Kamalika Chaudhuri

255 papers A* 56A 15B 7Misc 1Journal 142Unranked 34

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	How Vulnerable Are AI Agents to Indirect Prompt Injections? Insights from a Large-Scale Public Competition. CoRR Mateusz Dziemian, Maxwell Lin, Xiaohan Fu, Micha Nowak, Nick Winter, Eliot Krzysztof Jones, Andy Zou, Lama Ahmad, Kamalika Chaudhuri, Sahana Chennabasappa, Xander Davies, Lauren Deason, Benjamin L. Edelman, Tanner Emek, Ivan Evtimov, Jim Gust, Maia Hamin, Kat He, Klaudia Krawiecka, Riccardo Patana, Neil Perry, Troy Peterson, Xiangyu Qi, Javier Rando, Zifan Wang, Zihan Wang, Spencer Whitman, Eric Winsor, Arman Zharmagambetov, Matt Fredrikson, Zico Kolter
2025	J	`jnl`	A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection. CoRR Konstantin Garov, Kamalika Chaudhuri
2025	J	`jnl`	AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions. CoRR Polina Kirichenko, Mark Ibrahim, Kamalika Chaudhuri, Samuel J. Bell
2025	J	`jnl`	AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents. CoRR Arman Zharmagambetov, Chuan Guo, Ivan Evtimov, Maya Pavlova, Ruslan Salakhutdinov, Kamalika Chaudhuri
2025	A*	`conf`	Auditing $f$-differential privacy in one run. ICML Saeed Mahloujifar, Luca Melis, Kamalika Chaudhuri
2025	J	`jnl`	CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs. CoRR Niloofar Mireshghallah, Neal Mangaokar, Narine Kokhlikyan, Arman Zharmagambetov, Manzil Zaheer, Saeed Mahloujifar, Kamalika Chaudhuri
2025	J	`jnl`	Can We Infer Confidential Properties of Training Data from LLMs? CoRR Pengrun Huang, Chhavi Yadav, Ruihan Wu, Kamalika Chaudhuri
2025	J	`jnl`	Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness. CoRR 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
2025	J	`jnl`	Embodied AI Agents: Modeling the World. CoRR Pascale Fung, Yoram Bachrach, Asli Celikyilmaz, Kamalika Chaudhuri, Delong Chen, Willy Chung, Emmanuel Dupoux, Hervé Jégou, Alessandro Lazaric, Arjun Majumdar, Andrea Madotto, Franziska Meier, Florian Metze, Théo Moutakanni, Juan Pino, Basile Terver, Joseph Tighe, Jitendra Malik
2025	A*	`conf`	ExpProof : Operationalizing Explanations for Confidential Models with ZKPs. ICML Chhavi Yadav, Evan Laufer, Dan Boneh, Kamalika Chaudhuri
2025	J	`jnl`	ExpProof : Operationalizing Explanations for Confidential Models with ZKPs. CoRR Chhavi Yadav, Evan Laufer, Dan Boneh, Kamalika Chaudhuri
2025	J	`jnl`	How much do language models memorize? CoRR John X. Morris, Chawin Sitawarin, Chuan Guo, Narine Kokhlikyan, G. Edward Suh, Alexander M. Rush, Kamalika Chaudhuri, Saeed Mahloujifar
2025	J	`jnl`	Learning-Time Encoding Shapes Unlearning in LLMs. CoRR Ruihan Wu, Konstantin Garov, Kamalika Chaudhuri
2025	A*	`conf`	Machine Learning with Privacy for Protected Attributes. SP Saeed Mahloujifar, Chuan Guo, G. Edward Suh, Kamalika Chaudhuri
2025	J	`jnl`	Machine Learning with Privacy for Protected Attributes. CoRR Saeed Mahloujifar, Chuan Guo, G. Edward Suh, Kamalika Chaudhuri
2025	J	`jnl`	Measuring Déjà vu Memorization Efficiently. CoRR Narine Kokhlikyan, Bargav Jayaraman, Florian Bordes, Chuan Guo, Kamalika Chaudhuri
2025	—	`conf`	On the Reliability of Membership Inference Attacks. SaTML Amrita Roy Chowdhury, Zhifeng Kong, Kamalika Chaudhuri
2025	J	`jnl`	Privacy Blur: Quantifying Privacy and Utility for Image Data Release. CoRR Saeed Mahloujifar, Narine Kokhlikyan, Chuan Guo, Kamalika Chaudhuri
2025	J	`jnl`	RL Is a Hammer and LLMs Are Nails: A Simple Reinforcement Learning Recipe for Strong Prompt Injection. CoRR Yuxin Wen, Arman Zharmagambetov, Ivan Evtimov, Narine Kokhlikyan, Tom Goldstein, Kamalika Chaudhuri, Chuan Guo
2025	A	`conf`	Robust Locally Differentially Private Graph Analysis. AsiaCCS Amrita Roy Chowdhury, Jacob Imola, Kamalika Chaudhuri
2025	J	`jnl`	Safety Alignment of LMs via Non-cooperative Games. CoRR Anselm Paulus, Ilia Kulikov, Brandon Amos, Rémi Munos, Ivan Evtimov, Kamalika Chaudhuri, Arman Zharmagambetov
2025	A*	`conf`	SecAlign: Defending Against Prompt Injection with Preference Optimization. CCS Sizhe Chen, Arman Zharmagambetov, Saeed Mahloujifar, Kamalika Chaudhuri, David A. Wagner, Chuan Guo
2025	J	`jnl`	WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks. CoRR Ivan Evtimov, Arman Zharmagambetov, Aaron Grattafiori, Chuan Guo, Kamalika Chaudhuri
2025	J	`jnl`	Z0-Inf: Zeroth Order Approximation for Data Influence. CoRR Narine Kokhlikyan, Kamalika Chaudhuri, Saeed Mahloujifar
2024	J	`jnl`	Aligning LLMs to Be Robust Against Prompt Injection. CoRR Sizhe Chen, Arman Zharmagambetov, Saeed Mahloujifar, Kamalika Chaudhuri, Chuan Guo
2024	J	`jnl`	An Introduction to Vision-Language Modeling. CoRR 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
2024	J	`jnl`	Auditing CoRR Saeed Mahloujifar, Luca Melis, Kamalika Chaudhuri
2024	J	`jnl`	Better Membership Inference Privacy Measurement through Discrepancy. CoRR Ruihan Wu, Pengrun Huang, Kamalika Chaudhuri
2024	J	`jnl`	Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift. CoRR Robi Bhattacharjee, Nick Rittler, Kamalika Chaudhuri
2024	J	`jnl`	DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning. CoRR Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero-Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo
2024	—	`conf`	Data Redaction from Conditional Generative Models. SaTML Zhifeng Kong, Kamalika Chaudhuri
2024	—	`conf`	Differentially Private Multi-Site Treatment Effect Estimation. SaTML Tatsuki Koga, Kamalika Chaudhuri, David Page
2024	A*	`conf`	Differentially Private Representation Learning via Image Captioning. ICML Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2024	J	`jnl`	Differentially Private Representation Learning via Image Captioning. CoRR Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2024	A*	`conf`	Distribution Learning with Valid Outputs Beyond the Worst-Case. NeurIPS Nicholas Rittler, Kamalika Chaudhuri
2024	J	`jnl`	Distribution Learning with Valid Outputs Beyond the Worst-Case. CoRR Nick Rittler, Kamalika Chaudhuri
2024	A*	`conf`	Déjà Vu Memorization in Vision-Language Models. NeurIPS Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri
2024	J	`jnl`	Déjà Vu Memorization in Vision-Language Models. CoRR Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri
2024	A*	`conf`	Effective pruning of web-scale datasets based on complexity of concept clusters. ICLR Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos
2024	J	`jnl`	Effective pruning of web-scale datasets based on complexity of concept clusters. CoRR Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos
2024	J	`jnl`	Evaluating Deep Unlearning in Large Language Models. CoRR Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov, Kamalika Chaudhuri
2024	A*	`conf`	FairProof : Confidential and Certifiable Fairness for Neural Networks. ICML Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri
2024	J	`jnl`	FairProof : Confidential and Certifiable Fairness for Neural Networks. CoRR Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri
2024	J	`jnl`	Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds. CoRR Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert
2024	J	`jnl`	Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds. Trans. Mach. Learn. Res. Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert
2024	J	`jnl`	Influence-based Attributions can be Manipulated. CoRR Chhavi Yadav, Ruihan Wu, Kamalika Chaudhuri
2024	A*	`conf`	Measuring Dejavu Memorization Efficiently. NeurIPS Narine Kokhlikyan, Bargav Jayaraman, Florian Bordes, Chuan Guo, Kamalika Chaudhuri
2024	J	`jnl`	Measuring Privacy Loss in Distributed Spatio-Temporal Data. CoRR Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
2024	A*	`conf`	Metric Differential Privacy at the User-Level via the Earth-Mover's Distance. CCS Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri
2024	J	`jnl`	Metric Differential Privacy at the User-Level. CoRR Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri
2024	A*	`conf`	On Differentially Private U Statistics. NeurIPS Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar
2024	J	`jnl`	On Differentially Private U Statistics. CoRR Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar
2024	J	`jnl`	Privacy Amplification for the Gaussian Mechanism via Bounded Support. CoRR Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo
2024	J	`jnl`	Privacy-Preserving Retrieval Augmented Generation with Differential Privacy. CoRR Tatsuki Koga, Ruihan Wu, Kamalika Chaudhuri
2024	J	`jnl`	Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations. CoRR Christian Tomani, Kamalika Chaudhuri, Ivan Evtimov, Daniel Cremers, Mark Ibrahim
2024	A*	`conf`	ViP: A Differentially Private Foundation Model for Computer Vision. ICML Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2024	J	`jnl`	XAudit : A Learning-Theoretic Look at Auditing with Explanations. Trans. Mach. Learn. Res. Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri
2023	A*	`conf`	A Two-Stage Active Learning Algorithm for k-Nearest Neighbors. ICML Nicholas Rittler, Kamalika Chaudhuri
2023	A*	`conf`	Agnostic Multi-Group Active Learning. NeurIPS Nicholas Rittler, Kamalika Chaudhuri
2023	J	`jnl`	Agnostic Multi-Group Active Learning. CoRR Nick Rittler, Kamalika Chaudhuri
2023	J	`jnl`	Can Membership Inferencing be Refuted? CoRR Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri
2023	J	`jnl`	Data Redaction from Conditional Generative Models. CoRR Zhifeng Kong, Kamalika Chaudhuri
2023	—	`conf`	Data Redaction from Pre-trained GANs. SaTML Zhifeng Kong, Kamalika Chaudhuri
2023	A*	`conf`	Data-Copying in Generative Models: A Formal Framework. ICML Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri
2023	J	`jnl`	Data-Copying in Generative Models: A Formal Framework. CoRR Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri
2023	J	`jnl`	Differentially Private Multi-Site Treatment Effect Estimation. CoRR Tatsuki Koga, Kamalika Chaudhuri, David Page
2023	A*	`conf`	Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning. NeurIPS Casey Meehan, Florian Bordes, Pascal Vincent, Kamalika Chaudhuri, Chuan Guo
2023	J	`jnl`	Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning. CoRR Casey Meehan, Florian Bordes, Pascal Vincent, Kamalika Chaudhuri, Chuan Guo
2023	J	`jnl`	Large-Scale Public Data Improves Differentially Private Image Generation Quality. CoRR Ruihan Wu, Chuan Guo, Kamalika Chaudhuri
2023	A*	`conf`	Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. ICML Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat
2023	J	`jnl`	Probing Predictions on OOD Images via Nearest Categories. Trans. Mach. Learn. Res. Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri
2023	B	`conf`	Robust Empirical Risk Minimization with Tolerance. ALT Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri
2023	J	`jnl`	Unified Uncertainty Calibration. CoRR Kamalika Chaudhuri, David Lopez-Paz
2023	J	`jnl`	ViP: A Differentially Private Foundation Model for Computer Vision. CoRR Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2023	A*	`conf`	Why does Throwing Away Data Improve Worst-Group Error? ICML Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz
2022	J	`jnl`	A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models. CoRR Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri
2022	J	`jnl`	A Two-Stage Active Learning Algorithm for k-Nearest Neighbors. CoRR Nick Rittler, Kamalika Chaudhuri
2022	A*	`conf`	Bounding Training Data Reconstruction in Private (Deep) Learning. ICML Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten
2022	J	`jnl`	Bounding Training Data Reconstruction in Private (Deep) Learning. CoRR Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten
2022	A*	`conf`	Communication-Efficient Triangle Counting under Local Differential Privacy. USENIX Security Symposium Jacob Imola, Takao Murakami, Kamalika Chaudhuri
2022	J	`jnl`	Differentially Private Subgraph Counting in the Shuffle Model. CoRR Jacob Imola, Takao Murakami, Kamalika Chaudhuri
2022	A*	`conf`	Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model. CCS Jacob Imola, Takao Murakami, Kamalika Chaudhuri
2022	—	`conf`	Forgeability and Membership Inference Attacks. AISec@CCS Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri
2022	J	`jnl`	Forgetting Data from Pre-trained GANs. CoRR Zhifeng Kong, Kamalika Chaudhuri
2022	A*	`ed.`	International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. ICML Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, Sivan Sabato
2022	A	`conf`	Privacy Amplification by Subsampling in Time Domain. AISTATS Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
2022	J	`jnl`	Privacy Amplification by Subsampling in Time Domain. CoRR Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
2022	B	`conf`	Privacy Amplification via Shuffling for Linear Contextual Bandits. ALT Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta
2022	A*	`conf`	Privacy Implications of Shuffling. ICLR Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha
2022	J	`jnl`	Privacy-Aware Compression for Federated Data Analysis. CoRR Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
2022	A	`conf`	Privacy-aware compression for federated data analysis. UAI Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
2022	J	`jnl`	Robust Empirical Risk Minimization with Tolerance. CoRR Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri
2022	J	`jnl`	Robustness of Locally Differentially Private Graph Analysis Against Poisoning. CoRR Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri
2022	—	`conf`	Sentence-level Privacy for Document Embeddings. ACL (1) Casey Meehan, Khalil Mrini, Kamalika Chaudhuri
2022	J	`jnl`	Sentence-level Privacy for Document Embeddings. CoRR Casey Meehan, Khalil Mrini, Kamalika Chaudhuri
2022	J	`jnl`	The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning. CoRR Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat
2022	A*	`conf`	Thompson Sampling for Robust Transfer in Multi-Task Bandits. ICML Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri
2022	J	`jnl`	Thompson Sampling for Robust Transfer in Multi-Task Bandits. CoRR Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri
2022	J	`jnl`	Throwing Away Data Improves Worst-Class Error in Imbalanced Classification. CoRR Martín Arjovsky, Kamalika Chaudhuri, David Lopez-Paz
2022	J	`jnl`	Understanding Rare Spurious Correlations in Neural Networks. CoRR Yao-Yuan Yang, Kamalika Chaudhuri
2021	J	`jnl`	A Shuffling Framework for Local Differential Privacy. CoRR Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha
2021	A	`conf`	Approximate Data Deletion from Machine Learning Models. AISTATS Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou
2021	—	`conf`	Behavior of k-NN as an Instance-Based Explanation Method. PKDD/ECML Workshops (1) Chhavi Yadav, Kamalika Chaudhuri
2021	J	`jnl`	Behavior of k-NN as an Instance-Based Explanation Method. CoRR Chhavi Yadav, Kamalika Chaudhuri
2021	J	`jnl`	Communication-Efficient Triangle Counting under Local Differential Privacy. CoRR Jacob Imola, Takao Murakami, Kamalika Chaudhuri
2021	A*	`conf`	Connecting Interpretability and Robustness in Decision Trees through Separation. ICML Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
2021	J	`jnl`	Connecting Interpretability and Robustness in Decision Trees through Separation. CoRR Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
2021	J	`jnl`	Consistent Non-Parametric Methods for Adaptive Robustness. CoRR Robi Bhattacharjee, Kamalika Chaudhuri
2021	A*	`conf`	Consistent Non-Parametric Methods for Maximizing Robustness. NeurIPS Robi Bhattacharjee, Kamalika Chaudhuri
2021	A*	`conf`	Locally Differentially Private Analysis of Graph Statistics. USENIX Security Symposium Jacob Imola, Takao Murakami, Kamalika Chaudhuri
2021	A	`conf`	Location Trace Privacy Under Conditional Priors. AISTATS Casey Meehan, Kamalika Chaudhuri
2021	J	`jnl`	Location Trace Privacy Under Conditional Priors. CoRR Casey Meehan, Kamalika Chaudhuri
2021	A	`conf`	Multitask Bandit Learning Through Heterogeneous Feedback Aggregation. AISTATS Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri
2021	J	`jnl`	Privacy Amplification Via Bernoulli Sampling. CoRR Jacob Imola, Kamalika Chaudhuri
2021	J	`jnl`	Privacy Amplification via Shuffling for Linear Contextual Bandits. CoRR Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta
2021	A	`conf`	Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. AISTATS Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
2021	J	`jnl`	Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. J. Mach. Learn. Res. Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
2021	A*	`conf`	Sample Complexity of Robust Linear Classification on Separated Data. ICML Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri
2021	A*	`conf`	Understanding Instance-based Interpretability of Variational Auto-Encoders. NeurIPS Zhifeng Kong, Kamalika Chaudhuri
2021	J	`jnl`	Understanding Instance-based Interpretability of Variational Auto-Encoders. CoRR Zhifeng Kong, Kamalika Chaudhuri
2021	J	`jnl`	Universal Approximation of Residual Flows in Maximum Mean Discrepancy. CoRR Zhifeng Kong, Kamalika Chaudhuri
2020	A*	`conf`	A Closer Look at Accuracy vs. Robustness. NeurIPS Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri
2020	J	`jnl`	A Non-Parametric Test to Detect Data-Copying in Generative Models. CoRR Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta
2020	A	`conf`	A Three Sample Hypothesis Test for Evaluating Generative Models. AISTATS Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta
2020	J	`jnl`	Adversarial Robustness Through Local Lipschitzness. CoRR Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri
2020	J	`jnl`	Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations. CoRR Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Y. Zou
2020	J	`jnl`	Close Category Generalization. CoRR Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri
2020	—	`conf`	Efficient Distributed Training in Heterogeneous Mobile Networks with Active Sampling. MSN Yunhui Guo, Xiaofan Yu, Kamalika Chaudhuri, Tajana Rosing
2020	A*	`conf`	Exploring Connections Between Active Learning and Model Extraction. USENIX Security Symposium Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan
2020	J	`jnl`	Locally Differentially Private Analysis of Graph Statistics. CoRR Jacob Imola, Takao Murakami, Kamalika Chaudhuri
2020	J	`jnl`	Multitask Bandit Learning through Heterogeneous Feedback Aggregation. CoRR Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri
2020	A	`conf`	PABLO: Helping Novices Debug Python Code Through Data-Driven Fault Localization. SIGCSE Benjamin Cosman, Madeline Endres, Georgios Sakkas, Leon Medvinsky, Yao-Yuan Yang, Ranjit Jhala, Kamalika Chaudhuri, Westley Weimer
2020	J	`jnl`	Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. CoRR Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
2020	A	`conf`	Robustness for Non-Parametric Classification: A Generic Attack and Defense. AISTATS Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri
2020	J	`jnl`	Sample Complexity of Adversarially Robust Linear Classification on Separated Data. CoRR Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri
2020	J	`jnl`	Successive Refinement of Privacy. CoRR Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi
2020	J	`jnl`	Successive Refinement of Privacy. IEEE J. Sel. Areas Inf. Theory Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi
2020	A	`conf`	The Expressive Power of a Class of Normalizing Flow Models. AISTATS Zhifeng Kong, Kamalika Chaudhuri
2020	J	`jnl`	The Expressive Power of a Class of Normalizing Flow Models. CoRR Zhifeng Kong, Kamalika Chaudhuri
2020	J	`jnl`	Trustworthy AI Inference Systems: An Industry Research View. CoRR 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
2020	J	`jnl`	Variational Bayes In Private Settings (VIPS). J. Artif. Intell. Res. Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling
2020	A*	`conf`	Variational Bayes in Private Settings (VIPS) (Extended Abstract). IJCAI James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling
2020	A*	`conf`	When are Non-Parametric Methods Robust? ICML Robi Bhattacharjee, Kamalika Chaudhuri
2020	J	`jnl`	When are Non-Parametric Methods Robust? CoRR Robi Bhattacharjee, Kamalika Chaudhuri
2019	J	`jnl`	Adversarial Examples for Non-Parametric Methods: Attacks, Defenses and Large Sample Limits. CoRR Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri
2019	J	`jnl`	An Investigation of Data Poisoning Defenses for Online Learning. CoRR Yizhen Wang, Kamalika Chaudhuri
2019	A*	`conf`	Capacity Bounded Differential Privacy. NeurIPS Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala
2019	J	`jnl`	Capacity Bounded Differential Privacy. CoRR Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala
2019	J	`jnl`	Location Trace Privacy Under Conditional Priors. CoRR Casey Meehan, Kamalika Chaudhuri
2019	A*	`ed.`	Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA ICML Kamalika Chaudhuri, Ruslan Salakhutdinov
2019	J	`jnl`	Profile-Based Privacy for Locally Private Computations. CoRR Joseph Geumlek, Kamalika Chaudhuri
2019	B	`conf`	Profile-based Privacy for Locally Private Computations. ISIT Joseph Geumlek, Kamalika Chaudhuri
2019	A	`ed.`	The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 16-18 April 2019, Naha, Okinawa, Japan AISTATS Kamalika Chaudhuri, Masashi Sugiyama
2019	A*	`conf`	The Label Complexity of Active Learning from Observational Data. NeurIPS Songbai Yan, Kamalika Chaudhuri, Tara Javidi
2019	J	`jnl`	The Label Complexity of Active Learning from Observational Data. CoRR Songbai Yan, Kamalika Chaudhuri, Tara Javidi
2018	A*	`conf`	Active Learning with Logged Data. ICML Songbai Yan, Kamalika Chaudhuri, Tara Javidi
2018	J	`jnl`	Active Learning with Logged Data. CoRR Songbai Yan, Kamalika Chaudhuri, Tara Javidi
2018	A*	`conf`	Analyzing the Robustness of Nearest Neighbors to Adversarial Examples. ICML Yizhen Wang, Somesh Jha, Kamalika Chaudhuri
2018	J	`jnl`	Data Poisoning Attacks against Online Learning. CoRR Yizhen Wang, Kamalika Chaudhuri
2018	J	`jnl`	Differentially Private Continual Release of Graph Statistics. CoRR Shuang Song, Susan Little, Sanjay Mehta, Staal Amund Vinterbo, Kamalika Chaudhuri
2018	J	`jnl`	Model Extraction and Active Learning. CoRR Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan
2018	J	`jnl`	Special Issue on ALT 2015: Guest Editors' Introduction. Theor. Comput. Sci. Kamalika Chaudhuri, Claudio Gentile
2018	J	`jnl`	Spectral Learning of Binomial HMMs for DNA Methylation Data. CoRR Chicheng Zhang, Eran A. Mukamel, Kamalika Chaudhuri
2018	J	`jnl`	The Inductive Bias of Restricted f-GANs. CoRR Shuang Liu, Kamalika Chaudhuri
2017	A*	`conf`	Active Heteroscedastic Regression. ICML Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan
2017	J	`jnl`	Analyzing the Robustness of Nearest Neighbors to Adversarial Examples. CoRR Yizhen Wang, Somesh Jha, Kamalika Chaudhuri
2017	—	`conf`	Approximation and Convergence Properties of Generative Adversarial Learning. NIPS Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
2017	J	`jnl`	Approximation and Convergence Properties of Generative Adversarial Learning. CoRR Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
2017	—	`conf`	Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics. SIGMOD Conference Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton
2017	J	`jnl`	Composition Properties of Inferential Privacy for Time-Series Data. CoRR Shuang Song, Kamalika Chaudhuri
2017	—	`conf`	Composition properties of inferential privacy for time-series data. Allerton Shuang Song, Kamalika Chaudhuri
2017	J	`jnl`	Learning to Blame: Localizing Novice Type Errors with Data-Driven Diagnosis. CoRR Eric L. Seidel, Huma Sibghat, Kamalika Chaudhuri, Westley Weimer, Ranjit Jhala
2017	J	`jnl`	Learning to blame: localizing novice type errors with data-driven diagnosis. Proc. ACM Program. Lang. Eric L. Seidel, Huma Sibghat, Kamalika Chaudhuri, Westley Weimer, Ranjit Jhala
2017	—	`conf`	Pufferfish Privacy Mechanisms for Correlated Data. SIGMOD Conference Shuang Song, Yizhen Wang, Kamalika Chaudhuri
2017	—	`conf`	Renyi Differential Privacy Mechanisms for Posterior Sampling. NIPS Joseph Geumlek, Shuang Song, Kamalika Chaudhuri
2017	J	`jnl`	Rényi Differential Privacy Mechanisms for Posterior Sampling. CoRR Joseph Geumlek, Shuang Song, Kamalika Chaudhuri
2016	—	`conf`	Active Learning from Imperfect Labelers. NIPS Songbai Yan, Kamalika Chaudhuri, Tara Javidi
2016	J	`jnl`	Active Learning from Imperfect Labelers. CoRR Songbai Yan, Kamalika Chaudhuri, Tara Javidi
2016	J	`jnl`	Convex Optimization For Non-Convex Problems via Column Generation. CoRR Julian Yarkony, Kamalika Chaudhuri
2016	J	`jnl`	Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics. CoRR Xi Wu, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton
2016	A	`conf`	On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. UAI James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri
2016	J	`jnl`	On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. CoRR James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri
2016	J	`jnl`	Practical Privacy For Expectation Maximization. CoRR Mijung Park, Jimmy Foulds, Kamalika Chaudhuri, Max Welling
2016	J	`jnl`	Privacy-preserving Analysis of Correlated Data. CoRR Yizhen Wang, Shuang Song, Kamalika Chaudhuri
2016	J	`jnl`	Private Topic Modeling. CoRR Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling
2016	A*	`conf`	The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions. COLT Chicheng Zhang, Kamalika Chaudhuri
2016	J	`jnl`	The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions. CoRR Chicheng Zhang, Kamalika Chaudhuri
2016	J	`jnl`	Variational Bayes In Private Settings (VIPS). CoRR Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling
2015	—	`conf`	Active Learning from Weak and Strong Labelers. NIPS Chicheng Zhang, Kamalika Chaudhuri
2015	J	`jnl`	Active Learning from Weak and Strong Labelers. CoRR Chicheng Zhang, Kamalika Chaudhuri
2015	—	`conf`	Active learning from noisy and abstention feedback. Allerton Songbai Yan, Kamalika Chaudhuri, Tara Javidi
2015	B	`ed.`	Algorithmic Learning Theory - 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings ALT Kamalika Chaudhuri, Claudio Gentile, Sandra Zilles
2015	J	`jnl`	Bayesian Active Learning With Non-Persistent Noise. IEEE Trans. Inf. Theory Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri
2015	—	`conf`	Convergence Rates of Active Learning for Maximum Likelihood Estimation. NIPS Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi
2015	J	`jnl`	Convergence Rates of Active Learning for Maximum Likelihood Estimation. CoRR Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi
2015	B	`conf`	Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons. HCOMP James Y. Zou, Kamalika Chaudhuri, Adam Tauman Kalai
2015	J	`jnl`	Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons. CoRR James Y. Zou, Kamalika Chaudhuri, Adam Tauman Kalai
2015	A	`conf`	Learning from Data with Heterogeneous Noise using SGD. AISTATS Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate
2015	—	`conf`	Spectral Learning of Large Structured HMMs for Comparative Epigenomics. NIPS Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin C. Chen
2015	J	`jnl`	Spectral Learning of Large Structured HMMs for Comparative Epigenomics. CoRR Chicheng Zhang, Jimin Song, Kevin C. Chen, Kamalika Chaudhuri
2014	—	`conf`	Beyond Disagreement-Based Agnostic Active Learning. NIPS Chicheng Zhang, Kamalika Chaudhuri
2014	J	`jnl`	Beyond Disagreement-based Agnostic Active Learning. CoRR Chicheng Zhang, Kamalika Chaudhuri
2014	J	`jnl`	Consistent Procedures for Cluster Tree Estimation and Pruning. IEEE Trans. Inf. Theory Kamalika Chaudhuri, Sanjoy Dasgupta, Samory Kpotufe, Ulrike von Luxburg
2014	J	`jnl`	Learning from Data with Heterogeneous Noise using SGD. CoRR Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate
2014	—	`conf`	Rates of Convergence for Nearest Neighbor Classification. NIPS Kamalika Chaudhuri, Sanjoy Dasgupta
2014	J	`jnl`	Rates of Convergence for Nearest Neighbor Classification. CoRR Kamalika Chaudhuri, Sanjoy Dasgupta
2014	—	`conf`	The Large Margin Mechanism for Differentially Private Maximization. NIPS Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
2014	J	`jnl`	The Large Margin Mechanism for Differentially Private Maximization. CoRR Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
2013	—	`conf`	A Stability-based Validation Procedure for Differentially Private Machine Learning. NIPS Kamalika Chaudhuri, Staal Amund Vinterbo
2013	J	`jnl`	A near-optimal algorithm for differentially-private principal components. J. Mach. Learn. Res. Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha
2013	—	`conf`	Extrinsic Jensen-Shannon divergence and noisy Bayesian active learning. Allerton Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri
2013	J	`jnl`	Noisy Bayesian Active Learning. CoRR Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri
2013	J	`jnl`	Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data. IEEE Signal Process. Mag. Anand D. Sarwate, Kamalika Chaudhuri
2013	—	`conf`	Stochastic gradient descent with differentially private updates. GlobalSIP Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate
2012	J	`jnl`	An Online Learning-based Framework for Tracking CoRR Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
2012	A*	`conf`	Convergence Rates for Differentially Private Statistical Estimation. ICML Kamalika Chaudhuri, Daniel J. Hsu
2012	J	`jnl`	Near-Optimal Algorithms for Differentially-Private Principal Components CoRR Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha
2012	—	`conf`	Near-optimal Differentially Private Principal Components. NIPS Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha
2012	—	`conf`	Noisy Bayesian active learning. Allerton Conference Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri
2012	A*	`conf`	Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model. COLT Kamalika Chaudhuri, Fan Chung Graham, Alexander Tsiatas
2012	J	`jnl`	iDASH: integrating data for analysis, anonymization, and sharing. J. Am. Medical Informatics Assoc. 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
2011	J	`jnl`	Differentially Private Empirical Risk Minimization. J. Mach. Learn. Res. Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate
2011	A*	`conf`	Sample Complexity Bounds for Differentially Private Learning. COLT Kamalika Chaudhuri, Daniel J. Hsu
2011	J	`jnl`	Spectral Methods for Learning Multivariate Latent Tree Structure CoRR Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
2011	—	`conf`	Spectral Methods for Learning Multivariate Latent Tree Structure. NIPS Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
2010	A	`conf`	An Online Learning-based Framework for Tracking. UAI Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
2010	—	`conf`	Rates of convergence for the cluster tree. NIPS Kamalika Chaudhuri, Sanjoy Dasgupta
2009	—	`conf`	A Parameter-free Hedging Algorithm. NIPS Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
2009	J	`jnl`	A parameter-free hedging algorithm CoRR Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
2009	J	`jnl`	A push-relabel approximation algorithm for approximating the minimum-degree MST problem and its generalization to matroids. Theor. Comput. Sci. Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar
2009	J	`jnl`	Differentially Private Support Vector Machines CoRR Anand D. Sarwate, Kamalika Chaudhuri, Claire Monteleoni
2009	J	`jnl`	Learning Mixtures of Gaussians using the k-means Algorithm CoRR Kamalika Chaudhuri, Sanjoy Dasgupta, Andrea Vattani
2009	A*	`conf`	Multi-view clustering via canonical correlation analysis. ICML Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan
2009	A*	`conf`	Online Bipartite Perfect Matching With Augmentations. INFOCOM Kamalika Chaudhuri, Constantinos Daskalakis, Robert D. Kleinberg, Henry Lin
2009	J	`jnl`	Tracking using explanation-based modeling CoRR Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
2009	J	`jnl`	What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTs. Algorithmica Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar
2008	—	`conf`	A Network Coloring Game. WINE Kamalika Chaudhuri, Fan Chung Graham, Mohammad Shoaib Jamall
2008	A*	`conf`	Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed Distributions. COLT Kamalika Chaudhuri, Satish Rao
2008	A*	`conf`	Finding Metric Structure in Information Theoretic Clustering. COLT Kamalika Chaudhuri, Andrew McGregor
2008	A*	`conf`	Learning Mixtures of Product Distributions Using Correlations and Independence. COLT Kamalika Chaudhuri, Satish Rao
2008	—	`conf`	Privacy-preserving logistic regression. NIPS Kamalika Chaudhuri, Claire Monteleoni
2007	A*	`conf`	A rigorous analysis of population stratification with limited data. SODA Kamalika Chaudhuri, Eran Halperin, Satish Rao, Shuheng Zhou
2007	A*	`conf`	Privacy, accuracy, and consistency too: a holistic solution to contingency table release. PODS Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, Kunal Talwar
2007	J	`jnl`	Server Allocation Algorithms for Tiered Systems. Algorithmica Kamalika Chaudhuri, Anshul Kothari, Rudi Pendavingh, Ram Swaminathan, Robert Endre Tarjan, Yunhong Zhou
2006	—	`conf`	A Push-Relabel Algorithm for Approximating Degree Bounded MSTs. ICALP (1) Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar
2006	A*	`conf`	On the tandem duplication-random loss model of genome rearrangement. SODA Kamalika Chaudhuri, Kevin C. Chen, Radu Mihaescu, Satish Rao
2006	A*	`conf`	When Random Sampling Preserves Privacy. CRYPTO Kamalika Chaudhuri, Nina Mishra
2005	A*	`conf`	Deadline scheduling for animation rendering. SIGMETRICS Eric Anderson, Dirk Beyer, Kamalika Chaudhuri, Terence Kelly, Norman Salazar, Cipriano A. Santos, Ram Swaminathan, Robert Endre Tarjan, Janet L. Wiener, Yunhong Zhou
2005	Misc	`conf`	Server Allocation Algorithms for Tiered Systems. COCOON Kamalika Chaudhuri, Anshul Kothari, Rudi Pendavingh, Ram Swaminathan, Robert Endre Tarjan, Yunhong Zhou
2005	B	`conf`	Value-maximizing deadline scheduling and its application to animation rendering. SPAA Eric Anderson, Dirk Beyer, Kamalika Chaudhuri, Terence Kelly, Norman Salazar, Cipriano A. Santos, Ram Swaminathan, Robert Endre Tarjan, Janet L. Wiener, Yunhong Zhou
2005	—	`conf`	What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTs. APPROX-RANDOM Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar
2004	A*	`conf`	Selfish caching in distributed systems: a game-theoretic analysis. PODC Byung-Gon Chun, Kamalika Chaudhuri, Hoeteck Wee, Marco Barreno, Christos H. Papadimitriou, John Kubiatowicz
2003	—	`conf`	An Extension of Scalable Global IP Anycasting for Load Balancing in the Internet. ICOIN Siddhartha Saha, Kamalika Chaudhuri, Dheeraj Sanghi
2003	B	`conf`	Location determination of a mobile device using IEEE 802.11b access point signals. WCNC Siddhartha Saha, Kamalika Chaudhuri, Dheeraj Sanghi, Pravin Bhagwat
2003	A*	`conf`	Paths, Trees, and Minimum Latency Tours. FOCS Kamalika Chaudhuri, Brighten Godfrey, Satish Rao, Kunal Talwar