Two papers accepted to SIGMOD 2017!
Google Faculty Research Award, 2017. Thanks, Google!
Paper accepted to AISTATS 2017!
I am a Tutorials Co-Chair for ICML 2017.
Upcoming talk in the Statistics Department, UCLA (Apr 2017).
Upcoming keynote talk at the 11th Annual Machine Learning Symposium at the New York Academy of Sciences (Mar 2017).
I am an Area Chair for ICML 2017 and UAI 2017, and a PC Member for COLT 2017.
I am the Publications Chair for COLT 2017.
Upcoming talk at the Simons Workshop on Interactive Learning (Feb 2017).
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 awards from the National Science Foundation, the Office of Naval Research and Bloomberg LLC. In the past, my research has been supported by the National Institute of Health and the Hellman Foundation.
Shuang Song (PhD Student)
Chicheng Zhang (PhD Student)
Joseph Geumlek (PhD Student)
Yizhen Wang (PhD Student)
Shuang Liu (PhD Student)
Huma Sibghat (PhD Student)
Edward Wong (Undergraduate Student)
Charles Packer (Undergraduate Student)
Siyang Wang (Undergraduate Student)
Judy Zhou (Undergraduate Student)
Ruiqing Qiu (Undergraduate Student)
Mingshan Wang (Undergraduate Student)
Sumithra Velupillai (Visiting Scholar)
Enrico Tanuwidjaja (Undergraduate Student)
Trung Thanh Lam (Undergraduate Student)
Hesler Rodriguez (Undergraduate Student)
Bowen Shu (Undergraduate Student)
This is what Wordle thinks of my publication titles.
Variational Bayes in Private Settings (VIPS)
Mijung Park, James Foulds, Kamalika Chaudhuri and Max Welling, Arxiv Pre-print, 2016
Learning Mixtures of Gaussians using the k-means Algorithm
Kamalika Chaudhuri, Sanjoy Dasgupta and Andrea Vattani, Arxiv Pre-print, 2009
Bolt-On Differential Privacy for Stochastic Gradient Descent-based Analytics
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha and Jeff Naughton, ACM SIGMOD International Conference on Management of Data (SIGMOD), 2017 (to appear)
Pufferfish Privacy Mechanisms for Correlated Data
Shuang Song, Yizhen Wang and Kamalika Chaudhuri, ACM SIGMOD International Conference on Management of Data (SIGMOD), 2017 (to appear)
Practical Privacy for Expectation Maximization
Mijung Park, James Foulds, Kamalika Chaudhuri and Max Welling, International Conference on Artificial Intelligence and Statistics (AISTATS), 2017 (to appear)
Private Topic Modeling
Mijung Park, James Foulds, Kamalika Chaudhuri and Max Welling, NIPS Workshop on Private Multi-party Machine Learning, 2016
Active Learning from Imperfect Labelers
Songbai Yan, Kamalika Chaudhuri and Tara Javidi, Neural Information Processing Systems (NIPS) 2016
On the Theory and Practice of Privacy-preserving Bayesian Data Analysis
James Foulds, Joseph Geumlek, Max Welling and Kamalika Chaudhuri, Uncertainty in Artificial Intelligence (UAI) 2016
The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions
Chicheng Zhang and Kamalika Chaudhuri, Conference on Learning Theory (COLT) 2016
Spectral Learning of Large Structured HMMs for Comparative Epigenomics
Chicheng Zhang, Jimin Song, Kamalika Chaudhuri and Kevin Chen, Neural Information Processing Systems (NIPS) 2015 [Code]
Active Learning from Weak and Strong Labelers
Chicheng Zhang and Kamalika Chaudhuri, Neural Information Processing Systems (NIPS) 2015
Convergence Rates of Active Learning for Maximum Likelihood Estimation
Kamalika Chaudhuri, Sham Kakade, Praneeth Netrapalli and Sujay Sanghavi, Neural Information Processing Systems (NIPS) 2015
Active Learning from Noisy and Abstention Feedback
Songbai Yan, Kamalika Chaudhuri and Tara Javidi, Allerton Conference on Communication, Control and Computing, 2015.
Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons
James Y. Zou, Kamalika Chaudhuri and Adam Tauman Kalai, Conference on Human Computation and Crowdsourcing (HCOMP) 2015
Noisy Bayesian Active Learning
Mohammad Naghshvar, Tara Javidi and Kamalika Chaudhuri, IEEE Transactions of Information Theory, 2015
Learning from Data with Heterogenous Noise using SGD
Shuang Song, Kamalika Chaudhuri and Anand D. Sarwate, International Conference on Artificial Intelligence and Statistics (AISTATS) 2015
The Large Margin Mechanism for Differentially Private Maximization
Kamalika Chaudhuri, Daniel Hsu and Shuang Song, Neural Information Processing Systems (NIPS) 2014
Beyond Disagreement-Based Agnostic Active Learning
Chicheng Zhang and Kamalika Chaudhuri, Neural Information Processing Systems (NIPS) 2014
Rates of Convergence for Nearest Neighbor Classification
Kamalika Chaudhuri and Sanjoy Dasgupta, Neural Information Processing Systems (NIPS) 2014
Consistent Procedures for Cluster Tree Estimation and Pruning
Kamalika Chaudhuri, Sanjoy Dasgupta, Samory Kpotufe and Ulrike Von Luxburg, IEEE Transactions of Information Theory, 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
Learning Mixtures of Distributions
Kamalika Chaudhuri, Ph.D Dissertation,
UC Berkeley, 2007