Sanjoy Dasgupta

Sanjoy Dasgupta: Experiments with Random Projection. CoRR abs/1301.3849 (2013)

Sanjoy Dasgupta, Leonard J. Schulman: A Two-round Variant of EM for Gaussian Mixtures. CoRR abs/1301.3850 (2013)

Sanjoy Dasgupta: Learning Polytrees. CoRR abs/1301.6688 (2013)

Sanjoy Dasgupta, Kaushik Sinha: Randomized partition trees for exact nearest neighbor search. CoRR abs/1302.1948 (2013)

Samory Kpotufe, Sanjoy Dasgupta: A tree-based regressor that adapts to intrinsic dimension. J. Comput. Syst. Sci. 78(5): 1496-1515 (2012)

Sanjoy Dasgupta: Consistency of Nearest Neighbor Classification under Selective Sampling. Journal of Machine Learning Research - Proceedings Track 23: 18.1-18.15 (2012)

Matus Telgarsky, Sanjoy Dasgupta: Agglomerative Bregman Clustering. ICML 2012

Nakul Verma, Samory Kpotufe, Sanjoy Dasgupta: Which Spatial Partition Trees are Adaptive to Intrinsic Dimension? CoRR abs/1205.2609 (2012)

Sanjoy Dasgupta: Two faces of active learning. Theor. Comput. Sci. 412(19): 1767-1781 (2011)

Sanjoy Dasgupta: Strange effects in high dimension. Commun. ACM 53(2): 96 (2010)

Kamalika Chaudhuri, Sanjoy Dasgupta: Rates of convergence for the cluster tree. NIPS 2010: 343-351

Sanjoy Dasgupta: Active Learning Theory. Encyclopedia of Machine Learning 2010: 14-19

Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni: Analysis of Perceptron-Based Active Learning. Journal of Machine Learning Research 10: 281-299 (2009)

Sanjoy Dasgupta, Yoav Freund: Random projection trees for vector quantization. IEEE Transactions on Information Theory 55(7): 3229-3242 (2009)

Sanjoy Dasgupta: The Two Faces of Active Learning. ALT 2009: 1

Sanjoy Dasgupta: The Two Faces of Active Learning. Discovery Science 2009: 35

Alina Beygelzimer, Sanjoy Dasgupta, John Langford: Importance weighted active learning. ICML 2009: 7

Sanjoy Dasgupta, John Langford: Tutorial summary: Active learning. ICML 2009: 178

Nakul Verma, Samory Kpotufe, Sanjoy Dasgupta: Which Spatial Partition Trees are Adaptive to Intrinsic Dimension? UAI 2009: 565-574

Kamalika Chaudhuri, Sanjoy Dasgupta, Andrea Vattani: Learning Mixtures of Gaussians using the k-means Algorithm. CoRR abs/0912.0086 (2009)

Sanjoy Dasgupta, Christos H. Papadimitriou, Umesh V. Vazirani: Algorithms. McGraw-Hill 2008, isbn 978-0-07-352340-8, pp. I-X, 1-320

Sanjoy Dasgupta: Special issue on learning theory. J. Comput. Syst. Sci. 74(1): 1 (2008)

Sanjoy Dasgupta, Daniel Hsu: Hierarchical sampling for active learning. ICML 2008: 208-215

Sanjoy Dasgupta, Daniel Hsu, Claire Monteleoni: A General Agnostic Active Learning Algorithm. ISAIM 2008

Sanjoy Dasgupta, Yoav Freund: Random projection trees and low dimensional manifolds. STOC 2008: 537-546

Alina Beygelzimer, Sanjoy Dasgupta, John Langford: Importance Weighted Active Learning. CoRR abs/0812.4952 (2008)

Sanjoy Dasgupta, Leonard J. Schulman: A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians. Journal of Machine Learning Research 8: 203-226 (2007)

Sanjoy Dasgupta, Daniel Hsu: On-Line Estimation with the Multivariate Gaussian Distribution. COLT 2007: 278-292

Lawrence Cayton, Sanjoy Dasgupta: A learning framework for nearest neighbor search. NIPS 2007

Sanjoy Dasgupta, Daniel Hsu, Claire Monteleoni: A general agnostic active learning algorithm. NIPS 2007

Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma: Learning the structure of manifolds using random projections. NIPS 2007

Lawrence Cayton, Sanjoy Dasgupta: Robust Euclidean embedding. ICML 2006: 169-176

Sanjoy Dasgupta, Daniel Hsu, Nakul Verma: A Concentration Theorem for Projections. UAI 2006

Sanjoy Dasgupta, Philip M. Long: Performance guarantees for hierarchical clustering. J. Comput. Syst. Sci. 70(4): 555-569 (2005)

Tugkan Batu, Sanjoy Dasgupta, Ravi Kumar, Ronitt Rubinfeld: The Complexity of Approximating the Entropy. SIAM J. Comput. 35(1): 132-150 (2005)

Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni: Analysis of Perceptron-Based Active Learning. COLT 2005: 249-263

Sanjoy Dasgupta: Coarse sample complexity bounds for active learning. NIPS 2005

Sanjoy Dasgupta: Analysis of a greedy active learning strategy. NIPS 2004

Sanjoy Dasgupta, Wee Sun Lee, Philip M. Long: A Theoretical Analysis of Query Selection for Collaborative Filtering. Machine Learning 51(3): 283-298 (2003)

Sanjoy Dasgupta, Anupam Gupta: An elementary proof of a theorem of Johnson and Lindenstrauss. Random Struct. Algorithms 22(1): 60-65 (2003)

Sanjoy Dasgupta, Philip M. Long: Boosting with Diverse Base Classifiers. COLT 2003: 273-287

Sanjoy Dasgupta: Subspace Detection: A Robust Statistics Formulation. COLT 2003: 734

Sanjoy Dasgupta: How Fast Is k-Means? COLT 2003: 735

David Kauchak, Sanjoy Dasgupta: An Iterative Improvement Procedure for Hierarchical Clustering. NIPS 2003

Sanjoy Dasgupta, Elan Pavlov, Yoram Singer: An Efficient PAC Algorithm for Reconstructing a Mixture of Lines. ALT 2002: 351-364

Tugkan Batu, Sanjoy Dasgupta, Ravi Kumar, Ronitt Rubinfeld: The Complexity of Approximating the Entropy. IEEE Conference on Computational Complexity 2002: 17

Sanjoy Dasgupta: Performance Guarantees for Hierarchical Clustering. COLT 2002: 351-363

Tugkan Batu, Sanjoy Dasgupta, Ravi Kumar, Ronitt Rubinfeld: The complexity of approximating entropy. STOC 2002: 678-687

Doina Precup, Richard S. Sutton, Sanjoy Dasgupta: Off-Policy Temporal Difference Learning with Function Approximation. ICML 2001: 417-424

Sanjoy Dasgupta, Michael L. Littman, David A. McAllester: PAC Generalization Bounds for Co-training. NIPS 2001: 375-382

Sanjoy Dasgupta: Experiments with Random Projection. UAI 2000: 143-151

Sanjoy Dasgupta, Leonard J. Schulman: A Two-Round Variant of EM for Gaussian Mixtures. UAI 2000: 152-159

Sanjoy Dasgupta: Learning Mixtures of Gaussians. FOCS 1999: 634-644

Sanjoy Dasgupta: Learning Polytrees. UAI 1999: 134-141

Sanjoy Dasgupta: The Sample Complexity of Learning Fixed-Structure Bayesian Networks. Machine Learning 29(2-3): 165-180 (1997)