Can we use spectral learning to analyze large epigenomic data sets and clarify human disease mechanisms? Find out in our new pre-print on spectral learning for comparative epigenomics, now available on arxiv.
Paper accepted to HCOMP!
Do more rounds of interaction always lead to better active learning? Find out in our new pre-print on active learning, now available on arxiv.
Paper accepted to IEEE Transactions on Information Theory.
Can we get humans to give us good features for machine learning? Find out in our new pre-print on feature learning via crowd-sourcing, now available on arxiv.
Upcoming talk at the Nexus of Information and Computation Theories Workshop at IHP Paris (Mar 2016).
Slides for my tutorial with Anand Sarwate on Differential Privacy and Machine Learning at GlobalSIP/WIFS are now online! The tutorial is based on our survey on the same topic.
Upcoming talk at the Stochastic Methods in Game Theory Workshop at NUS Singapore (Nov 2015).
Upcoming talk at the External Seminar, Gatsby Unit (Jul 2015).
I am the PC Co-Chair for ALT 2015.
Shachar Lovett and I are organizing a Workshop on Algorithmic Challenges in Machine Learning at UCSD.
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.
Sumithra Velupillai (Visiting Scholar)
Shuang Song (PhD Student)
Chicheng Zhang (PhD Student)
Joseph Geumlek (PhD Student)
Yan Shu (PhD Student)
Yizhen Wang (PhD Student)
Songbai Yan (PhD Student, jointly advised with Tara Javidi)
Ruiqing Qiu (Undergraduate Student)
Mingshan Wang (Undergraduate Student)
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.
Spectral Learning of Large Structured HMMs for Comparative Epigenomics
Chicheng Zhang, Jimin Song, Kamalika Chaudhuri and Kevin Chen, Arxiv Pre-print, 2015
Convergence Rates of Active Learning for Maximum Likelihood Estimation
Kamalika Chaudhuri, Sham Kakade, Praneeth Netrapalli and Sujay Sanghavi, Arxiv Pre-print, 2015
Learning Mixtures of Gaussians using the k-means Algorithm
Kamalika Chaudhuri, Sanjoy Dasgupta and Andrea Vattani, Arxiv Pre-print, 2009
Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons
James Y. Zou, Kamalika Chaudhuri and Adam Tauman Kalai, HCOMP 2015 (to appear)
Noisy Bayesian Active Learning
Mohammad Naghshvar, Tara Javidi and Kamalika Chaudhuri, IEEE Transactions of Information Theory (to appear), 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