| [1] | G. Cormode, S. Maddock, and C. Maple. FLAIM: AIM-based synthetic data generation in the federated setting. In ACM SIGKDD Conference, 2024. |
| [2] | G. Cormode, I. L. Markov, and H. Srinivas. Private and efficient federated numerical aggregation. In International Conference on Extending Database Technology, pages 734-742. OpenProceedings.org, 2024. |
| [3] | W. Chen, G. Cormode, A. Bharadwaj, P. Romov, and A. Özgür. Federated experiment design under distributed differential privacy. In AISTATS, volume 238 of Proceedings of Machine Learning Research, pages 2458-2466. PMLR, 2024. |
| [4] | G. Cormode, M. Dall'Agnol, T. Gur, and C. Hickey. Streaming zero-knowledge proofs. In Computational Complexity Conference (CCC), 2024. |
| [5] | G. Cormode and I. L. Markov. Federated calibration and evaluation of binary classifiers. In Proceedings of the VLDB Endowment, volume 16, pages 3253-3265, 2023. |
| [6] | W.-N. Chen, A. Özgür, G. Cormode, and A. Bharadwaj. The communication cost of security and privacy in federated frequency estimation. In AISTATS, pages 4247-4274, 2023. |
| [7] | J. Hehir, D. Ting, and G. Cormode. Sketch-flip-merge: Mergeable sketches for private distinct counting. In International Conference on Machine Learning, (ICML), 2023. |
| [8] | A. Biswas and G. Cormode. Interactive proofs for differentially private counting. In ACM Conference on Computer and Communications Security, 2023. |
| [9] | K. Cai, X. Xiao, and G. Cormode. Privlava: Synthesizing relational data with foreign keys under differential privacy. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2023. |
| [10] | M. Shekelyan, G. Cormode, P. Triantafillou, Q. Ma, and A. M. Shanghooshabad. Streaming weighted sampling over join queries. In EDBT, pages 298-310, 2023. |
| [11] | K. Prasad, S. Ghosh, G. Cormode, I. Mironov, A. Yousefpour, and P. Stock. Reconciling security and communication efficiency in federated learning. In International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022, 2022. |
| [12] | S. Maddock, G. Cormode, S. Jha, C. Maple, and T. Wang. Federated boosted decision trees with differential privacy. In ACM Conference on Computer and Communications Security, 2022. |
| [13] | L. Watson, C. Guo, G. Cormode, and A. Sablayrolles. On the importance of difficulty calibration in membership inference attacks. In International Conference on Learning Representations, ICLR, 2022. |
| [14] | A. Bharadwaj and G. Cormode. Sample-and-threshold differential privacy: Histograms and applications. In AISTATS, 2022. (full version). |
| [15] | Z. Huang, Y. Qiu, K. Yi, and G. Cormode. Frequency estimation under multiparty differential privacy: One-shot and streaming. In International Conference on Very Large Data Bases (VLDB), volume 15, page 2058–2070. VLDB Endowment, 2022. |
| [16] | A. Yousefpour, I. Shilov, A. Sablayrolles, D. Testuggine, K. Prasad, M. Malek, J. Nguyen, S. Ghosh, A. Bharadwaj, J. Zhao, G. Cormode, and I. Mironov. Opacus: User-friendly differential privacy library in pytorch. In Privacy in Machine Learning (NeurIPS workshop), 2021. |
| [17] | A. Bharadwaj and G. Cormode. Sample-and-threshold differential privacy: Histograms and applications. In Privacy in Machine Learning (NeurIPS workshop), 2021. (workshop version). |
| [18] | G. Cormode and I. Markov. Bit-efficient numerical aggregation and stronger privacy for trust in federated analytics. In PPML Workshop, 2021. |
| [19] | G. Cormode, C. Maple, and M. Scott. Applying the shuffle model of differential privacy to vector aggregation. In BICOD, 2021. |
| [20] | T. Cunningham, G. Cormode, H. Ferhatosmanoglu, and D. Srivastava. Real-world trajectory sharing with local differential privacy. In International Conference on Very Large Data Bases (VLDB), 2021. |
| [21] | G. Cormode, S. Maddock, and C. Maple. Frequency estimation under local differential privacy. In International Conference on Very Large Data Bases (VLDB), 2021. |
| [22] | T. Cunningham, G. Cormode, and H. Ferhatosmanoglu. Privacy-preserving synthetic location data in the real world. In Proceedings of International Symposium on Spatial and Temporal Databases, 2021. |
| [23] | G. Cormode, A. Mishra, J. Ross, and P. Veselý. Theory meets practice at the median:a worst case comparison of relative error quantile algorithms. In ACM SIGKDD Conference, 2021. |
| [24] | M. Shekelyan and G. Cormode. Sequential random sampling revisited: Hidden shuffle method. In AISTATS, volume 130 of Proceedings of Machine Learning Research, pages 3628-3636. PMLR, 2021. |
| [25] | G. Cormode, Z. Karnin, E. Liberty, J. Thaler, and P. Veselý. Relative error streaming quantiles. In ACM Principles of Database Systems (PODS), 2021. |
| [26] | G. Cormode, C. Dickens, and D. Woodruff. Subspace exploration: Bounds on projected frequency estimation. In ACM Principles of Database Systems (PODS), 2021. |
| [27] | G. Cormode, M. Garofalakis, and M. Shekelyan. Data-independent space partitionings for summaries. In ACM Principles of Database Systems (PODS), 2021. |
| [28] | G. Cormode and P. Veselý. A tight lower bound for comparison-based quantile summaries. In ACM Principles of Database Systems (PODS), pages 81-93. ACM, 2020. |
| [29] | G. Cormode and C. Dickens. Iterative hessian sketch in input sparsity time. In Proceedings of Beyond First Order Methods in ML (NeurIPS workshop), 2019. |
| [30] | G. Cormode and C. Hickey. Efficient interactive proofs for linear algebra. In Proceedings of International Symposium on Algorithms and Computation (ISAAC), 2019. |
| [31] | G. Cormode and P. Veselý. Streaming algorithms for bin packing and vector scheduling. In Workshop on Approximation and Online Algorithms, 2019. |
| [32] | R. Chitnis and G. Cormode. Towards a theory of parameterized streaming algorithms. In International Symposium on Parameterized and Exact Computation, 2019. |
| [33] | G. Cormode, T. Kulkarni, and D. Srivastava. Answering range queries under local differential privacy. In International Conference on Very Large Data Bases (VLDB), 2019. |
| [34] | G. Cormode, J. Dark, and C. Konrad. Independent sets in vertex-arrival streams. In International Colloquium on Automata, Languages and Programming (ICALP), 2019. |
| [35] | G. Cormode, C. Dickens, and D. P. Woodruff. Leveraging well-conditioned bases: Streaming and distributed summaries in minkowski p-norms. In International Conference on Machine Learning, (ICML), 2018. |
| [36] | G. Cormode, T. Kulkarni, and D. Srivastava. Marginal release under local differential privacy. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2018. |
| [37] | G. Cormode and C. Hickey. You can check others' work more quickly than doing it yourself. In International Conference on Data Engineering (ICDE), 2018. |
| [38] | G. Cormode, J. Dark, and C. Konrad. Approximating the caro-wei bound for independent sets in graph streams. In International Symposium on Combinatorial Optimization, 2018. |
| [39] | Y. Zhang, S. Tirthapura, and G. Cormode. Learning graphical models from a distributed stream. In International Conference on Data Engineering (ICDE), 2018. |
| [40] | G. Cormode and J. Dark. Fast sketch-based recovery of correlation outliers. In International Conference on Database Theory, 2018. |
| [41] | G. Cormode and C. Hickey. Cheap checking for cloud computing: Statistical analysis via annotated data streams. In AISTATS, 2018. |
| [42] | G. Cormode, T. Kulkarni, and D. Srivastava. Constrained private mechanisms for count data. In International Conference on Data Engineering (ICDE), 2018. |
| [43] | G. Cormode, H. Jowhari, M. Monemizadeh, and S. Muthukrishnan. Streaming algorithms for matching size estimation in sparse graphs. In European Symposium on Algorithms, 2017. |
| [44] | Z. Jorgensen, T. Yu, and G. Cormode. Publishing attributed social graphs with formal privacy guarantees. In ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 107-122, 2016. |
| [45] | R. Chitnis, G. Cormode, H. Esfandiari, M. Hajiaghayi, A. McGregor, M. Monemizadeh, and S. Vorotnikova. Kernelization via sampling with applications to dynamic graph streams. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2016. |
| [46] | K. J. Ahn, G. Cormode, S. Guha, A. McGregor, and A. Wirth. Correlation clustering in data streams. In International Conference on Machine Learning, (ICML), pages 2237-2246, 2015. |
| [47] | X. He, G. Cormode, A. Machanavajjhala, C. M. Procopiuc, and D. Srivastava. DPT: differentially private trajectory synthesis using hierarchical reference systems. In Proceedings of the VLDB Endowment, volume 8, pages 1154-1165, 2015. |
| [48] | R. Chitnis, G. Cormode, H. Esfandiari, M. Hajiaghayi, and M. Monemizadeh. New streaming algorithms for parameterized maximal matching & beyond. In Symposium on Parallelism in Algorithms, pages 56-58, 2015. |
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| [50] | A. Chakrabarti, G. Cormode, A. McGregor, J. Thaler, and S. Venkatasubramanian. Verifiable stream computation and Arthur-Merlin communication. In Computational Complexity Conference, 2015. |
| [51] | Z. Jorgensen, T. Yu, and G. Cormode. Conservative or liberal? personalized differential privacy. In International Conference on Data Engineering (ICDE), 2015. |
| [52] | R. Chitnis, G. Cormode, M. Hajiaghayi, and M. Monemizadeh. Parameterized streaming: Maximal matching and vertex cover. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2015. |
| [53] | G. Cormode, M. Procopiuc, D. Srivastava, X. Xiao, and J. Zhang. Privbayes: Private data release via bayesian networks. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2014. |
| [54] | Q. Ma, S. Muthukrishnan, B. Thompson, and G. Cormode. Modeling collaboration in academia: A game theoretic approach. In WWW Workshop on Big Scholarly Data, 2014. |
| [55] | G. Cormode, S. Muthukrishnan, and J. Yan. People like us: Mining scholarly data for comparable researchers. In WWW Workshop on Big Scholarly Data, 2014. |
| [56] | A. Chakrabarti, G. Cormode, N. Goyal, and J. Thaler. Annotations for sparse data streams. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2014. |
| [57] | G. Cormode, S. Muthukrishnan, and J. Yan. First author advantage: Citation labeling in research. In Proceedings of the Computational Scientometrics: Theory and Applications Workshop at CIKM, 2013. |
| [58] | G. Cormode, X. Gong, C. M. Procopiuc, E. Shen, D. Srivastava, and T. Yu. UMicS: From anonymized data to usable microdata. In ACM Conference on Information and Knowledge Management (CIKM), 2013. |
| [59] | S. Papadopoulos, G. Cormode, A. Deligiannakis, and M. Garofalakis. Lightweight authentication of linear algebraic queries on data streams. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2013. |
| [60] | L. Wang, G. Luo, K. Yi, and G. Cormode. Quantiles over data streams: An experimental study. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2013. |
| [61] | G. Cormode, C. M. Procopiuc, E. Shen, D. Srivastava, and T. Yu. Empirical privacy and empirical utility of anonymized data. In Privacy-Preserving Data Publication and Analysis (PrivDB), 2013. |
| [62] | G. Cormode, K. Mirylenka, T. Palpanas, and D. Srivastava. Finding interesting correlations with conditional heavy hitters. In International Conference on Data Engineering (ICDE), 2013. |
| [63] | G. Cormode, C. M. Procopiuc, D. Srivastava, and G. Yaroslavtsev. Accurate and efficient private release of datacubes and contingency tables. In International Conference on Data Engineering (ICDE), 2013. |
| [64] | G. Cormode and D. Firmani. On unifying the space of l0-sampling algorithms. In SIAM Meeting on Algorithm Engineering and Experiments, 2013. |
| [65] | G. Cormode, J. Thaler, and K. Yi. Verifying computations with streaming interactive proofs. In International Conference on Very Large Data Bases (VLDB), Sept. 2012. |
| [66] | A. Goyal, H. Daumé III, and G. Cormode. Sketch algorithms for estimating point queries in NLP. In EMNLP-CoNLL, pages 1093-1103, 2012. |
| [67] | P. Agarwal, G. Cormode, Z. Huang, J. Phillips, Z. Wei, and K. Yi. Mergeable summaries. In ACM Principles of Database Systems (PODS), 2012. |
| [68] | E. Cohen, G. Cormode, and N. Duffield. Don't let the negatives bring you down: Sampling from streams of signed updates. In ACM Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2012. |
| [69] | G. Cormode and K. Yi. Tracking distributed aggregates over time-based sliding windows. In Scientific and Statistical Database Management (SSDBM), 2012. |
| [70] | G. Cormode, S. Muthukrishnan, and J. Yan. Scienceography: the study of how science is written. In Proceedings of the International Conference on Fun with Algorithms (FUN), 2012. |
| [71] | G. Cormode, M. Procopiuc, D. Srivastava, and T. Tran. Differentially private publication of sparse data. In International Conference on Database Theory (ICDT), 2012. |
| [72] | G. Cormode, M. Procopiuc, E. Shen, D. Srivastava, and T. Yu. Differentially private spatial decompositions. In International Conference on Data Engineering (ICDE), 2012. |
| [73] | M. Lu, S. Bangalore, G. Cormode, M. Hadjieleftheriou, and D. Srivastava. A dataset search engine for the research document corpus. In International Conference on Data Engineering (ICDE), 2012. |
| [74] | G. Cormode, E. Shen, D. Srivastava, and T. Yu. Aggregate query answering on possibilistic data with cardinality constraints. In International Conference on Data Engineering (ICDE), 2012. |
| [75] | G. Cormode, M. Mitzenmacher, and J. Thaler. Practical verified computation with streaming interactive proofs. In Innovations in Theoretical Computer Science (ITCS), 2012. |
| [76] | P. Agarwal, G. Cormode, Z. Huang, J. Phillips, Z. Wei, and K. Yi. Mergeable coresets. In Third Workshop on Massive Data Algorithmics (MASSIVE), 2011. |
| [77] | G. Cormode. Personal privacy vs population privacy: Learning to attack anonymization. In ACM SIGKDD Conference, 2011. |
| [78] | E. Cohen, G. Cormode, and N. Duffield. Structure-aware sampling: Flexible and accurate summarization. In International Conference on Very Large Data Bases (VLDB), 2011. |
| [79] | G. Cormode and K. Yi. Tracking distributed aggregates over time-based sliding windows (brief announcement). In ACM Principles of Distributed Computing (PODC), 2011. |
| [80] | E. Cohen, G. Cormode, and N. Duffield. Structure-aware sampling on data streams. In ACM Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2011. |
| [81] | G. Cormode, H. Karloff, and T. Wirth. Set cover algorithms for very large datasets. In ACM Conference on Information and Knowledge Management (CIKM), 2010. |
| [82] | G. Cormode, S. Muthukrishnan, K. Yi, and Q. Zhang. Optimal sampling from distributed streams. In ACM Principles of Database Systems (PODS), 2010. |
| [83] | S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava. Privacy in dynamic social networks. In World Wide Web Conference (WWW), 2010. |
| [84] | G. Cormode, M. Mitzenmacher, and J. Thaler. Streaming graph computations with a helpful advisor. In European Symposium on Algorithms, 2010. |
| [85] | A. Chakrabarti, G. Cormode, R. Kondapally, and A. McGregor. Information cost tradeoffs for augmented index and streaming language recognition. In IEEE Foundations of Computer Science (FOCS), 2010. |
| [86] | G. Cormode, N. Li, T. Li, and D. Srivastava. Minimizing minimality and maximizing utility: Analyzing method-based attacks on anonymized data. In International Conference on Very Large Data Bases (VLDB), 2010. |
| [87] | S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava. Prediction promotes privacy in dynamic social networks. In Workshop on Online Social Networks (WOSN), 2010. |
| [88] | S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava. Class-based graph anonymization for social network data. In International Conference on Very Large Data Bases (VLDB), 2009. |
| [89] | G. Cormode, A. Deligiannakis, M. Garofalakis, and A. McGregor. Probabilistic histograms for probabilistic data. In International Conference on Very Large Data Bases (VLDB), 2009. |
| [90] | A. Chakrabarti, G. Cormode, and A. McGregor. Annotations in data streams. In International Colloquium on Automata, Languages and Programming (ICALP), 2009. |
| [91] | G. Cormode, L. Golab, F. Korn, A. McGregor, D. Srivastava, and X. Zhang. Estimating the confidence of conditional functional dependencies. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2009. |
| [92] | R. Berinde, G. Cormode, P. Indyk, and M. Strauss. Space-optimal heavy hitters with strong error bounds. In ACM Principles of Database Systems (PODS), 2009. |
| [93] | G. Cormode, S. Tirthapura, and B. Xu. Time-decayed correlated aggregates over data streams. In SIAM Conference on Data Mining (SDM), 2009. |
| [94] | G. Cormode and M. Garofalakis. Histograms and wavelets on probabilistic data. In International Conference on Data Engineering (ICDE), 2009. Best paper award. |
| [95] | G. Cormode, F. Li, and K. Yi. Semantics of ranking queries for probabilistic data and expected ranks. In International Conference on Data Engineering (ICDE), 2009. |
| [96] | G. Cormode, V. Shkapenyuk, D. Srivastava, and B. Xu. Forward decay: A practical time decay model for streaming systems. In International Conference on Data Engineering (ICDE), 2009. |
| [97] | G. Cormode and M. Hadjieleftheriou. Finding frequent items in data streams. In International Conference on Very Large Data Bases (VLDB), 2008. Best paper award. |
| [98] | G. Cormode, D. Srivastava, T. Yu, and Q. Zhang. Anonymizing bipartite graph data using safe groupings. In International Conference on Very Large Data Bases (VLDB), 2008. |
| [99] | G. Cormode, F. Korn, and S. Tirthapura. Time-decaying aggregates in out-of-order streams. In ACM Principles of Database Systems (PODS), 2008. |
| [100] | G. Cormode and A. McGregor. Approximation algorithms for clustering uncertain data. In ACM Principles of Database Systems (PODS), 2008. |
| [101] | G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Summarizing two-dimensional data with skyline-based statistical descriptors. In Scientific and Statistical Database Management (SSDBM), 2008. |
| [102] | A. Chakrabarti, G. Cormode, and A. McGregor. Robust lower bounds for communication and stream computation. In ACM Symposium on Theory of Computing (STOC), 2008. |
| [103] | G. Cormode, F. Korn, S. Muthukrishnan, and Y. Wu. On signatures for communication graphs. In International Conference on Data Engineering (ICDE), 2008. |
| [104] | G. Cormode, F. Korn, and S. Tirthapura. Exponentially decayed aggregates on data streams. In International Conference on Data Engineering (ICDE), 2008. |
| [105] | G. Cormode, S. Muthukrishnan, and K. Yi. Algorithms for distributed, functional monitoring. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2008. |
| [106] | S. Bhagat, G. Cormode, and I. Rozenbaum. Applying link-based classification to label blogs. In Joint WEBKDD and SNA-KDD Workshop, 2007. |
| [107] | S. Ganguly and G. Cormode. On estimating frequency moments of data streams. In Proceedings of RANDOM, 2007. |
| [108] | G. Cormode, S. Tirthapura, and B. Xu. Time-decaying sketches for sensor data aggregation. In ACM Principles of Distributed Computing (PODC), 2007. |
| [109] | G. Cormode and M. Garofalakis. Sketching probabilistic data streams. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2007. |
| [110] | S. Bhagat, G. Cormode, S. Muthukrishnan, I. Rozenbaum, and H. Xue. No blog is an island analyzing connections across information networks. In International Conference on Weblogs and Social Media, 2007. |
| [111] | G. Cormode, S. Muthukrishnan, and W. Zhuang. Conquering the divide: Continuous clustering of distributed data streams. In International Conference on Data Engineering (ICDE), 2007. |
| [112] | A. Chakrabarti, G. Cormode, and A. McGregor. A near-optimal algorithm for computing the entropy of a stream. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2007. |
| [113] | G. Cormode and S. Muthukrishnan. Combinatorial algorithms for compressed sensing. In SIROCCO, 2006. |
| [114] | G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Space- and time-efficient deterministic algorithms for biased quantiles over data streams. In ACM Principles of Database Systems (PODS), 2006. |
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| [116] | G. Cormode, M. Garofalakis, and D. Sacharidis. Fast approximate wavelet tracking on streams. In Extending Database Technology, pages 4-22, 2006. |
| [117] | G. Cormode, S. Muthukrishnan, and W. Zhuang. What's different: Distributed, continuous monitoring of duplicate-resilient aggregates on data streams. In International Conference on Data Engineering (ICDE), pages 20-31, 2006. |
| [118] | G. Cormode, S. Muthukrishnan, and I. Rozenbaum. Summarizing and mining inverse distributions on data streams via dynamic inverse sampling. In International Conference on Very Large Data Bases (VLDB), pages 25-36, 2005. |
| [119] | G. Cormode and M. Garofalakis. Sketching streams through the net: Distributed approximate query tracking. In International Conference on Very Large Data Bases (VLDB), pages 13-24, 2005. |
| [120] | G. Cormode and S. Muthukrishnan. Space efficient mining of multigraph streams. In ACM Principles of Database Systems (PODS), pages 271-282, 2005. |
| [121] | G. Cormode, M. Garofalakis, S. Muthukrishnan, and R. Rastogi. Holistic aggregates in a networked world: Distributed tracking of approximate quantiles. In ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 25-36, 2005. |
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| [124] | G. Cormode and S. Muthukrishnan. Substring compression problems. In ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 321-330, 2005. |
| [125] | G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Diamond in the rough: Finding hierarchical heavy hitters in multi-dimensional data. In ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 155-166, 2004. |
| [126] | G. Cormode, F. Korn, S. Muthukrishnan, T. Johnson, O. Spatscheck, and D. Srivastava. Holistic UDAFs at streaming speeds. In ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 35-46, 2004. |
| [127] | G. Cormode. The hardness of the lemmings game, or Oh no, more NP-completeness proofs. In Proceedings of Third International Conference on Fun with Algorithms, pages 65-76, 2004. |
| [128] | G. Cormode, A. Czumaj, and S. Muthukrishnan. How to increase the acceptance ratios of top conferences. In Proceedings of Third International Conference on Fun with Algorithms, pages 262-273, 2004. |
| [129] | G. Cormode and S. Muthukrishnan. What's new: Finding significant differences in network data streams. In Proceedings of IEEE Infocom, pages 1534-1545, 2004. |
| [130] | G. Cormode and S. Muthukrishnan. An improved data stream summary: The count-min sketch and its applications. In Proceedings of Latin American Theoretical Informatics (LATIN), pages 29-38, 2004. |
| [131] | G. Cormode. Stable distributions for stream computations: it's as easy as 0,1,2. In Workshop on Management and Processing of Massive Data Streams at FCRC, 2003. |
| [132] | G. Cormode and S. Muthukrishnan. What's hot and what's not: Tracking most frequent items dynamically. In ACM Principles of Database Systems (PODS), pages 296-306, 2003. |
| [133] | G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Finding hierarchical heavy hitters in data streams. In International Conference on Very Large Data Bases (VLDB), pages 464-475, 2003. |
| [134] | G. Cormode and S. Muthukrishnan. Estimating dominance norms of multiple data streams. In European Symposium on Algorithms, volume 2838 of LNCS, 2003. |
| [135] | G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using Hamming norms. In International Conference on Very Large Data Bases (VLDB), pages 335-345, 2002. |
| [136] | G. Cormode, P. Indyk, N. Koudas, and S. Muthukrishnan. Fast mining of tabular data via approximate distance computations. In International Conference on Data Engineering (ICDE), pages 605-616, 2002. |
| [137] | G. Cormode and S. Muthukrishnan. The string edit distance matching problem with moves. In ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 667-676, 2002. |
| [138] | G. Cormode, S. Muthukrishnan, and S. C. Sahinalp. Permutation editing and matching via embeddings. In International Colloquium on Automata, Languages and Programming (ICALP), volume 2076, pages 481-492, 2001. |
| [139] | G. Cormode, M. Paterson, S. C. Sahinalp, and U. Vishkin. Communication complexity of document exchange. In ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 197-206, 2000. |
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