Paths, Trees, and Minimum Latency Tours. - Kamalika Chaudhuri; Brighten Godfrey; Satish Rao; Kunal Talwar (FOCS, 2003)

An Extension of Scalable Global IP Anycasting for Load Balancing in the Internet. - Siddhartha Saha; Kamalika Chaudhuri; Dheeraj Sanghi (ICOIN, 2003)

Location determination of a mobile device using IEEE 802.11b access point signals. - Siddhartha Saha; Kamalika Chaudhuri; Dheeraj Sanghi; Pravin Bhagwat (WCNC, 2003)

Selfish caching in distributed systems: a game-theoretic analysis. - Byung-Gon Chun; Kamalika Chaudhuri; Hoeteck Wee; Marco Barreno; Christos H. Papadimitriou; John Kubiatowicz (PODC, 2004)

Deadline scheduling for animation rendering. - Eric Anderson 003; Dirk Beyer 002; Kamalika Chaudhuri; Terence Kelly; Norman Salazar; Cipriano A. Santos; Ram Swaminathan; Robert Endre Tarjan; Janet L. Wiener; Yunhong Zhou (SIGMETRICS, 2005)

Value-maximizing deadline scheduling and its application to animation rendering. - Eric Anderson 003; Dirk Beyer 002; Kamalika Chaudhuri; Terence Kelly; Norman Salazar; Cipriano A. Santos; Ram Swaminathan; Robert Endre Tarjan; Janet L. Wiener; Yunhong Zhou (SPAA, 2005)

When Random Sampling Preserves Privacy. - Kamalika Chaudhuri; Nina Mishra (CRYPTO, 2006)

A Push-Relabel Algorithm for Approximating Degree Bounded MSTs. - Kamalika Chaudhuri; Satish Rao; Samantha J. Riesenfeld; Kunal Talwar (ICALP (1), 2006)

On the tandem duplication-random loss model of genome rearrangement. - Kamalika Chaudhuri; Kevin C. Chen; Radu Mihaescu; Satish Rao (SODA, 2006)

Privacy, accuracy, and consistency too: a holistic solution to contingency table release. - Boaz Barak; Kamalika Chaudhuri; Cynthia Dwork; Satyen Kale; Frank McSherry; Kunal Talwar (PODS, 2007)

A rigorous analysis of population stratification with limited data. - Kamalika Chaudhuri; Eran Halperin; Satish Rao; Shuheng Zhou 002 (SODA, 2007)

Server Allocation Algorithms for Tiered Systems. - Kamalika Chaudhuri; Anshul Kothari; Rudi Pendavingh; Ram Swaminathan; Robert Endre Tarjan; Yunhong Zhou (Algorithmica, 2007)

Finding Metric Structure in Information Theoretic Clustering. - Kamalika Chaudhuri; Andrew McGregor 001 (COLT, 2008)

Learning Mixtures of Product Distributions Using Correlations and Independence. - Kamalika Chaudhuri; Satish Rao (COLT, 2008)

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

Privacy-preserving logistic regression. - Kamalika Chaudhuri; Claire Monteleoni (NIPS, 2008)

A Network Coloring Game. - Kamalika Chaudhuri; Fan Chung Graham; Mohammad Shoaib Jamall (WINE, 2008)

Multi-view clustering via canonical correlation analysis. - Kamalika Chaudhuri; Sham M. Kakade; Karen Livescu; Karthik Sridharan (ICML, 2009)

Online Bipartite Perfect Matching With Augmentations. - Kamalika Chaudhuri; Constantinos Daskalakis; Robert D. Kleinberg; Henry Lin (INFOCOM, 2009)

A Parameter-free Hedging Algorithm. - Kamalika Chaudhuri; Yoav Freund; Daniel J. Hsu (NIPS, 2009)

What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTs. - Kamalika Chaudhuri; Satish Rao; Samantha J. Riesenfeld; Kunal Talwar (Algorithmica, 2009)

Tracking using explanation-based modeling - Kamalika Chaudhuri; Yoav Freund; Daniel J. Hsu (CoRR, 2009)

Differentially Private Support Vector Machines - Anand D. Sarwate; Kamalika Chaudhuri; Claire Monteleoni (CoRR, 2009)

Learning Mixtures of Gaussians using the k-means Algorithm - Kamalika Chaudhuri; Sanjoy Dasgupta; Andrea Vattani (CoRR, 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)

Rates of convergence for the cluster tree. - Kamalika Chaudhuri; Sanjoy Dasgupta (NIPS, 2010)

An Online Learning-based Framework for Tracking. - Kamalika Chaudhuri; Yoav Freund; Daniel J. Hsu (UAI, 2010)

Spectral Methods for Learning Multivariate Latent Tree Structure. - Animashree Anandkumar; Kamalika Chaudhuri; Daniel J. Hsu; Sham M. Kakade; Le Song; Tong Zhang 001 (NIPS, 2011)

Sample Complexity Bounds for Differentially Private Learning. - Kamalika Chaudhuri; Daniel J. Hsu (COLT, 2011)

Differentially Private Empirical Risk Minimization. - Kamalika Chaudhuri; Claire Monteleoni; Anand D. Sarwate (J. Mach. Learn. Res., 2011)

Noisy Bayesian active learning. - Mohammad Naghshvar; Tara Javidi; Kamalika Chaudhuri (Allerton Conference, 2012)

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

Near-optimal Differentially Private Principal Components. - Kamalika Chaudhuri; Anand D. Sarwate; Kaushik Sinha (NIPS, 2012)

Near-Optimal Algorithms for Differentially-Private Principal Components - Kamalika Chaudhuri; Anand D. Sarwate; Kaushik Sinha (CoRR, 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 001; et al. (J. Am. Medical Informatics Assoc., 2012)

Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model. - Kamalika Chaudhuri; Fan Chung Graham; Alexander Tsiatas (COLT, 2012)

Extrinsic Jensen-Shannon divergence and noisy Bayesian active learning. - Mohammad Naghshvar; Tara Javidi; Kamalika Chaudhuri (Allerton, 2013)

Stochastic gradient descent with differentially private updates. - Shuang Song 001; Kamalika Chaudhuri; Anand D. Sarwate (GlobalSIP, 2013)

A Stability-based Validation Procedure for Differentially Private Machine Learning. - Kamalika Chaudhuri; Staal Amund Vinterbo (NIPS, 2013)

A near-optimal algorithm for differentially-private principal components. - Kamalika Chaudhuri; Anand D. Sarwate; Kaushik Sinha (J. Mach. Learn. Res., 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)

Rates of Convergence for Nearest Neighbor Classification. - Kamalika Chaudhuri; Sanjoy Dasgupta (NIPS, 2014)

The Large Margin Mechanism for Differentially Private Maximization. - Kamalika Chaudhuri; Daniel J. Hsu; Shuang Song 001 (NIPS, 2014)

Beyond Disagreement-Based Agnostic Active Learning. - Chicheng Zhang; Kamalika Chaudhuri (NIPS, 2014)

Consistent Procedures for Cluster Tree Estimation and Pruning. - Kamalika Chaudhuri; Sanjoy Dasgupta; Samory Kpotufe; Ulrike von Luxburg (IEEE Trans. Inf. Theory, 2014)

Learning from Data with Heterogeneous Noise using SGD. - Shuang Song 001; Kamalika Chaudhuri; Anand D. Sarwate (AISTATS, 2015)

Active learning from noisy and abstention feedback. - Songbai Yan; Kamalika Chaudhuri; Tara Javidi (Allerton, 2015)

Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons. - James Y. Zou; Kamalika Chaudhuri; Adam Tauman Kalai (HCOMP, 2015)

Convergence Rates of Active Learning for Maximum Likelihood Estimation. - Kamalika Chaudhuri; Sham M. Kakade; Praneeth Netrapalli; Sujay Sanghavi (NIPS, 2015)

Active Learning from Weak and Strong Labelers. - Chicheng Zhang; Kamalika Chaudhuri (NIPS, 2015)

Spectral Learning of Large Structured HMMs for Comparative Epigenomics. - Chicheng Zhang; Jimin Song; Kamalika Chaudhuri; Kevin C. Chen (NIPS, 2015)

Bayesian Active Learning With Non-Persistent Noise. - Mohammad Naghshvar; Tara Javidi; Kamalika Chaudhuri (IEEE Trans. Inf. Theory, 2015)

The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions. - Chicheng Zhang; Kamalika Chaudhuri (COLT, 2016)

Active Learning from Imperfect Labelers. - Songbai Yan; Kamalika Chaudhuri; Tara Javidi (NIPS, 2016)

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

Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics. - Xi Wu 001; Arun Kumar 001; Kamalika Chaudhuri; Somesh Jha; Jeffrey F. Naughton (CoRR, 2016)

Practical Privacy For Expectation Maximization. - Mijung Park; Jimmy Foulds; Kamalika Chaudhuri; Max Welling (CoRR, 2016)

Private Topic Modeling. - Mijung Park; James R. Foulds; Kamalika Chaudhuri; Max Welling (CoRR, 2016)

Privacy-preserving Analysis of Correlated Data. - Yizhen Wang; Shuang Song 001; Kamalika Chaudhuri (CoRR, 2016)

Convex Optimization For Non-Convex Problems via Column Generation. - Julian Yarkony; Kamalika Chaudhuri (CoRR, 2016)

Composition properties of inferential privacy for time-series data. - Shuang Song 001; Kamalika Chaudhuri (Allerton, 2017)

Active Heteroscedastic Regression. - Kamalika Chaudhuri; Prateek Jain 002; Nagarajan Natarajan (ICML, 2017)

Renyi Differential Privacy Mechanisms for Posterior Sampling. - Joseph Geumlek; Shuang Song 001; Kamalika Chaudhuri (NIPS, 2017)

Approximation and Convergence Properties of Generative Adversarial Learning. - Shuang Liu; Olivier Bousquet; Kamalika Chaudhuri (NIPS, 2017)

Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics. - Xi Wu 001; Fengan Li; Arun Kumar 001; Kamalika Chaudhuri; Somesh Jha; Jeffrey F. Naughton (SIGMOD Conference, 2017)

Pufferfish Privacy Mechanisms for Correlated Data. - Shuang Song 001; Yizhen Wang; Kamalika Chaudhuri (SIGMOD Conference, 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)

Analyzing the Robustness of Nearest Neighbors to Adversarial Examples. - Yizhen Wang; Somesh Jha; Kamalika Chaudhuri (ICML, 2018)

Active Learning with Logged Data. - Songbai Yan; Kamalika Chaudhuri; Tara Javidi (ICML, 2018)

Spectral Learning of Binomial HMMs for DNA Methylation Data. - Chicheng Zhang; Eran A. Mukamel; Kamalika Chaudhuri (CoRR, 2018)

Data Poisoning Attacks against Online Learning. - Yizhen Wang; Kamalika Chaudhuri (CoRR, 2018)

Differentially Private Continual Release of Graph Statistics. - Shuang Song 001; Susan Little; Sanjay Mehta; Staal Amund Vinterbo; Kamalika Chaudhuri (CoRR, 2018)

The Inductive Bias of Restricted f-GANs. - Shuang Liu; Kamalika Chaudhuri (CoRR, 2018)

Model Extraction and Active Learning. - Varun Chandrasekaran; Kamalika Chaudhuri; Irene Giacomelli; Somesh Jha; Songbai Yan (CoRR, 2018)

Special Issue on ALT 2015: Guest Editors' Introduction. - Kamalika Chaudhuri; Claudio Gentile (Theor. Comput. Sci., 2018)

Profile-based Privacy for Locally Private Computations. - Joseph Geumlek; Kamalika Chaudhuri (ISIT, 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 (NeurIPS, 2019)

An Investigation of Data Poisoning Defenses for Online Learning. - Yizhen Wang; Kamalika Chaudhuri (CoRR, 2019)

Adversarial Examples for Non-Parametric Methods: Attacks, Defenses and Large Sample Limits. - Yao-Yuan Yang; Cyrus Rashtchian; Yizhen Wang; Kamalika Chaudhuri (CoRR, 2019)

The Expressive Power of a Class of Normalizing Flow Models. - Zhifeng Kong; 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)

When are Non-Parametric Methods Robust? - Robi Bhattacharjee; Kamalika Chaudhuri (ICML, 2020)

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

Efficient Distributed Training in Heterogeneous Mobile Networks with Active Sampling. - Yunhui Guo; Xiaofan Yu 001; Kamalika Chaudhuri; Tajana Rosing (MSN, 2020)

A Closer Look at Accuracy vs. Robustness. - Yao-Yuan Yang; Cyrus Rashtchian; Hongyang Zhang 001; Ruslan Salakhutdinov; Kamalika Chaudhuri (NeurIPS, 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 (SIGCSE, 2020)

Exploring Connections Between Active Learning and Model Extraction. - Varun Chandrasekaran; Kamalika Chaudhuri; Irene Giacomelli; Somesh Jha; Songbai Yan (USENIX Security Symposium, 2020)

Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations. - Zachary Izzo; Mary Anne Smart; Kamalika Chaudhuri; James Y. Zou (CoRR, 2020)

Adversarial Robustness Through Local Lipschitzness. - Yao-Yuan Yang; Cyrus Rashtchian; Hongyang Zhang 001; Ruslan Salakhutdinov; Kamalika Chaudhuri (CoRR, 2020)

A Non-Parametric Test to Detect Data-Copying in Generative Models. - Casey Meehan; Kamalika Chaudhuri; Sanjoy Dasgupta (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 003; Emmanuel Stapf; Ahmad-Reza Sadeghi; Daniel Demmler; Huili Chen; et al. (CoRR, 2020)

Close Category Generalization. - Yao-Yuan Yang; Cyrus Rashtchian; Ruslan Salakhutdinov; Kamalika Chaudhuri (CoRR, 2020)

Sample Complexity of Adversarially Robust Linear Classification on Separated Data. - Robi Bhattacharjee; Somesh Jha; Kamalika Chaudhuri (CoRR, 2020)

Variational Bayes In Private Settings (VIPS). - Mijung Park; James R. Foulds; Kamalika Chaudhuri; Max Welling (J. Artif. Intell. Res., 2020)

Successive Refinement of Privacy. - Antonious M. Girgis; Deepesh Data; Kamalika Chaudhuri; Christina Fragouli; Suhas N. Diggavi (IEEE J. Sel. Areas Inf. Theory, 2020)

Approximate Data Deletion from Machine Learning Models. - Zachary Izzo; Mary Anne Smart; Kamalika Chaudhuri; James Zou 001 (AISTATS, 2021)

Location Trace Privacy Under Conditional Priors. - Casey Meehan; Kamalika Chaudhuri (AISTATS, 2021)

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation. - Zhi Wang 013; Chicheng Zhang; Manish Kumar Singh 002; Laurel D. Riek; Kamalika Chaudhuri (AISTATS, 2021)

Sample Complexity of Robust Linear Classification on Separated Data. - Robi Bhattacharjee; Somesh Jha; Kamalika Chaudhuri (ICML, 2021)

Connecting Interpretability and Robustness in Decision Trees through Separation. - Michal Moshkovitz; Yao-Yuan Yang; Kamalika Chaudhuri (ICML, 2021)

Consistent Non-Parametric Methods for Maximizing Robustness. - Robi Bhattacharjee; Kamalika Chaudhuri (NeurIPS, 2021)

Understanding Instance-based Interpretability of Variational Auto-Encoders. - Zhifeng Kong; Kamalika Chaudhuri (NeurIPS, 2021)

Behavior of k-NN as an Instance-Based Explanation Method. - Chhavi Yadav; Kamalika Chaudhuri (PKDD/ECML Workshops (1), 2021)

Locally Differentially Private Analysis of Graph Statistics. - Jacob Imola; Takao Murakami; Kamalika Chaudhuri (USENIX Security Symposium, 2021)

Consistent Non-Parametric Methods for Adaptive Robustness. - Robi Bhattacharjee; Kamalika Chaudhuri (CoRR, 2021)

Universal Approximation of Residual Flows in Maximum Mean Discrepancy. - Zhifeng Kong; Kamalika Chaudhuri (CoRR, 2021)

Privacy Amplification Via Bernoulli Sampling. - Jacob Imola; Kamalika Chaudhuri (CoRR, 2021)

A Shuffling Framework for Local Differential Privacy. - Casey Meehan; Amrita Roy Chowdhury 001; Kamalika Chaudhuri; Somesh Jha (CoRR, 2021)

Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. - Chong Liu 007; Yuqing Zhu 005; Kamalika Chaudhuri; Yu-Xiang Wang 003 (J. Mach. Learn. Res., 2021)

Sentence-level Privacy for Document Embeddings. - Casey Meehan; Khalil Mrini; Kamalika Chaudhuri (ACL (1), 2022)

Privacy Amplification by Subsampling in Time Domain. - Tatsuki Koga; Casey Meehan; Kamalika Chaudhuri (AISTATS, 2022)

Privacy Amplification via Shuffling for Linear Contextual Bandits. - Evrard Garcelon; Kamalika Chaudhuri; Vianney Perchet; Matteo Pirotta (ALT, 2022)

Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model. - Jacob Imola; Takao Murakami; Kamalika Chaudhuri (CCS, 2022)

Forgeability and Membership Inference Attacks. - Zhifeng Kong; Amrita Roy Chowdhury 001; Kamalika Chaudhuri (AISec@CCS, 2022)

Privacy Implications of Shuffling. - Casey Meehan; Amrita Roy Chowdhury 001; Kamalika Chaudhuri; Somesh Jha (ICLR, 2022)

Thompson Sampling for Robust Transfer in Multi-Task Bandits. - Zhi Wang 013; Chicheng Zhang; Kamalika Chaudhuri (ICML, 2022)

Bounding Training Data Reconstruction in Private (Deep) Learning. - Chuan Guo 001; Brian Karrer; Kamalika Chaudhuri; Laurens van der Maaten (ICML, 2022)

Privacy-aware compression for federated data analysis. - Kamalika Chaudhuri; Chuan Guo 001; Mike Rabbat (UAI, 2022)

Communication-Efficient Triangle Counting under Local Differential Privacy. - Jacob Imola; Takao Murakami; Kamalika Chaudhuri (USENIX Security Symposium, 2022)

Understanding Rare Spurious Correlations in Neural Networks. - Yao-Yuan Yang; Kamalika Chaudhuri (CoRR, 2022)

Differentially Private Subgraph Counting in the Shuffle Model. - Jacob Imola; Takao Murakami; Kamalika Chaudhuri (CoRR, 2022)

Throwing Away Data Improves Worst-Class Error in Imbalanced Classification. - Martín Arjovsky; Kamalika Chaudhuri; David Lopez-Paz (CoRR, 2022)

A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models. - Chhavi Yadav; Michal Moshkovitz; Kamalika Chaudhuri (CoRR, 2022)

Forgetting Data from Pre-trained GANs. - Zhifeng Kong; Kamalika Chaudhuri (CoRR, 2022)

Robustness of Locally Differentially Private Graph Analysis Against Poisoning. - Jacob Imola; Amrita Roy Chowdhury 001; Kamalika Chaudhuri (CoRR, 2022)

The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning. - Chuan Guo 001; Kamalika Chaudhuri; Pierre Stock; Mike Rabbat (CoRR, 2022)

Robust Empirical Risk Minimization with Tolerance. - Robi Bhattacharjee; Max Hopkins; Akash Kumar 010; Hantao Yu; Kamalika Chaudhuri (ALT, 2023)

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

Why does Throwing Away Data Improve Worst-Group Error? - Kamalika Chaudhuri; Kartik Ahuja; Martín Arjovsky; David Lopez-Paz (ICML, 2023)

Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. - Chuan Guo 001; Kamalika Chaudhuri; Pierre Stock; Michael G. Rabbat (ICML, 2023)

A Two-Stage Active Learning Algorithm for k-Nearest Neighbors. - Nicholas Rittler; Kamalika Chaudhuri (ICML, 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 001 (NeurIPS, 2023)

Agnostic Multi-Group Active Learning. - Nicholas Rittler; Kamalika Chaudhuri (NeurIPS, 2023)

Data Redaction from Pre-trained GANs. - Zhifeng Kong; Kamalika Chaudhuri (SaTML, 2023)

Can Membership Inferencing be Refuted? - Zhifeng Kong; Amrita Roy Chowdhury 001; Kamalika Chaudhuri (CoRR, 2023)

Large-Scale Public Data Improves Differentially Private Image Generation Quality. - Ruihan Wu; Chuan Guo 001; Kamalika Chaudhuri (CoRR, 2023)

Unified Uncertainty Calibration. - Kamalika Chaudhuri; David Lopez-Paz (CoRR, 2023)

Probing Predictions on OOD Images via Nearest Categories. - Yao-Yuan Yang; Cyrus Rashtchian; Ruslan Salakhutdinov; Kamalika Chaudhuri (Trans. Mach. Learn. Res., 2023)

Metric Differential Privacy at the User-Level via the Earth-Mover's Distance. - Jacob Imola; Amrita Roy Chowdhury 001; Kamalika Chaudhuri (CCS, 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 (ICLR, 2024)

Differentially Private Representation Learning via Image Captioning. - Tom Sander; Yaodong Yu; Maziar Sanjabi; Alain Oliviero Durmus; Yi Ma 001; Kamalika Chaudhuri; Chuan Guo 001 (ICML, 2024)

FairProof : Confidential and Certifiable Fairness for Neural Networks. - Chhavi Yadav; Amrita Roy Chowdhury 001; Dan Boneh; Kamalika Chaudhuri (ICML, 2024)

ViP: A Differentially Private Foundation Model for Computer Vision. - Yaodong Yu; Maziar Sanjabi; Yi Ma 001; Kamalika Chaudhuri; Chuan Guo 001 (ICML, 2024)

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

Déjà Vu Memorization in Vision-Language Models. - Bargav Jayaraman; Chuan Guo 001; Kamalika Chaudhuri (NeurIPS, 2024)

Measuring Dejavu Memorization Efficiently. - Narine Kokhlikyan; Bargav Jayaraman; Florian Bordes; Chuan Guo 001; Kamalika Chaudhuri (NeurIPS, 2024)

Distribution Learning with Valid Outputs Beyond the Worst-Case. - Nicholas Rittler; Kamalika Chaudhuri (NeurIPS, 2024)

Differentially Private Multi-Site Treatment Effect Estimation. - Tatsuki Koga; Kamalika Chaudhuri; David Page (SaTML, 2024)

Data Redaction from Conditional Generative Models. - Zhifeng Kong; Kamalika Chaudhuri (SaTML, 2024)

Measuring Privacy Loss in Distributed Spatio-Temporal Data. - Tatsuki Koga; Casey Meehan; Kamalika Chaudhuri (CoRR, 2024)

Privacy Amplification for the Gaussian Mechanism via Bounded Support. - Shengyuan Hu 001; Saeed Mahloujifar; Virginia Smith; Kamalika Chaudhuri; Chuan Guo 001 (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 001 (CoRR, 2024)

Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations. - Christian Tomani; Kamalika Chaudhuri; Ivan Evtimov; Daniel Cremers; Mark Ibrahim (CoRR, 2024)

Metric Differential Privacy at the User-Level. - Jacob Imola; Amrita Roy Chowdhury 001; Kamalika Chaudhuri (CoRR, 2024)

Better Membership Inference Privacy Measurement through Discrepancy. - Ruihan Wu; Pengrun Huang; 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 002; Bargav Jayaraman; Mark Ibrahim; Melissa Hall; et al. (CoRR, 2024)

Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift. - Robi Bhattacharjee; Nick Rittler; Kamalika Chaudhuri (CoRR, 2024)

Influence-based Attributions can be Manipulated. - Chhavi Yadav; Ruihan Wu; Kamalika Chaudhuri (CoRR, 2024)

Aligning LLMs to Be Robust Against Prompt Injection. - Sizhe Chen; Arman Zharmagambetov; Saeed Mahloujifar; Kamalika Chaudhuri; Chuan Guo 001 (CoRR, 2024)

Evaluating Deep Unlearning in Large Language Models. - Ruihan Wu; Chhavi Yadav; Russ Salakhutdinov; Kamalika Chaudhuri (CoRR, 2024)

Auditing f-Differential Privacy in One Run. - Saeed Mahloujifar; Luca Melis; Kamalika Chaudhuri (CoRR, 2024)

Privacy-Preserving Retrieval Augmented Generation with Differential Privacy. - Tatsuki Koga; Ruihan Wu; Kamalika Chaudhuri (CoRR, 2024)

Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds. - Kamalika Chaudhuri; Chuan Guo 001; Laurens van der Maaten; Saeed Mahloujifar; Mark Tygert (Trans. Mach. Learn. Res., 2024)

XAudit : A Learning-Theoretic Look at Auditing with Explanations. - Chhavi Yadav; Michal Moshkovitz; Kamalika Chaudhuri (Trans. Mach. Learn. Res., 2024)

On the Reliability of Membership Inference Attacks. - Amrita Roy Chowdhury 001; Zhifeng Kong; Kamalika Chaudhuri (SaTML, 2025)

Machine Learning with Privacy for Protected Attributes. - Saeed Mahloujifar; Chuan Guo 001; G. Edward Suh; Kamalika Chaudhuri (SP, 2025)

A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection. - Konstantin Garov; Kamalika Chaudhuri (CoRR, 2025)

ExpProof : Operationalizing Explanations for Confidential Models with ZKPs. - Chhavi Yadav; Evan Laufer; Dan Boneh; Kamalika Chaudhuri (CoRR, 2025)

AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents. - Arman Zharmagambetov; Chuan Guo 001; Ivan Evtimov; Maya Pavlova; Ruslan Salakhutdinov; Kamalika Chaudhuri (CoRR, 2025)

Measuring Déjà vu Memorization Efficiently. - Narine Kokhlikyan; Bargav Jayaraman; Florian Bordes; Chuan Guo 001; Kamalika Chaudhuri (CoRR, 2025)

WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks. - Ivan Evtimov; Arman Zharmagambetov; Aaron Grattafiori; Chuan Guo 001; Kamalika Chaudhuri (CoRR, 2025)

How much do language models memorize? - John X. Morris; Chawin Sitawarin; Chuan Guo 001; Narine Kokhlikyan; G. Edward Suh; Alexander M. Rush; Kamalika Chaudhuri; Saeed Mahloujifar (CoRR, 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 005 (CoRR, 2025)

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions. - Polina Kirichenko; Mark Ibrahim; Kamalika Chaudhuri; Samuel J. Bell (CoRR, 2025)

Can We Infer Confidential Properties of Training Data from LLMs? - Pengrun Huang; Chhavi Yadav; Ruihan Wu; Kamalika Chaudhuri (CoRR, 2025)

Learning-Time Encoding Shapes Unlearning in LLMs. - Ruihan Wu; Konstantin Garov; Kamalika Chaudhuri (CoRR, 2025)

Embodied AI Agents: Modeling the World. - Pascale Fung; Yoram Bachrach; Asli Celikyilmaz; Kamalika Chaudhuri; Delong Chen; Willy Chung; Emmanuel Dupoux; Hervé Jégou; Alessandro Lazaric; Arjun Majumdar; Andrea Madotto; Franziska Meier; et al. (CoRR, 2025)