Kamalika Chaudhuri

发表

Privacy, accuracy, and consistency too: a holistic solution to contingency table release

Cynthia Dwork, Kamalika Chaudhuri, Kunal Talwar, 2007, PODS.

Multi-view clustering via canonical correlation analysis

Sham M. Kakade, Kamalika Chaudhuri, Karthik Sridharan, 2009, ICML '09.

Spectral Methods for Learning Multivariate Latent Tree Structure

Le Song, Anima Anandkumar, Sham M. Kakade, 2011, NIPS.

Differentially Private Empirical Risk Minimization

Anand D. Sarwate, Kamalika Chaudhuri, Claire Monteleoni, 2009, J. Mach. Learn. Res..

Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics

Jeffrey F. Naughton, Somesh Jha, Xi Wu, 2016, SIGMOD Conference.

Server Allocation Problem for Multi-Tiered Applications

R. Tarjan, Kamalika Chaudhuri, Yunhong Zhou, 2004 .

Near-optimal Differentially Private Principal Components

Anand D. Sarwate, Kamalika Chaudhuri, Kaushik Sinha, 2012, NIPS.

Approximation and Convergence Properties of Generative Adversarial Learning

Kamalika Chaudhuri, Shuang Liu, Olivier Bousquet, 2017, NIPS.

Sample Complexity Bounds for Differentially Private Learning

Kamalika Chaudhuri, Daniel J. Hsu, Kamalika Chaudhuri, 2011, COLT.

When Random Sampling Preserves Privacy

Kamalika Chaudhuri, Nina Mishra, Kamalika Chaudhuri, 2006, CRYPTO.

Rates of convergence for the cluster tree

Sanjoy Dasgupta, Kamalika Chaudhuri, S. Dasgupta, 2010, NIPS.

Learning Mixtures of Product Distributions Using Correlations and Independence

Satish Rao, Kamalika Chaudhuri, Kamalika Chaudhuri, 2008, COLT.

Learning mixtures of distributions

Kamalika Chaudhuri, Satish Rao, 2007 .

On the tandem duplication-random loss model of genome rearrangement

Satish Rao, Kamalika Chaudhuri, Radu Mihaescu, 2006, SODA '06.

Active Heteroscedastic Regression

Nagarajan Natarajan, Prateek Jain, Kamalika Chaudhuri, 2017, ICML.

Variational Bayes In Private Settings (VIPS)

James R. Foulds, Max Welling, Kamalika Chaudhuri, 2016, J. Artif. Intell. Res..

2011 1 The Multi-armed Bandit Problem

Kamalika Chaudhuri, 2011 .

The Expressive Power of Planar Flows

Kamalika Chaudhuri, Zhifeng Kong, 2019 .

Stochastic Multi-Player Bandit Learning from Player-Dependent Feedback

Kamalika Chaudhuri, L. Riek, Chicheng Zhang, 2020 .

Renyi Differential Privacy Mechanisms for Posterior Sampling

Kamalika Chaudhuri, Shuang Song, Joseph Geumlek, 2017, NIPS.

Sentence-level Privacy for Document Embeddings

Kamalika Chaudhuri, Khalil Mrini, Casey Meehan, 2022, ACL.

Efficient Distributed Training in Heterogeneous Mobile Networks with Active Sampling

Kamalika Chaudhuri, T. Simunic, Yunhui Guo, 2020, International Conference on Mobile Ad-hoc and Sensor Networks.

Thompson Sampling for Robust Transfer in Multi-Task Bandits

Kamalika Chaudhuri, Chicheng Zhang, Zhi Wang, 2022, International Conference on Machine Learning.

Special Issue on ALT 2015: Guest Editors' Introduction

Claudio Gentile, Kamalika Chaudhuri, C. Gentile, 2018, Theor. Comput. Sci..

A Three Sample Hypothesis Test for Evaluating Generative Models

Sanjoy Dasgupta, Kamalika Chaudhuri, Casey Meehan, 2020, AISTATS.

Privacy-Aware Compression for Federated Data Analysis

Kamalika Chaudhuri, M. Rabbat, Chuan Guo, 2022, UAI.

Differentially Private Subgraph Counting in the Shuffle Model

Kamalika Chaudhuri, Takao Murakami, Jacob Imola, 2022, arXiv.org.

Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model

Kamalika Chaudhuri, Takao Murakami, Jacob Imola, 2022, CCS.

Signal Processing and Machine Learning with Differential Privacy

Anand D. Sarwate, Kamalika Chaudhuri, Kamalika Chaudhuri, 2013 .

Robust Empirical Risk Minimization with Tolerance

Kamalika Chaudhuri, Max Hopkins, Akash Kumar, 2022, International Conference on Algorithmic Learning Theory.

The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions

Kamalika Chaudhuri, Chicheng Zhang, Kamalika Chaudhuri, 2016, COLT.

A Potential-based Framework for Online Learning with Mistakes and Abstentions

Kamalika Chaudhuri, U. Diego, Chicheng Zhang, 2016 .

Connecting Interpretability and Robustness in Decision Trees through Separation

Kamalika Chaudhuri, Michal Moshkovitz, Yao-Yuan Yang, 2021, ICML.

Universal Approximation of Residual Flows in Maximum Mean Discrepancy pdf

Kamalika Chaudhuri, Zhifeng Kong, Kamalika Chaudhuri, 2021, ArXiv.

Development of an effective method of teaching history in the secondary classes

Kamalika Chaudhuri, Kamalika Chaudhuri, 2005 .

Proceedings of the 26th International Conference on Algorithmic Learning Theory - Volume 9355

Claudio Gentile, Sandra Zilles, Kamalika Chaudhuri, 2015 .

P RIVACY I MPLICATIONS OF S HUFFLING A BSTRACT

Kamalika Chaudhuri, S. Jha, Casey Meehan, 2022 .

Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple’s Count Mean Sketch in Practice

Kamalika Chaudhuri, Jinyuan Jia, Xiaoyu Cao, 2022 .

A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models

Kamalika Chaudhuri, Michal Moshkovitz, Chhavi Yadav, 2022, arXiv.org.

Understanding Rare Spurious Correlations in Neural Networks

Kamalika Chaudhuri, Yao-Yuan Yang, 2022, arXiv.org.

A Two-Stage Active Learning Algorithm for k-Nearest Neighbors

Kamalika Chaudhuri, Nick Rittler, 2022, International Conference on Machine Learning.

Convex Optimization For Non-Convex Problems via Column Generation pdf

Julian Yarkony, Kamalika Chaudhuri, Kamalika Chaudhuri, 2016, ArXiv.

The Expressive Power of a Class of Normalizing Flow Models pdf

Kamalika Chaudhuri, Zhifeng Kong, Kamalika Chaudhuri, 2020, AISTATS.

Finding Metric Structure in Information Theoretic Clustering

Andrew McGregor, Kamalika Chaudhuri, Kamalika Chaudhuri, 2008, COLT.

Capacity Bounded Differential Privacy

Ashwin Machanavajjhala, Kamalika Chaudhuri, Jacob Imola, 2019, NeurIPS.

A rigorous analysis of population stratification with limited data

Eran Halperin, Satish Rao, Kamalika Chaudhuri, 2007, SODA '07.

Stochastic gradient descent with differentially private updates

Anand D. Sarwate, Kamalika Chaudhuri, Shuang Song, 2013, 2013 IEEE Global Conference on Signal and Information Processing.

Throwing Away Data Improves Worst-Class Error in Imbalanced Classification

Kamalika Chaudhuri, Martín Arjovsky, David Lopez-Paz, 2022, ArXiv.

Robustness of Locally Differentially Private Graph Analysis Against Poisoning

Kamalika Chaudhuri, A. Chowdhury, Jacob Imola, 2022, ArXiv.

An Extension of Scalable Global IP Anycasting for Load Balancing in the Internet

Kamalika Chaudhuri, Siddhartha Saha, Dheeraj Sanghi, 2003, ICOIN.

iDASH: integrating data for analysis, anonymization, and sharing

Jihoon Kim, Xiaoqian Jiang, Lucila Ohno-Machado, 2012, J. Am. Medical Informatics Assoc..

A Non-Parametric Test to Detect Data-Copying in Generative Models pdf

Sanjoy Dasgupta, Kamalika Chaudhuri, Casey Meehan, 2020, ArXiv.

A Stability-based Validation Procedure for Differentially Private Machine Learning

Kamalika Chaudhuri, Staal A. Vinterbo, Kamalika Chaudhuri, 2013, NIPS.

Convergence Rates for Differentially Private Statistical Estimation

Kamalika Chaudhuri, Daniel J. Hsu, Kamalika Chaudhuri, 2012, ICML.

Probing Predictions on OOD Images via Nearest Categories

R. Salakhutdinov, Kamalika Chaudhuri, Cyrus Rashtchian, 2020, 2011.08485.

Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model

Fan Chung Graham, Kamalika Chaudhuri, Alexander Tsiatas, 2012, COLT.

Spectral Learning of Binomial HMMs for DNA Methylation Data pdf

Kamalika Chaudhuri, Chicheng Zhang, Eran A. Mukamel, 2018, ArXiv.

Spectral Learning of Large Structured HMMs for Comparative Epigenomics

Kamalika Chaudhuri, Chicheng Zhang, Kevin C. Chen, 2015, NIPS.

Robust Empirical Risk Minimization with Tolerance

Kamalika Chaudhuri, Max Hopkins, Akash Kumar, 2022, ALT.

Consistent Non-Parametric Methods for Maximizing Robustness

Kamalika Chaudhuri, Robi Bhattacharjee, 2021, NeurIPS.

When are Non-Parametric Methods Robust?

Kamalika Chaudhuri, Robi Bhattacharjee, 2020, ICML.

Adversarial Examples for Non-Parametric Methods: Attacks, Defenses and Large Sample Limits

Kamalika Chaudhuri, Cyrus Rashtchian, Yao-Yuan Yang, 2019, ArXiv.

Analyzing the Robustness of Nearest Neighbors to Adversarial Examples

Somesh Jha, Yizhen Wang, Kamalika Chaudhuri, 2017, ICML.

Location determination of a mobile device using IEEE 802.11b access point signals

Kamalika Chaudhuri, Pravin Bhagwat, Siddhartha Saha, 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

Exploring Connections Between Active Learning and Model Extraction

Somesh Jha, Kamalika Chaudhuri, Varun Chandrasekaran, 2018, USENIX Security Symposium.

Differentially Private Support Vector Machines pdf

Anand D. Sarwate, Kamalika Chaudhuri, Claire Monteleoni, 2009, ArXiv.

Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations

Kamalika Chaudhuri, James Zou, James Y. Zou, 2020, AISTATS.

Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations pdf

Kamalika Chaudhuri, James Zou, James Y. Zou, 2020, ArXiv.

A Closer Look at Accuracy vs. Robustness

Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri, 2020, NeurIPS.

The Inductive Bias of Restricted f-GANs pdf

Kamalika Chaudhuri, Shuang Liu, Kamalika Chaudhuri, 2018, ArXiv.

PABLO: Helping Novices Debug Python Code Through Data-Driven Fault Localization

Kamalika Chaudhuri, Ranjit Jhala, B. Cosman, 2020, SIGCSE.

Privacy-preserving logistic regression

Kamalika Chaudhuri, Claire Monteleoni, Kamalika Chaudhuri, 2008, NIPS.

Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics pdf

Jeffrey F. Naughton, Somesh Jha, Xi Wu, 2016, ArXiv.

Privacy Amplification Via Bernoulli Sampling pdf

Kamalika Chaudhuri, Jacob Imola, Kamalika Chaudhuri, 2021, ArXiv.

A Learning-Theoretic Framework for Certified Auditing with Explanations

Kamalika Chaudhuri, Michal Moshkovitz, Chhavi Yadav, 2022, 2206.04740.

Data Redaction from Conditional Generative Models

Kamalika Chaudhuri, Zhifeng Kong, 2023, ArXiv.

Data Redaction from Pre-trained GANs

Kamalika Chaudhuri, Zhifeng Kong, 2022, 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML).

Data Poisoning Attacks against Online Learning pdf

Yizhen Wang, Kamalika Chaudhuri, Kamalika Chaudhuri, 2018, ArXiv.

Trustworthy AI Inference Systems: An Industry Research View

Fateme S. Hosseini, Kamalika Chaudhuri, F. Koushanfar, 2020, ArXiv.

Adversarial Robustness Through Local Lipschitzness pdf

Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri, 2020, ArXiv.

Location Trace Privacy Under Conditional Priors pdf

Kamalika Chaudhuri, Casey Meehan, Kamalika Chaudhuri, 2019, AISTATS.

Forgetting Data from Pre-trained GANs

Kamalika Chaudhuri, Zhifeng Kong, 2022, ArXiv.

A near-optimal algorithm for differentially-private principal components pdf

Anand D. Sarwate, Kamalika Chaudhuri, Kaushik Sinha, 2012, J. Mach. Learn. Res..

Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data

Anand D. Sarwate, Kamalika Chaudhuri, Kamalika Chaudhuri, 2013, IEEE Signal Processing Magazine.

An Investigation of Data Poisoning Defenses for Online Learning pdf

Yizhen Wang, Kamalika Chaudhuri, Kamalika Chaudhuri, 2019, ArXiv.

Bounding Training Data Reconstruction in Private (Deep) Learning

Kamalika Chaudhuri, B. Karrer, Chuan Guo, 2022, International Conference on Machine Learning.

Pufferfish Privacy Mechanisms for Correlated Data

Yizhen Wang, Kamalika Chaudhuri, Shuang Song, 2016, SIGMOD Conference.

The Large Margin Mechanism for Differentially Private Maximization

Kamalika Chaudhuri, Shuang Song, Daniel J. Hsu, 2014, NIPS.

Value-maximizing deadline scheduling and its application to animation rendering

Dirk Beyer, Robert E. Tarjan, Eric Anderson, 2005, SPAA '05.

Variational Bayes in Private Settings (VIPS) (Extended Abstract)

James R. Foulds, Max Welling, Kamalika Chaudhuri, 2020, IJCAI.

ON THE COMPLEXITY OF THE GAME OF SET

Kamalika Chaudhuri, Brighten Godfrey, KAMALIKA CHAUDHURI, 2003 .

Why does Throwing Away Data Improve Worst-Group Error?

Kamalika Chaudhuri, Martín Arjovsky, David Lopez-Paz, 2022, ICML.

Can Membership Inferencing be Refuted?

Kamalika Chaudhuri, A. Chowdhury, Zhifeng Kong, 2023, ArXiv.

Forgeability and Membership Inference Attacks

Kamalika Chaudhuri, Zhifeng Kong, Amrita Roy Chowdhury, 2022, AISec@CCS.

On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis pdf

James R. Foulds, Max Welling, Kamalika Chaudhuri, 2016, UAI.

Rates of Convergence for Nearest Neighbor Classification

Sanjoy Dasgupta, Kamalika Chaudhuri, S. Dasgupta, 2014, NIPS.

An Online Learning-based Framework for Tracking

Yoav Freund, Kamalika Chaudhuri, Daniel J. Hsu, 2010, UAI.

Privacy Implications of Shuffling pdf

Somesh Jha, Amrita Roy Chowdhury, Kamalika Chaudhuri, 2021, ICLR.

Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning

Kamalika Chaudhuri, Pascal Vincent, Florian Bordes, 2023, ArXiv.

ViP: A Differentially Private Foundation Model for Computer Vision

Kamalika Chaudhuri, Maziar Sanjabi, Chuan Guo, 2023, ArXiv.

A Parameter-free Hedging Algorithm

Yoav Freund, Kamalika Chaudhuri, Daniel J. Hsu, 2009, NIPS.

Robustness for Non-Parametric Classification: A Generic Attack and Defense

Kamalika Chaudhuri, Cyrus Rashtchian, Yao-Yuan Yang, 2019, AISTATS.

Sample Complexity of Robust Linear Classification on Separated Data

Somesh Jha, Kamalika Chaudhuri, Robi Bhattacharjee, 2021, ICML.

Online Bipartite Perfect Matching With Augmentations

Henry Lin, Kamalika Chaudhuri, Constantinos Daskalakis, 2009, IEEE INFOCOM 2009.

Active Learning from Weak and Strong Labelers

Kamalika Chaudhuri, Chicheng Zhang, Kamalika Chaudhuri, 2015, NIPS.

Convergence Rates of Active Learning for Maximum Likelihood Estimation

Sham M. Kakade, Kamalika Chaudhuri, Sujay Sanghavi, 2015, NIPS.

Privacy Amplification via Shuffling for Linear Contextual Bandits pdf

Vianney Perchet, Kamalika Chaudhuri, Matteo Pirotta, 2021, ALT.

Communication-Efficient Triangle Counting under Local Differential Privacy

Kamalika Chaudhuri, Takao Murakami, Jacob Imola, 2021, USENIX Security Symposium.

Locally Differentially Private Analysis of Graph Statistics

Kamalika Chaudhuri, Takao Murakami, Jacob Imola, 2020, USENIX Security Symposium.

Paths, trees, and minimum latency tours

Satish Rao, Brighten Godfrey, Kamalika Chaudhuri, 2003, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings..

Learning to blame: localizing novice type errors with data-driven diagnosis

Kamalika Chaudhuri, Westley Weimer, Ranjit Jhala, 2017, Proc. ACM Program. Lang..

Understanding Instance-based Interpretability of Variational Auto-Encoders pdf

Kamalika Chaudhuri, Zhifeng Kong, Kamalika Chaudhuri, 2021, NeurIPS.

Selfish caching in distributed systems: a game-theoretic analysis

Christos H. Papadimitriou, John Kubiatowicz, Byung-Gon Chun, 2004, PODC '04.

Beyond Disagreement-Based Agnostic Active Learning

Kamalika Chaudhuri, Chicheng Zhang, Kamalika Chaudhuri, 2014, NIPS.

Profile-based Privacy for Locally Private Computations

Kamalika Chaudhuri, Joseph Geumlek, Kamalika Chaudhuri, 2019, 2019 IEEE International Symposium on Information Theory (ISIT).

Successive Refinement of Privacy

Christina Fragouli, Kamalika Chaudhuri, Suhas Diggavi, 2020, IEEE Journal on Selected Areas in Information Theory.

Agnostic Multi-Group Active Learning

Kamalika Chaudhuri, Nick Rittler, 2023, arXiv.org.

The Label Complexity of Active Learning from Observational Data

Tara Javidi, Kamalika Chaudhuri, Songbai Yan, 2019, NeurIPS.

Model Extraction and Active Learning

Kamalika Chaudhuri, S. Jha, Varun Chandrasekaran, 2018, ArXiv.

Active Learning from Imperfect Labelers

Tara Javidi, Kamalika Chaudhuri, Songbai Yan, 2016, NIPS.

Improved Algorithms for Confidence-Rated Prediction with Error Guarantees

Kamalika Chaudhuri, Chicheng Zhang, Kamalika Chaudhuri, 2013 .

Composition properties of inferential privacy for time-series data

Kamalika Chaudhuri, Shuang Song, Kamalika Chaudhuri, 2017, 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice

Kamalika Chaudhuri, Jinyuan Jia, Xiaoyu Cao, 2023, USENIX Security Symposium.

Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design

Michael G. Rabbat, Kamalika Chaudhuri, Pierre Stock, 2022, ICML.

The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning

Michael G. Rabbat, Kamalika Chaudhuri, Pierre Stock, 2022, ArXiv.

Active Learning with Logged Data pdf

Tara Javidi, Kamalika Chaudhuri, Songbai Yan, 2018, ICML.

A Shuffling Framework for Local Differential Privacy

Kamalika Chaudhuri, S. Jha, A. Chowdhury, 2021, arXiv.org.

Server Allocation Algorithms for Tiered Systems

Robert E. Tarjan, Kamalika Chaudhuri, Rudi Pendavingh, 2005, Algorithmica.

Consistent Procedures for Cluster Tree Estimation and Pruning

Ulrike von Luxburg, Sanjoy Dasgupta, Kamalika Chaudhuri, 2014, IEEE Transactions on Information Theory.

Large-Scale Public Data Improves Differentially Private Image Generation Quality

Kamalika Chaudhuri, Chuan Guo, Ruihan Wu, 2023, ArXiv.

Close Category Generalization for Out-of-Distribution Classification pdf

Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri, 2020, ArXiv.

Privacy-preserving logistic regression

Kamalika Chaudhuri, C. Monteleoni, 2008, NIPS.

Tracking using explanation-based modeling

Daniel J. Hsu, Y. Freund, Kamalika Chaudhuri, 2009, arXiv.org.

Tracking Using an Explanatory Framework pdf

Yoav Freund, Kamalika Chaudhuri, Daniel J. Hsu, 2010 .

What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTs

Satish Rao, Kamalika Chaudhuri, Samantha Riesenfeld, 2009, Algorithmica.

A Push-Relabel Algorithm for Approximating Degree Bounded MSTs

Satish Rao, Kamalika Chaudhuri, Samantha Riesenfeld, 2006, ICALP.

Learning Mixtures of Gaussians using the k-means Algorithm pdf

Sanjoy Dasgupta, Kamalika Chaudhuri, Andrea Vattani, 2009, ArXiv.

Multitask Bandit Learning through Heterogeneous Feedback Aggregation

Kamalika Chaudhuri, Laurel D. Riek, Chicheng Zhang, 2020, AISTATS.

Multi-view clustering via canonical correlation analysis

S. Kakade, Kamalika Chaudhuri, Karthik Sridharan, 2009, ICML '09.

Sample Complexity of Adversarially Robust Linear Classification on Separated Data

Kamalika Chaudhuri, S. Jha, Robi Bhattacharjee, 2020, International Conference on Machine Learning.

Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning

Kamalika Chaudhuri, Pascal Vincent, Florian Bordes, 2023, ArXiv.

Differentially Private Continual Release of Graph Statistics pdf

Kamalika Chaudhuri, Staal A. Vinterbo, Sanjay Mehta, 2018, ArXiv.

Consistent Non-Parametric Methods for Adaptive Robustness pdf

Kamalika Chaudhuri, Robi Bhattacharjee, Kamalika Chaudhuri, 2021, ArXiv.

What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTs

Satish Rao, Kamalika Chaudhuri, Samantha Riesenfeld, 2005, APPROX-RANDOM.

A Closer Look at Accuracy vs. Robustness

R. Salakhutdinov, Kamalika Chaudhuri, Cyrus Rashtchian, 2020, NeurIPS.

Learning from Data with Heterogeneous Noise using SGD

Anand D. Sarwate, Kamalika Chaudhuri, Shuang Song, 2014, AISTATS.