Near-Optimal Bounds for Cross-Validation via Loss Stability. ICML (1) 2013: 27-35 - [c45]Ravi Kumar, Benjamin Moseley, Sergei Vassilvitskii, Andrea Vattani:
Fast greedy algorithms in mapreduce and streaming. SPAA 2013: 1-10 - [c44]Sergei Vassilvitskii:
MapReduce Algorithmics. WADS 2013: 524 - [c43]Ravi Kumar, Ronny Lempel, Roy Schwartz, Sergei Vassilvitskii:
Rank quantization. WSDM 2013: 153-162 - [c42]Quang Duong, Sharad Goel, Jake M. Hofman, Sergei Vassilvitskii:
Sharding social networks. WSDM 2013: 223-232 - 2012
- [j10]Bahman Bahmani, Ravi Kumar, Sergei Vassilvitskii:
Densest Subgraph in Streaming and MapReduce. Proc. VLDB Endow. 5(5): 454-465 (2012) - [j9]Bahman Bahmani, Benjamin Moseley, Andrea Vattani, Ravi Kumar, Sergei Vassilvitskii:
Scalable K-Means++. Proc. VLDB Endow. 5(7): 622-633 (2012) - [c41]Jian Yang, Erik Vee, Sergei Vassilvitskii, John A. Tomlin, Jayavel Shanmugasundaram, Tasos Anastasakos, Oliver Kennedy:
Inventory Allocation for Online Graphical Display Advertising using Multi-objective Optimization. ICORES 2012: 293-304 - [c40]Hu Fu, Patrick R. Jordan, Mohammad Mahdian, Uri Nadav, Inbal Talgam-Cohen, Sergei Vassilvitskii:
Ad auctions with data. INFOCOM Workshops 2012: 184-189 - [c39]Vijay Bharadwaj, Peiji Chen, Wenjing Ma, Chandrashekhar Nagarajan, John A. Tomlin, Sergei Vassilvitskii, Erik Vee, Jian Yang:
SHALE: an efficient algorithm for allocation of guaranteed display advertising. KDD 2012: 1195-1203 - [c38]Hu Fu, Patrick R. Jordan, Mohammad Mahdian, Uri Nadav, Inbal Talgam-Cohen, Sergei Vassilvitskii:
Ad Auctions with Data. SAGT 2012: 168-179 - [c37]Peiji Chen, Wenjing Ma, Srinath Mandalapu, Chandrashekhar Nagarajan, Jayavel Shanmugasundaram, Sergei Vassilvitskii, Erik Vee, Manfai Yu, Jason Y. Zien:
Ad serving using a compact allocation plan. EC 2012: 319-336 - [c36]Mohammad Mahdian, Arpita Ghosh, R. Preston McAfee, Sergei Vassilvitskii:
To match or not to match: economics of cookie matching in online advertising. EC 2012: 741-753 - [c35]Kevin J. Lang, Benjamin Moseley, Sergei Vassilvitskii:
Handling forecast errors while bidding for display advertising. WWW 2012: 371-380 - [i8]Bahman Bahmani, Ravi Kumar, Sergei Vassilvitskii:
Densest Subgraph in Streaming and MapReduce. CoRR abs/1201.6567 (2012) - [i7]Peiji Chen, Wenjing Ma, Srinath Mandalapu, Chandrashekhar Nagarajan, Jayavel Shanmugasundaram, Sergei Vassilvitskii, Erik Vee, Manfai Yu, Jason Y. Zien:
Ad Serving Using a Compact Allocation Plan. CoRR abs/1203.3593 (2012) - [i6]Vijay Bharadwaj, Peiji Chen, Wenjing Ma, Chandrashekhar Nagarajan, John A. Tomlin, Sergei Vassilvitskii, Erik Vee, Jian Yang:
SHALE: An Efficient Algorithm for Allocation of Guaranteed Display Advertising. CoRR abs/1203.3619 (2012) - [i5]Bahman Bahmani, Benjamin Moseley, Andrea Vattani, Ravi Kumar, Sergei Vassilvitskii:
Scalable K-Means++. CoRR abs/1203.6402 (2012) - 2011
- [j8]Ning Chen, Arpita Ghosh, Sergei Vassilvitskii:
Optimal Envy-Free Pricing with Metric Substitutability. SIAM J. Comput. 40(3): 623-645 (2011) - [c34]Marcus Fontoura, Maxim Gurevich, Vanja Josifovski, Sergei Vassilvitskii:
Efficiently encoding term co-occurrences in inverted indexes. CIKM 2011: 307-316 - [c33]