Conference Publications

[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.
[49] J. Zhang, G. Cormode, M. Procopiuc, D. Srivastava, and X. Xiao. Private release of graph statistics using ladder functions. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2015.
[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.
[115] G. Cormode, R. Keralapura, and J. Ramimirtham. Communication-efficient distributed monitoring of thresholded counts. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2006.
[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.
[122] G. Cormode and S. Muthukrishnan. Summarizing and mining skewed data streams. In SIAM Conference on Data Mining (SDM), 2005.
[123] G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Effective computation of biased quantiles over data streams. In International Conference on Data Engineering (ICDE), pages 20-31, 2005.
[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.
[140] G. Ozsoyoglu, N. H. Balkir, G. Cormode, and Z. M. Ozsoyoglu. Electronic books in digital libraries. In Proceedings of IEEE Advances in Digital Libraries (ADL), pages 5-14, 2000.

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