Kamalika Chaudhuri

According to our database, Kamalika Chaudhuri authored at least 164 papers between 2003 and 2024.

Collaborative distances:

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2024

XAudit : A Learning-Theoretic Look at Auditing with Explanations.

Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri

Trans. Mach. Learn. Res., 2024

Auditing <i>f</i>-Differential Privacy in One Run.

Saeed Mahloujifar, Luca Melis, Kamalika Chaudhuri

CoRR, 2024

Distribution Learning with Valid Outputs Beyond the Worst-Case.

Nick Rittler, Kamalika Chaudhuri

CoRR, 2024

Evaluating Deep Unlearning in Large Language Models.

Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov, Kamalika Chaudhuri

CoRR, 2024

Aligning LLMs to Be Robust Against Prompt Injection.

Sizhe Chen, Arman Zharmagambetov, Saeed Mahloujifar, Kamalika Chaudhuri, Chuan Guo

CoRR, 2024

Influence-based Attributions can be Manipulated.

Chhavi Yadav, Ruihan Wu, Kamalika Chaudhuri

CoRR, 2024

On Differentially Private U Statistics.

Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar

CoRR, 2024

Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift.

Robi Bhattacharjee, Nick Rittler, Kamalika Chaudhuri

CoRR, 2024

An Introduction to Vision-Language Modeling.

Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay, Alexander C. Li, Adrien Bardes, Suzanne Petryk, Oscar Mañas, Zhiqiu Lin, Anas Mahmoud, Bargav Jayaraman, Mark Ibrahim, Melissa Hall, Yunyang Xiong, Jonathan Lebensold, Candace Ross, Srihari Jayakumar, Chuan Guo, Diane Bouchacourt, Haider Al-Tahan, Karthik Padthe, Vasu Sharma, Hu Xu, Xiaoqing Ellen Tan, Megan Richards, Samuel Lavoie, Pietro Astolfi, Reyhane Askari Hemmat, Jun Chen, Kushal Tirumala, Rim Assouel, Mazda Moayeri, Arjang Talattof, Kamalika Chaudhuri, Zechun Liu, Xilun Chen, Quentin Garrido, Karen Ullrich, Aishwarya Agrawal, Kate Saenko, Asli Celikyilmaz, Vikas Chandra

CoRR, 2024

Better Membership Inference Privacy Measurement through Discrepancy.

Ruihan Wu, Pengrun Huang, Kamalika Chaudhuri

CoRR, 2024

Metric Differential Privacy at the User-Level.

Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri

CoRR, 2024

Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations.

Christian Tomani, Kamalika Chaudhuri, Ivan Evtimov, Daniel Cremers, Mark Ibrahim

CoRR, 2024

Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds.

Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert

CoRR, 2024

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning.

Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero-Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo

CoRR, 2024

Privacy Amplification for the Gaussian Mechanism via Bounded Support.

Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo

CoRR, 2024

Measuring Privacy Loss in Distributed Spatio-Temporal Data.

Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri

CoRR, 2024

Déjà Vu Memorization in Vision-Language Models.

Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri

CoRR, 2024

Data Redaction from Conditional Generative Models.

Zhifeng Kong, Kamalika Chaudhuri

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

Differentially Private Multi-Site Treatment Effect Estimation.

Tatsuki Koga, Kamalika Chaudhuri, David Page

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

ViP: A Differentially Private Foundation Model for Computer Vision.

Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo

Proceedings of the Forty-first International Conference on Machine Learning, 2024

FairProof : Confidential and Certifiable Fairness for Neural Networks.

Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Differentially Private Representation Learning via Image Captioning.

Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Effective pruning of web-scale datasets based on complexity of concept clusters.

Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Metric Differential Privacy at the User-Level via the Earth-Mover's Distance.

Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri

Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024

2023

Probing Predictions on OOD Images via Nearest Categories.

Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri

Trans. Mach. Learn. Res., 2023

Unified Uncertainty Calibration.

Kamalika Chaudhuri, David Lopez-Paz

CoRR, 2023

Large-Scale Public Data Improves Differentially Private Image Generation Quality.

Ruihan Wu, Chuan Guo, Kamalika Chaudhuri

CoRR, 2023

Can Membership Inferencing be Refuted?

Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri

CoRR, 2023

Data Redaction from Pre-trained GANs.

Zhifeng Kong, Kamalika Chaudhuri

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

Agnostic Multi-Group Active Learning.

Nicholas Rittler, Kamalika Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Casey Meehan, Florian Bordes, Pascal Vincent, Kamalika Chaudhuri, Chuan Guo

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Nicholas Rittler, Kamalika Chaudhuri

Proceedings of the International Conference on Machine Learning, 2023

Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design.

Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat

Proceedings of the International Conference on Machine Learning, 2023

Why does Throwing Away Data Improve Worst-Group Error?

Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz

Proceedings of the International Conference on Machine Learning, 2023

Data-Copying in Generative Models: A Formal Framework.

Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri

Proceedings of the International Conference on Machine Learning, 2023

Robust Empirical Risk Minimization with Tolerance.

Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022

The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning.

Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat

CoRR, 2022

Robustness of Locally Differentially Private Graph Analysis Against Poisoning.

Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri

CoRR, 2022

Forgetting Data from Pre-trained GANs.

Zhifeng Kong, Kamalika Chaudhuri

CoRR, 2022

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

Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri

CoRR, 2022

Throwing Away Data Improves Worst-Class Error in Imbalanced Classification.

Martín Arjovsky, Kamalika Chaudhuri, David Lopez-Paz

CoRR, 2022

Differentially Private Subgraph Counting in the Shuffle Model.

Jacob Imola, Takao Murakami, Kamalika Chaudhuri

CoRR, 2022

Understanding Rare Spurious Correlations in Neural Networks.

Yao-Yuan Yang, Kamalika Chaudhuri

CoRR, 2022

Communication-Efficient Triangle Counting under Local Differential Privacy.

Jacob Imola, Takao Murakami, Kamalika Chaudhuri

Proceedings of the 31st USENIX Security Symposium, 2022

Privacy-aware compression for federated data analysis.

Kamalika Chaudhuri, Chuan Guo, Mike Rabbat

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Bounding Training Data Reconstruction in Private (Deep) Learning.

Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten

Proceedings of the International Conference on Machine Learning, 2022

Thompson Sampling for Robust Transfer in Multi-Task Bandits.

Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri

Proceedings of the International Conference on Machine Learning, 2022

Privacy Implications of Shuffling.

Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha

Proceedings of the Tenth International Conference on Learning Representations, 2022

Forgeability and Membership Inference Attacks.

Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri

Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security, 2022

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

Jacob Imola, Takao Murakami, Kamalika Chaudhuri

Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022

Privacy Amplification via Shuffling for Linear Contextual Bandits.

Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

Privacy Amplification by Subsampling in Time Domain.

Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Sentence-level Privacy for Document Embeddings.

Casey Meehan, Khalil Mrini, Kamalika Chaudhuri

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning.

Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang

J. Mach. Learn. Res., 2021

A Shuffling Framework for Local Differential Privacy.

Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha

CoRR, 2021

Privacy Amplification Via Bernoulli Sampling.

Jacob Imola, Kamalika Chaudhuri

CoRR, 2021

Universal Approximation of Residual Flows in Maximum Mean Discrepancy.

Zhifeng Kong, Kamalika Chaudhuri

CoRR, 2021

Consistent Non-Parametric Methods for Adaptive Robustness.

Robi Bhattacharjee, Kamalika Chaudhuri

CoRR, 2021

Locally Differentially Private Analysis of Graph Statistics.

Jacob Imola, Takao Murakami, Kamalika Chaudhuri

Proceedings of the 30th USENIX Security Symposium, 2021

Behavior of k-NN as an Instance-Based Explanation Method.

Chhavi Yadav, Kamalika Chaudhuri

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

Understanding Instance-based Interpretability of Variational Auto-Encoders.

Zhifeng Kong, Kamalika Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Consistent Non-Parametric Methods for Maximizing Robustness.

Robi Bhattacharjee, Kamalika Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Connecting Interpretability and Robustness in Decision Trees through Separation.

Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri

Proceedings of the 38th International Conference on Machine Learning, 2021

Sample Complexity of Robust Linear Classification on Separated Data.

Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri

Proceedings of the 38th International Conference on Machine Learning, 2021

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation.

Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Location Trace Privacy Under Conditional Priors.

Casey Meehan, Kamalika Chaudhuri

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Approximate Data Deletion from Machine Learning Models.

Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Successive Refinement of Privacy.

Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi

IEEE J. Sel. Areas Inf. Theory, 2020

Variational Bayes In Private Settings (VIPS).

Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling

J. Artif. Intell. Res., 2020

Sample Complexity of Adversarially Robust Linear Classification on Separated Data.

Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri

CoRR, 2020

Close Category Generalization.

Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri

CoRR, 2020

Trustworthy AI Inference Systems: An Industry Research View.

Rosario Cammarota, Matthias Schunter, Anand Rajan, Fabian Boemer, Ágnes Kiss, Amos Treiber, Christian Weinert, Thomas Schneider, Emmanuel Stapf, Ahmad-Reza Sadeghi, Daniel Demmler, Huili Chen, Siam Umar Hussain, M. Sadegh Riazi, Farinaz Koushanfar, Saransh Gupta, Tajana Simunic Rosing, Kamalika Chaudhuri, Hamid Nejatollahi, Nikil D. Dutt, Mohsen Imani, Kim Laine, Anuj Dubey, Aydin Aysu, Fateme Sadat Hosseini, Chengmo Yang, Eric Wallace, Pamela Norton

CoRR, 2020

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

Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta

CoRR, 2020

Adversarial Robustness Through Local Lipschitzness.

Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri

CoRR, 2020

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

Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Y. Zou

CoRR, 2020

Exploring Connections Between Active Learning and Model Extraction.

Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan

Proceedings of the 29th USENIX Security Symposium, 2020

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

Benjamin Cosman, Madeline Endres, Georgios Sakkas, Leon Medvinsky, Yao-Yuan Yang, Ranjit Jhala, Kamalika Chaudhuri, Westley Weimer

Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 2020

A Closer Look at Accuracy vs. Robustness.

Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Distributed Training in Heterogeneous Mobile Networks with Active Sampling.

Yunhui Guo, Xiaofan Yu, Kamalika Chaudhuri, Tajana Rosing

Proceedings of the 16th International Conference on Mobility, Sensing and Networking, 2020

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

James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

When are Non-Parametric Methods Robust?

Robi Bhattacharjee, Kamalika Chaudhuri

Proceedings of the 37th International Conference on Machine Learning, 2020

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

Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

A Three Sample Hypothesis Test for Evaluating Generative Models.

Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

The Expressive Power of a Class of Normalizing Flow Models.

Zhifeng Kong, Kamalika Chaudhuri

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

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

Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri

CoRR, 2019

An Investigation of Data Poisoning Defenses for Online Learning.

Yizhen Wang, Kamalika Chaudhuri

CoRR, 2019

The Label Complexity of Active Learning from Observational Data.

Songbai Yan, Kamalika Chaudhuri, Tara Javidi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Capacity Bounded Differential Privacy.

Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Profile-based Privacy for Locally Private Computations.

Joseph Geumlek, Kamalika Chaudhuri

Proceedings of the IEEE International Symposium on Information Theory, 2019

2018

Special Issue on ALT 2015: Guest Editors' Introduction.

Kamalika Chaudhuri, Claudio Gentile

Theor. Comput. Sci., 2018

Model Extraction and Active Learning.

Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan

CoRR, 2018

The Inductive Bias of Restricted f-GANs.

Shuang Liu, Kamalika Chaudhuri

CoRR, 2018

Differentially Private Continual Release of Graph Statistics.

Shuang Song, Susan Little, Sanjay Mehta, Staal Amund Vinterbo, Kamalika Chaudhuri

CoRR, 2018

Data Poisoning Attacks against Online Learning.

Yizhen Wang, Kamalika Chaudhuri

CoRR, 2018

Spectral Learning of Binomial HMMs for DNA Methylation Data.

Chicheng Zhang, Eran A. Mukamel, Kamalika Chaudhuri

CoRR, 2018

Active Learning with Logged Data.

Songbai Yan, Kamalika Chaudhuri, Tara Javidi

Proceedings of the 35th International Conference on Machine Learning, 2018

Analyzing the Robustness of Nearest Neighbors to Adversarial Examples.

Yizhen Wang, Somesh Jha, Kamalika Chaudhuri

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

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

Eric L. Seidel, Huma Sibghat, Kamalika Chaudhuri, Westley Weimer, Ranjit Jhala

Proc. ACM Program. Lang., 2017

Pufferfish Privacy Mechanisms for Correlated Data.

Shuang Song, Yizhen Wang, Kamalika Chaudhuri

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

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

Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

Approximation and Convergence Properties of Generative Adversarial Learning.

Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Renyi Differential Privacy Mechanisms for Posterior Sampling.

Joseph Geumlek, Shuang Song, Kamalika Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Active Heteroscedastic Regression.

Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan

Proceedings of the 34th International Conference on Machine Learning, 2017

Composition properties of inferential privacy for time-series data.

Shuang Song, Kamalika Chaudhuri

Proceedings of the 55th Annual Allerton Conference on Communication, 2017

2016

Convex Optimization For Non-Convex Problems via Column Generation.

Julian Yarkony, Kamalika Chaudhuri

CoRR, 2016

Privacy-preserving Analysis of Correlated Data.

Yizhen Wang, Shuang Song, Kamalika Chaudhuri

CoRR, 2016

Private Topic Modeling.

Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling

CoRR, 2016

Practical Privacy For Expectation Maximization.

Mijung Park, Jimmy Foulds, Kamalika Chaudhuri, Max Welling

CoRR, 2016

Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics.

Xi Wu, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton

CoRR, 2016

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

James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Active Learning from Imperfect Labelers.

Songbai Yan, Kamalika Chaudhuri, Tara Javidi

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

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

Chicheng Zhang, Kamalika Chaudhuri

Proceedings of the 29th Conference on Learning Theory, 2016

2015

Bayesian Active Learning With Non-Persistent Noise.

Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri

IEEE Trans. Inf. Theory, 2015

Spectral Learning of Large Structured HMMs for Comparative Epigenomics.

Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin C. Chen

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Active Learning from Weak and Strong Labelers.

Chicheng Zhang, Kamalika Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Convergence Rates of Active Learning for Maximum Likelihood Estimation.

Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons.

James Y. Zou, Kamalika Chaudhuri, Adam Tauman Kalai

Proceedings of the Third AAAI Conference on Human Computation and Crowdsourcing, 2015

Active learning from noisy and abstention feedback.

Songbai Yan, Kamalika Chaudhuri, Tara Javidi

Proceedings of the 53rd Annual Allerton Conference on Communication, 2015

Learning from Data with Heterogeneous Noise using SGD.

Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Consistent Procedures for Cluster Tree Estimation and Pruning.

Kamalika Chaudhuri, Sanjoy Dasgupta, Samory Kpotufe, Ulrike von Luxburg

IEEE Trans. Inf. Theory, 2014

Beyond Disagreement-Based Agnostic Active Learning.

Chicheng Zhang, Kamalika Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

The Large Margin Mechanism for Differentially Private Maximization.

Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Rates of Convergence for Nearest Neighbor Classification.

Kamalika Chaudhuri, Sanjoy Dasgupta

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

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

Anand D. Sarwate, Kamalika Chaudhuri

IEEE Signal Process. Mag., 2013

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

Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha

J. Mach. Learn. Res., 2013

A Stability-based Validation Procedure for Differentially Private Machine Learning.

Kamalika Chaudhuri, Staal Amund Vinterbo

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Stochastic gradient descent with differentially private updates.

Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate

Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013

Extrinsic Jensen-Shannon divergence and noisy Bayesian active learning.

Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri

Proceedings of the 51st Annual Allerton Conference on Communication, 2013

2012

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

Kamalika Chaudhuri, Fan Chung Graham, Alexander Tsiatas

Proceedings of the COLT 2012, 2012

iDASH: integrating data for analysis, anonymization, and sharing.

Lucila Ohno-Machado, Vineet Bafna, Aziz A. Boxwala, Brian E. Chapman, Wendy Webber Chapman, Kamalika Chaudhuri, Michele E. Day, Claudiu Farcas, Nathaniel D. Heintzman, Xiaoqian Jiang, Hyeoneui Kim, Jihoon Kim, Michael E. Matheny, Frederic S. Resnic, Staal Amund Vinterbo

J. Am. Medical Informatics Assoc., 2012

Near-Optimal Algorithms for Differentially-Private Principal Components

Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha

CoRR, 2012

Near-optimal Differentially Private Principal Components.

Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Convergence Rates for Differentially Private Statistical Estimation.

Kamalika Chaudhuri, Daniel J. Hsu

Proceedings of the 29th International Conference on Machine Learning, 2012

Noisy Bayesian active learning.

Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri

Proceedings of the 50th Annual Allerton Conference on Communication, 2012

2011

Differentially Private Empirical Risk Minimization.

Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate

J. Mach. Learn. Res., 2011

Sample Complexity Bounds for Differentially Private Learning.

Kamalika Chaudhuri, Daniel J. Hsu

Proceedings of the COLT 2011, 2011

Spectral Methods for Learning Multivariate Latent Tree Structure.

Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

An Online Learning-based Framework for Tracking.

Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu

Proceedings of the UAI 2010, 2010

Rates of convergence for the cluster tree.

Kamalika Chaudhuri, Sanjoy Dasgupta

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

A push-relabel approximation algorithm for approximating the minimum-degree MST problem and its generalization to matroids.

Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar

Theor. Comput. Sci., 2009

Learning Mixtures of Gaussians using the k-means Algorithm

Kamalika Chaudhuri, Sanjoy Dasgupta, Andrea Vattani

CoRR, 2009

Differentially Private Support Vector Machines

Anand D. Sarwate, Kamalika Chaudhuri, Claire Monteleoni

CoRR, 2009

Tracking using explanation-based modeling

Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu

CoRR, 2009

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

Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar

Algorithmica, 2009

A Parameter-free Hedging Algorithm.

Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Online Bipartite Perfect Matching With Augmentations.

Kamalika Chaudhuri, Constantinos Daskalakis, Robert D. Kleinberg, Henry Lin

Proceedings of the INFOCOM 2009. 28th IEEE International Conference on Computer Communications, 2009

Multi-view clustering via canonical correlation analysis.

Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

A Network Coloring Game.

Kamalika Chaudhuri, Fan Chung Graham, Mohammad Shoaib Jamall

Proceedings of the Internet and Network Economics, 4th International Workshop, 2008

Privacy-preserving logistic regression.

Kamalika Chaudhuri, Claire Monteleoni

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed Distributions.

Kamalika Chaudhuri, Satish Rao

Proceedings of the 21st Annual Conference on Learning Theory, 2008

Learning Mixtures of Product Distributions Using Correlations and Independence.

Kamalika Chaudhuri, Satish Rao

Proceedings of the 21st Annual Conference on Learning Theory, 2008

Finding Metric Structure in Information Theoretic Clustering.

Kamalika Chaudhuri, Andrew McGregor

Proceedings of the 21st Annual Conference on Learning Theory, 2008

2007

Server Allocation Algorithms for Tiered Systems.

Kamalika Chaudhuri, Anshul Kothari, Rudi Pendavingh, Ram Swaminathan, Robert Endre Tarjan, Yunhong Zhou

Algorithmica, 2007

A rigorous analysis of population stratification with limited data.

Kamalika Chaudhuri, Eran Halperin, Satish Rao, Shuheng Zhou

Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2007

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

Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, Kunal Talwar

Proceedings of the Twenty-Sixth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 2007

2006

On the tandem duplication-random loss model of genome rearrangement.

Kamalika Chaudhuri, Kevin C. Chen, Radu Mihaescu, Satish Rao

Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2006

A Push-Relabel Algorithm for Approximating Degree Bounded MSTs.

Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar

Proceedings of the Automata, Languages and Programming, 33rd International Colloquium, 2006

When Random Sampling Preserves Privacy.

Kamalika Chaudhuri, Nina Mishra

Proceedings of the Advances in Cryptology, 2006

2005

Value-maximizing deadline scheduling and its application to animation rendering.

Eric Anderson, Dirk Beyer, Kamalika Chaudhuri, Terence Kelly, Norman Salazar, Cipriano A. Santos, Ram Swaminathan, Robert Endre Tarjan, Janet L. Wiener, Yunhong Zhou

Proceedings of the SPAA 2005: Proceedings of the 17th Annual ACM Symposium on Parallelism in Algorithms and Architectures, 2005

Deadline scheduling for animation rendering.

Eric Anderson, Dirk Beyer, Kamalika Chaudhuri, Terence Kelly, Norman Salazar, Cipriano A. Santos, Ram Swaminathan, Robert Endre Tarjan, Janet L. Wiener, Yunhong Zhou

Proceedings of the International Conference on Measurements and Modeling of Computer Systems, 2005

2004

Selfish caching in distributed systems: a game-theoretic analysis.

Byung-Gon Chun, Kamalika Chaudhuri, Hoeteck Wee, Marco Barreno, Christos H. Papadimitriou, John Kubiatowicz

Proceedings of the Twenty-Third Annual ACM Symposium on Principles of Distributed Computing, 2004

2003

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

Siddhartha Saha, Kamalika Chaudhuri, Dheeraj Sanghi, Pravin Bhagwat

Proceedings of the 2003 IEEE Wireless Communications and Networking, 2003

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

Siddhartha Saha, Kamalika Chaudhuri, Dheeraj Sanghi

Proceedings of the Information Networking, 2003

Paths, Trees, and Minimum Latency Tours.

Kamalika Chaudhuri, Brighten Godfrey, Satish Rao, Kunal Talwar

Proceedings of the 44th Symposium on Foundations of Computer Science (FOCS 2003), 2003