Chaudhuri, Kamalika

90 publications

NeurIPS 2025 AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions Polina Kirichenko, Mark Ibrahim, Kamalika Chaudhuri, Samuel Bell

NeurIPS 2025 AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents Arman Zharmagambetov, Chuan Guo, Ivan Evtimov, Maya Pavlova, Ruslan Salakhutdinov, Kamalika Chaudhuri

ICML 2025 Auditing $f$-Differential Privacy in One Run Saeed Mahloujifar, Luca Melis, Kamalika Chaudhuri

NeurIPS 2025 Can We Infer Confidential Properties of Training Data from LLMs? Pengrun Huang, Chhavi Yadav, Kamalika Chaudhuri, Ruihan Wu

NeurIPS 2025 Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness Rongzhe Wei, Peizhi Niu, Hans Hao-Hsun Hsu, Ruihan Wu, Haoteng Yin, Mohsen Ghassemi, Yifan Li, Vamsi K. Potluru, Eli Chien, Kamalika Chaudhuri, Olgica Milenkovic, Pan Li

ICML 2025 ExpProof : Operationalizing Explanations for Confidential Models with ZKPs Chhavi Yadav, Evan Laufer, Dan Boneh, Kamalika Chaudhuri

ICLRW 2025 ExpProof : Operationalizing Explanations for Confidential Models with ZKPs Chhavi Yadav, Evan Laufer, Dan Boneh, Kamalika Chaudhuri

NeurIPS 2025 Rethinking the Role of Verbatim Memorization in LLM Privacy Tom Sander, Bargav Jayaraman, Mark Ibrahim, Kamalika Chaudhuri, Chuan Guo

NeurIPS 2025 WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks Ivan Evtimov, Arman Zharmagambetov, Aaron Grattafiori, Chuan Guo, Kamalika Chaudhuri

ICML 2024 Differentially Private Representation Learning via Image Captioning Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo

NeurIPS 2024 Distribution Learning with Valid Outputs Beyond the Worst-Case Nick Rittler, Kamalika Chaudhuri

NeurIPS 2024 Déjà Vu Memorization in Vision–Language Models Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri

ICLR 2024 Effective Pruning of Web-Scale Datasets Based on Complexity of Concept Clusters Amro Kamal Mohamed Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos

ICML 2024 FairProof : Confidential and Certifiable Fairness for Neural Networks Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri

ICLRW 2024 FairProof : Confidential and Certifiable Fairness for Neural Networks Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri

NeurIPSW 2024 FairProof : Confidential and Certifiable Fairness for Neural Networks Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri

TMLR 2024 Guarantees of Confidentiality via Hammersley-Chapman-Robbins Bounds Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert

NeurIPSW 2024 Influence-Based Attributions Can Be Manipulated Chhavi Yadav, Ruihan Wu, Kamalika Chaudhuri

NeurIPS 2024 Measuring Dejavu Memorization Efficiently Narine Kokhlikyan, Bargav Jayaraman, Florian Bordes, Chuan Guo, Kamalika Chaudhuri

NeurIPS 2024 On Differentially Private U Statistics Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar

ICML 2024 ViP: A Differentially Private Foundation Model for Computer Vision Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo

TMLR 2024 XAudit : A Learning-Theoretic Look at Auditing with Explanations Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri

ICML 2023 A Two-Stage Active Learning Algorithm for K-Nearest Neighbors Nicholas Rittler, Kamalika Chaudhuri

NeurIPS 2023 Agnostic Multi-Group Active Learning Nicholas Rittler, Kamalika Chaudhuri

ICML 2023 Data-Copying in Generative Models: A Formal Framework Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri

NeurIPS 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

ICMLW 2023 Machine Learning with Feature Differential Privacy Saeed Mahloujifar, Chuan Guo, G. Edward Suh, Kamalika Chaudhuri

ICML 2023 Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael Rabbat

TMLR 2023 Probing Predictions on OOD Images via Nearest Categories Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri

ALT 2023 Robust Empirical Risk Minimization with Tolerance Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri

ICML 2023 Why Does Throwing Away Data Improve Worst-Group Error? Kamalika Chaudhuri, Kartik Ahuja, Martin Arjovsky, David Lopez-Paz

AISTATS 2022 Privacy Amplification by Subsampling in Time Domain Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri

ICML 2022 Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens Maaten

NeurIPSW 2022 Data Redaction from Pre-Trained GANs Zhifeng Kong, Kamalika Chaudhuri

ALT 2022 Privacy Amplification via Shuffling for Linear Contextual Bandits Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta

ICLR 2022 Privacy Implications of Shuffling Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha

UAI 2022 Privacy-Aware Compression for Federated Data Analysis Kamalika Chaudhuri, Chuan Guo, Mike Rabbat

NeurIPSW 2022 The Interpolated MVU Mechanism for Communication-Efficient Private Federated Learning Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael Rabbat

ICML 2022 Thompson Sampling for Robust Transfer in Multi-Task Bandits Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri

ICMLW 2022 Understanding Rare Spurious Correlations in Neural Networks Yao-Yuan Yang, Chi-Ning Chou, Kamalika Chaudhuri

AISTATS 2021 Approximate Data Deletion from Machine Learning Models Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou

AISTATS 2021 Location Trace Privacy Under Conditional Priors Casey Meehan, Kamalika Chaudhuri

AISTATS 2021 Multitask Bandit Learning Through Heterogeneous Feedback Aggregation Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel Riek, Kamalika Chaudhuri

AISTATS 2021 Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang

ICML 2021 Connecting Interpretability and Robustness in Decision Trees Through Separation Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri

NeurIPS 2021 Consistent Non-Parametric Methods for Maximizing Robustness Robi Bhattacharjee, Kamalika Chaudhuri

JMLR 2021 Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang

ICML 2021 Sample Complexity of Robust Linear Classification on Separated Data Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri

NeurIPS 2021 Understanding Instance-Based Interpretability of Variational Auto-Encoders Zhifeng Kong, Kamalika Chaudhuri

ICMLW 2021 Universal Approximation of Residual Flows in Maximum Mean Discrepancy Zhifeng Kong, Kamalika Chaudhuri

NeurIPS 2020 A Closer Look at Accuracy vs. Robustness Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri

AISTATS 2020 A Three Sample Hypothesis Test for Evaluating Generative Models Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta

AISTATS 2020 Robustness for Non-Parametric Classification: A Generic Attack and Defense Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri

AISTATS 2020 The Expressive Power of a Class of Normalizing Flow Models Zhifeng Kong, Kamalika Chaudhuri

JAIR 2020 Variational Bayes in Private Settings (VIPS) Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling

IJCAI 2020 Variational Bayes in Private Settings (VIPS) (Extended Abstract) James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling

ICML 2020 When Are Non-Parametric Methods Robust? Robi Bhattacharjee, Kamalika Chaudhuri

NeurIPS 2019 Capacity Bounded Differential Privacy Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala

NeurIPS 2019 The Label Complexity of Active Learning from Observational Data Songbai Yan, Kamalika Chaudhuri, Tara Javidi

ICML 2018 Active Learning with Logged Data Songbai Yan, Kamalika Chaudhuri, Tara Javidi

ICML 2018 Analyzing the Robustness of Nearest Neighbors to Adversarial Examples Yizhen Wang, Somesh Jha, Kamalika Chaudhuri

ICML 2017 Active Heteroscedastic Regression Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan

NeurIPS 2017 Approximation and Convergence Properties of Generative Adversarial Learning Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri

NeurIPS 2017 Renyi Differential Privacy Mechanisms for Posterior Sampling Joseph Geumlek, Shuang Song, Kamalika Chaudhuri

NeurIPS 2016 Active Learning from Imperfect Labelers Songbai Yan, Kamalika Chaudhuri, Tara Javidi

UAI 2016 On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri

COLT 2016 The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions Chicheng Zhang, Kamalika Chaudhuri

NeurIPS 2015 Active Learning from Weak and Strong Labelers Chicheng Zhang, Kamalika Chaudhuri

NeurIPS 2015 Convergence Rates of Active Learning for Maximum Likelihood Estimation Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi

AISTATS 2015 Learning from Data with Heterogeneous Noise Using SGD Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate

NeurIPS 2015 Spectral Learning of Large Structured HMMs for Comparative Epigenomics Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin Chen

NeurIPS 2014 Beyond Disagreement-Based Agnostic Active Learning Chicheng Zhang, Kamalika Chaudhuri

NeurIPS 2014 Rates of Convergence for Nearest Neighbor Classification Kamalika Chaudhuri, Sanjoy Dasgupta

NeurIPS 2014 The Large Margin Mechanism for Differentially Private Maximization Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song

JMLR 2013 A Near-Optimal Algorithm for Differentially-Private Principal Components Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha

NeurIPS 2013 A Stability-Based Validation Procedure for Differentially Private Machine Learning Kamalika Chaudhuri, Staal A Vinterbo

ICML 2012 Convergence Rates for Differentially Private Statistical Estimation Kamalika Chaudhuri, Daniel J. Hsu

NeurIPS 2012 Near-Optimal Differentially Private Principal Components Kamalika Chaudhuri, Anand Sarwate, Kaushik Sinha

COLT 2012 Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model Kamalika Chaudhuri, Fan Chung, Alexander Tsiatas

JMLR 2011 Differentially Private Empirical Risk Minimization Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate

COLT 2011 Sample Complexity Bounds for Differentially Private Learning Kamalika Chaudhuri, Daniel Hsu

NeurIPS 2011 Spectral Methods for Learning Multivariate Latent Tree Structure Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang

UAI 2010 An Online Learning-Based Framework for Tracking Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu

NeurIPS 2010 Rates of Convergence for the Cluster Tree Kamalika Chaudhuri, Sanjoy Dasgupta

NeurIPS 2009 A Parameter-Free Hedging Algorithm Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu

ICML 2009 Multi-View Clustering via Canonical Correlation Analysis Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan

COLT 2008 Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed Distributions Kamalika Chaudhuri, Satish Rao

COLT 2008 Finding Metric Structure in Information Theoretic Clustering Kamalika Chaudhuri, Andrew McGregor

COLT 2008 Learning Mixtures of Product Distributions Using Correlations and Independence Kamalika Chaudhuri, Satish Rao

NeurIPS 2008 Privacy-Preserving Logistic Regression Kamalika Chaudhuri, Claire Monteleoni