| 2025 | AsiaCCS | Robust Locally Differentially Private Graph Analysis. | Amrita Roy Chowdhury, Jacob Imola, Kamalika Chaudhuri |
| 2025 | CCS | SecAlign: Defending Against Prompt Injection with Preference Optimization. | Sizhe Chen, Arman Zharmagambetov, Saeed Mahloujifar, Kamalika Chaudhuri, David A. Wagner, Chuan Guo |
| 2025 | ICML | Auditing $f$-differential privacy in one run. | Saeed Mahloujifar, Luca Melis, Kamalika Chaudhuri |
| 2025 | ICML | ExpProof : Operationalizing Explanations for Confidential Models with ZKPs. | Chhavi Yadav, Evan Laufer, Dan Boneh, Kamalika Chaudhuri |
| 2025 | SP | Machine Learning with Privacy for Protected Attributes. | Saeed Mahloujifar, Chuan Guo, G. Edward Suh, Kamalika Chaudhuri |
| 2024 | CCS | Metric Differential Privacy at the User-Level via the Earth-Mover's Distance. | Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri |
| 2024 | ICLR | 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 |
| 2024 | ICML | Differentially Private Representation Learning via Image Captioning. | Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo |
| 2024 | ICML | FairProof : Confidential and Certifiable Fairness for Neural Networks. | Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri |
| 2024 | ICML | ViP: A Differentially Private Foundation Model for Computer Vision. | Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo |
| 2023 | ALT | Robust Empirical Risk Minimization with Tolerance. | Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri |
| 2023 | ICML | Data-Copying in Generative Models: A Formal Framework. | Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri |
| 2023 | ICML | Why does Throwing Away Data Improve Worst-Group Error? | Kamalika Chaudhuri, Kartik Ahuja, Martn Arjovsky, David Lopez-Paz |
| 2023 | ICML | Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. | Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat |
| 2023 | ICML | A Two-Stage Active Learning Algorithm for k-Nearest Neighbors. | Nicholas Rittler, Kamalika Chaudhuri |
| 2022 | ACL | Sentence-level Privacy for Document Embeddings. | Casey Meehan, Khalil Mrini, Kamalika Chaudhuri |
| 2022 | AISTATS | Privacy Amplification by Subsampling in Time Domain. | Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri |
| 2022 | ALT | Privacy Amplification via Shuffling for Linear Contextual Bandits. | Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta |
| 2022 | CCS | Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model. | Jacob Imola, Takao Murakami, Kamalika Chaudhuri |
| 2022 | CCS | Forgeability and Membership Inference Attacks. | Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri |
| 2022 | ICLR | Privacy Implications of Shuffling. | Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha |
| 2022 | ICML | Thompson Sampling for Robust Transfer in Multi-Task Bandits. | Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri |
| 2022 | ICML | Bounding Training Data Reconstruction in Private (Deep) Learning. | Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten |
| 2022 | UAI | Privacy-aware compression for federated data analysis. | Kamalika Chaudhuri, Chuan Guo, Mike Rabbat |
| 2021 | AISTATS | Approximate Data Deletion from Machine Learning Models. | Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou |
| 2021 | AISTATS | Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. | Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang |
| 2021 | AISTATS | Location Trace Privacy Under Conditional Priors. | Casey Meehan, Kamalika Chaudhuri |
| 2021 | AISTATS | Multitask Bandit Learning Through Heterogeneous Feedback Aggregation. | Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri |
| 2021 | ICML | Sample Complexity of Robust Linear Classification on Separated Data. | Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri |
| 2021 | ICML | Connecting Interpretability and Robustness in Decision Trees through Separation. | Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri |
| 2020 | AISTATS | The Expressive Power of a Class of Normalizing Flow Models. | Zhifeng Kong, Kamalika Chaudhuri |
| 2020 | AISTATS | A Three Sample Hypothesis Test for Evaluating Generative Models. | Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta |
| 2020 | AISTATS | Robustness for Non-Parametric Classification: A Generic Attack and Defense. | Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri |
| 2020 | ICML | When are Non-Parametric Methods Robust? | Robi Bhattacharjee, Kamalika Chaudhuri |
| 2020 | IJCAI | Variational Bayes in Private Settings (VIPS) (Extended Abstract). | James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling |
| 2020 | SIGCSE | 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 |
| 2019 | ISIT | Profile-based Privacy for Locally Private Computations. | Joseph Geumlek, Kamalika Chaudhuri |
| 2018 | ICML | Analyzing the Robustness of Nearest Neighbors to Adversarial Examples. | Yizhen Wang, Somesh Jha, Kamalika Chaudhuri |
| 2018 | ICML | Active Learning with Logged Data. | Songbai Yan, Kamalika Chaudhuri, Tara Javidi |
| 2017 | ICML | Active Heteroscedastic Regression. | Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan |
| 2017 | SIGMOD | Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics. | Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton |
| 2017 | SIGMOD | Pufferfish Privacy Mechanisms for Correlated Data. | Shuang Song, Yizhen Wang, Kamalika Chaudhuri |
| 2016 | COLT | The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions. | Chicheng Zhang, Kamalika Chaudhuri |
| 2016 | UAI | On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. | James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri |
| 2015 | AISTATS | Learning from Data with Heterogeneous Noise using SGD. | Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate |
| 2015 | HCOMP | Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons. | James Y. Zou, Kamalika Chaudhuri, Adam Tauman Kalai |
| 2012 | ICML | Convergence Rates for Differentially Private Statistical Estimation. | Kamalika Chaudhuri, Daniel J. Hsu |
| 2010 | UAI | An Online Learning-based Framework for Tracking. | Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu |
| 2009 | ICML | Multi-view clustering via canonical correlation analysis. | Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan |
| 2009 | INFOCOM | Online Bipartite Perfect Matching With Augmentations. | Kamalika Chaudhuri, Constantinos Daskalakis, Robert D. Kleinberg, Henry Lin |
| 2008 | COLT | Finding Metric Structure in Information Theoretic Clustering. | Kamalika Chaudhuri, Andrew McGregor |
| 2008 | COLT | Learning Mixtures of Product Distributions Using Correlations and Independence. | Kamalika Chaudhuri, Satish Rao |
| 2008 | COLT | Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed Distributions. | Kamalika Chaudhuri, Satish Rao |
| 2007 | PODS | Privacy, accuracy, and consistency too: a holistic solution to contingency table release. | Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, Kunal Talwar |
| 2007 | SODA | A rigorous analysis of population stratification with limited data. | Kamalika Chaudhuri, Eran Halperin, Satish Rao, Shuheng Zhou |
| 2006 | CRYPTO | When Random Sampling Preserves Privacy. | Kamalika Chaudhuri, Nina Mishra |
| 2006 | ICALP | A Push-Relabel Algorithm for Approximating Degree Bounded MSTs. | Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar |
| 2006 | SODA | On the tandem duplication-random loss model of genome rearrangement. | Kamalika Chaudhuri, Kevin C. Chen, Radu Mihaescu, Satish Rao |
| 2005 | COCOON | Server Allocation Algorithms for Tiered Systems. | Kamalika Chaudhuri, Anshul Kothari, Rudi Pendavingh, Ram Swaminathan, Robert Endre Tarjan, Yunhong Zhou |
| 2005 | SIGMETRICS | 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 |
| 2005 | SPAA | 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 |
| 2004 | PODC | Selfish caching in distributed systems: a game-theoretic analysis. | Byung-Gon Chun, Kamalika Chaudhuri, Hoeteck Wee, Marco Barreno, Christos H. Papadimitriou, John Kubiatowicz |
| 2003 | FOCS | Paths, Trees, and Minimum Latency Tours. | Kamalika Chaudhuri, Brighten Godfrey, Satish Rao, Kunal Talwar |
| 2003 | WCNC | Location determination of a mobile device using IEEE 802.11b access point signals. | Siddhartha Saha, Kamalika Chaudhuri, Dheeraj Sanghi, Pravin Bhagwat |