Kamalika Chaudhuri

Assistant Professor
Computer Science and Engineering
University of California, San Diego
Office: EBU3B 4110
email: kamalika at cs dot ucsd dot edu
phone: 858-822-7703


Upcoming talk at UT Austin (Feb 2015)
Upcoming talk at the Simons Workshop on Information Theory, Learning and Big Data.
Upcoming talk at Microsoft Research New York City.
Upcoming talk at Harvard University.
Three papers accepted to NIPS 2014.
New pre-print on privacy-preserving learning now available on arxiv.
Upcoming tutorial with Anand Sarwate on Differential Privacy and Machine Learning at WIFS 14.
New pre-print on nearest neighbor classification now available on arxiv.
New pre-print on active learning now available on arxiv.



My research is on machine learning. Much of my work is on privacy-preserving machine learning and unsupervised learning, but I am broadly interested in a number of topics in learning theory, such as confidence-rated prediction, online learning, and active learning.

Here is a survey I wrote on machine learning with privacy. Here is an overview I wrote in 2008 about learning mixture models. Here is a press article on Biomedical Computation Review that talks about some of my work on privacy-preserving machine learning.

My research is supported by the National Science Foundation, and the National Institute of Health. In the past, my research has been supported by the Hellman Foundation.



Current

Shuang Song (PhD Student)

Chicheng Zhang (PhD Student)

Sumithra Velupillai (Visiting Scholar)

Sonali Rahagude (MS Student)

Xinzhe Xu (Undergraduate Student)

Alumni

Enrico Tanuwidjaja (Undergraduate Student)

Trung Thanh Lam (Undergraduate Student)

Hesler Rodriguez (Undergraduate Student)

Bowen Shu (Undergraduate Student)



CSE 151: Introduction to AI: A Statistical Approach
CSE 202: Graduate Algorithms
CSE 291: Machine Learning Theory
CSE 291: Topics in Online Learning and Bandit Problems


This is what Wordle thinks of my publication titles.

Noisy Bayesian Active Learning
Mohammad Naghshvar, Tara Javidi and Kamalika Chaudhuri, Arxiv Pre-print, 2013

Learning Mixtures of Gaussians using the k-means Algorithm
Kamalika Chaudhuri, Sanjoy Dasgupta and Andrea Vattani, Arxiv Pre-print, 2009

The Large Margin Mechanism for Differentially Private Maximization
Kamalika Chaudhuri, Daniel Hsu and Shuang Song, Neural Information Processing Systems (NIPS) 2014 (to appear)

Beyond Disagreement-Based Agnostic Active Learning
Chicheng Zhang and Kamalika Chaudhuri, Neural Information Processing Systems (NIPS) 2014 (to appear)

Rates of Convergence for Nearest Neighbor Classification
Kamalika Chaudhuri and Sanjoy Dasgupta, Neural Information Processing Systems (NIPS) 2014 (to appear)

Consistent Procedures for Cluster Tree Estimation and Pruning
Kamalika Chaudhuri, Sanjoy Dasgupta, Samory Kpotufe and Ulrike Von Luxburg, IEEE Transactions of Information Theory (to appear), 2014

Improved Algorithms for Confidence-Rated Prediction with Error Guarantees
Kamalika Chaudhuri and Chicheng Zhang, NIPS Workshop on Learning Faster From Easy Data, NIPS 2013

A Stability-based Validation Procedure for Differentially Private Machine Learning
Kamalika Chaudhuri and Staal Vinterbo, Neural Information Processing Systems (NIPS), 2013

Stochastic Gradient Descent with Differentially Private Updates
Shuang Song, Kamalika Chaudhuri and Anand Sarwate, GlobalSIP Conference, 2013

Signal Processing and Machine Learning with Differential Privacy: Theory, Algorithms and Challenges
Anand Sarwate and Kamalika Chaudhuri, IEEE Signal Processing Magazine, 2013

Near-Optimal Algorithms for Differentially Private Principal Components
Kamalika Chaudhuri, Anand Sarwate and Kaushik Sinha, Neural Information Processing Systems (NIPS), 2012

Convergence Rates for Differentially Private Statistical Estimation
Kamalika Chaudhuri and Daniel Hsu, International Conference on Machine Learning (ICML), 2012

Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model
Kamalika Chaudhuri, Fan Chung and Alexander Tsiatas, Conference on Learning Theory (COLT), 2012

Spectral Methods for Learning Multivariate Latent Tree Structure
Animashree Anandkumar, Kamalika Chaudhuri, Daniel Hsu, Sham Kakade, Le Song and Tong Zhang, Neural Information Processing Systems (NIPS), 2011.

Sample Complexity Bounds for Differentially Private Learning
Kamalika Chaudhuri and Daniel Hsu, Conference on Learning Theory (COLT), 2011

Differentially Private ERM
Kamalika Chaudhuri, Claire Monteleoni, and Anand Sarwate, Journal of Machine Learning Research (JMLR), 2011. A previous version appeared in Neural Information Processing Systems (NIPS), 2008.

Rates of Convergence for the Cluster Tree
Kamalika Chaudhuri and Sanjoy Dasgupta, Neural Inf. Processing Systems (NIPS), 2010.

An Online Learning-based Framework for Tracking
Kamalika Chaudhuri, Yoav Freund and Daniel Hsu, Uncertainty in Artificial Intelligence (UAI), 2010

A New Parameter-Free Hedging Algorithm
Kamalika Chaudhuri, Yoav Freund and Daniel Hsu, Neural Information Processing Systems (NIPS), 2009

Online Bipartite Matching with Augmentations
Kamalika Chaudhuri, Costis Daskalakis, Robert Kleinberg and Henry Lin, International Conf. on Computer Communications (INFOCOM), 2009

Multiview Clustering via Canonical Correlation Analysis
Kamalika Chaudhuri , Sham Kakade, Karen Livescu and Karthik Sridharan, International Conf. on Machine Learning (ICML), 2009. [Full proofs ]

A Network Coloring Game
Kamalika Chaudhuri, Fan Chung Graham, Mohammad S. Jamall, Workshop on Internet and Network Econimics (WINE), 2008.

Finding Metric Structure in Information-Theoretic Clustering
Kamalika Chaudhuri and Andrew McGregor, Conference on Learning Theory (COLT), 2008

Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed Distributions
Kamalika Chaudhuri and Satish Rao, Conference on Learning Theory (COLT), 2008

Learning Mixtures of Product Distributions using Correlations and Independence
Kamalika Chaudhuri and Satish Rao, Conference on Learning Theory (COLT), 2008

Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release
Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank Mcsherry and Kunal Talwar, Principles of Database Systems (PODS), 2007

A Rigorous Analysis of Population Stratification with Limited Data
Kamalika Chaudhuri, Eran Halperin, Satish Rao and Shuheng Zhou, Symposium on Discrete Algorithms (SODA), 2007 [Slides]

Push-Relabel and an Improved Approximation Algorithm for the Bounded-degree MST Problem
Kamalika Chaudhuri, Satish Rao, Samantha Riesenfeld, and Kunal Talwar, International Conference on Automata, Languages, and Programming (ICALP), 2006.

When Random Sampling preserves Privacy
Kamalika Chaudhuri and Nina Mishra, International Cryptology Conference (CRYPTO), 2006

On the tandem duplication-random loss model of genome rearrangement
Kamalika Chaudhuri, Kevin Chen, Radu Mihaescu, and Satish Rao, Symposium of Discrete Algorithms (SODA), 2006

Server Allocation Algorithms for Tiered Systems
Kamalika Chaudhuri, Anshul Kothari, Rudi Pendavingh, Ram Swaminathan, Robert Tarjan, and Yunhong Zhou, International Computing and Combinatorics Conference (COCOON), 2005

What would Edmonds do? Augmenting Paths, Witnesses and Improved Approximations for Bounded-degree MSTs
Kamalika Chaudhuri, Satish Rao, Samantha Riesenfeld, and Kunal Talwar, Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), 2005. [Slides]

Value-Maximizing Deadline Scheduling and its Application to Animation Rendering
Eric Anderson, Dirk Beyer, Kamalika Chaudhuri, Terrance Kelly, Norman Salazar, Ciprano Santos, Ram Swaminathan, Robert Tarjan, Janet Wiener, and Yunhong Zhou, Symposium on Parallelism in Algorithms and Architecture (SPAA), 2005

Selfish Caching in Distributed Systems: A Game Theoretic Analysis
Byung-Gon Chun, Kamalika Chaudhuri, Hoeteck Wee, Marco Barreno, Christos Papadimitriou, and John Kubiatowicz, Principles of Distributed Computing (PODC), 2004

Paths, Trees and Minimum Latency Tours
Kamalika Chaudhuri, Brighten Godfrey, Satish Rao, and Kunal Talwar, Foundations of Computer Science (FOCS), 2003. [Slides]

Learning Mixtures of Distributions
Kamalika Chaudhuri, Ph.D Dissertation,
UC Berkeley, 2007