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ORCIDKamalika Chaudhuri
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- affiliation: University of California San Diego, CA, USA
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2020 – today
- 2025
[i112]Konstantin Garov, Kamalika Chaudhuri:
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection. CoRR abs/2501.08821 (2025)- [i111]Chhavi Yadav, Evan Laufer, Dan Boneh, Kamalika Chaudhuri:
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs. CoRR abs/2502.03773 (2025) - [i110]Arman Zharmagambetov, Chuan Guo, Ivan Evtimov, Maya Pavlova, Ruslan Salakhutdinov, Kamalika Chaudhuri:
AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents. CoRR abs/2503.09780 (2025) - [i109]Narine Kokhlikyan, Bargav Jayaraman, Florian Bordes, Chuan Guo, Kamalika Chaudhuri:
Measuring Déjà vu Memorization Efficiently. CoRR abs/2504.05651 (2025) - [i108]Ivan Evtimov, Arman Zharmagambetov, Aaron Grattafiori, Chuan Guo, Kamalika Chaudhuri:
WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks. CoRR abs/2504.18575 (2025) - 2024
- [j16]Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri:
XAudit : A Learning-Theoretic Look at Auditing with Explanations. Trans. Mach. Learn. Res. 2024 (2024) - [c103]Jacob Imola
, Amrita Roy Chowdhury, Kamalika Chaudhuri:
Metric Differential Privacy at the User-Level via the Earth-Mover's Distance. CCS 2024: 348-362 - [c102]Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos:
Effective pruning of web-scale datasets based on complexity of concept clusters. ICLR 2024 - [c101]Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
Differentially Private Representation Learning via Image Captioning. ICML 2024 - [c100]Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri:
FairProof : Confidential and Certifiable Fairness for Neural Networks. ICML 2024 - [c99]Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
ViP: A Differentially Private Foundation Model for Computer Vision. ICML 2024 - [c98]Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar:
On Differentially Private U Statistics. NeurIPS 2024 - [c97]Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri:
Déjà Vu Memorization in Vision-Language Models. NeurIPS 2024 - [c96]Narine Kokhlikyan, Bargav Jayaraman, Florian Bordes, Chuan Guo, Kamalika Chaudhuri:
Measuring Dejavu Memorization Efficiently. NeurIPS 2024 - [c95]Nicholas Rittler, Kamalika Chaudhuri:
Distribution Learning with Valid Outputs Beyond the Worst-Case. NeurIPS 2024 - [c94]Tatsuki Koga, Kamalika Chaudhuri, David Page:
Differentially Private Multi-Site Treatment Effect Estimation. SaTML 2024: 472-489 - [c93]Zhifeng Kong, Kamalika Chaudhuri:
Data Redaction from Conditional Generative Models. SaTML 2024: 569-591 - [i107]Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos:
Effective pruning of web-scale datasets based on complexity of concept clusters. CoRR abs/2401.04578 (2024) - [i106]Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri:
Déjà Vu Memorization in Vision-Language Models. CoRR abs/2402.02103 (2024) - [i105]Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri:
Measuring Privacy Loss in Distributed Spatio-Temporal Data. CoRR abs/2402.11526 (2024) - [i104]Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri:
FairProof : Confidential and Certifiable Fairness for Neural Networks. CoRR abs/2402.12572 (2024) - [i103]Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
Differentially Private Representation Learning via Image Captioning. CoRR abs/2403.02506 (2024) - [i102]Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo:
Privacy Amplification for the Gaussian Mechanism via Bounded Support. CoRR abs/2403.05598 (2024) - [i101]Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero-Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo:
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning. CoRR abs/2403.14421 (2024) - [i100]Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert:
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds. CoRR abs/2404.02866 (2024) - [i99]Christian Tomani, Kamalika Chaudhuri, Ivan Evtimov, Daniel Cremers, Mark Ibrahim:
Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations. CoRR abs/2404.10960 (2024) - [i98]Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri:
Metric Differential Privacy at the User-Level. CoRR abs/2405.02665 (2024) - [i97]Ruihan Wu, Pengrun Huang, Kamalika Chaudhuri:
Better Membership Inference Privacy Measurement through Discrepancy. CoRR abs/2405.15140 (2024) - [i96]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:
An Introduction to Vision-Language Modeling. CoRR abs/2405.17247 (2024) - [i95]Robi Bhattacharjee, Nick Rittler, Kamalika Chaudhuri:
Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift. CoRR abs/2405.19156 (2024) - [i94]Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar:
On Differentially Private U Statistics. CoRR abs/2407.04945 (2024) - [i93]Chhavi Yadav, Ruihan Wu, Kamalika Chaudhuri:
Influence-based Attributions can be Manipulated. CoRR abs/2409.05208 (2024) - [i92]Sizhe Chen, Arman Zharmagambetov, Saeed Mahloujifar, Kamalika Chaudhuri, Chuan Guo:
Aligning LLMs to Be Robust Against Prompt Injection. CoRR abs/2410.05451 (2024) - [i91]Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov, Kamalika Chaudhuri:
Evaluating Deep Unlearning in Large Language Models. CoRR abs/2410.15153 (2024) - [i90]Nick Rittler, Kamalika Chaudhuri:
Distribution Learning with Valid Outputs Beyond the Worst-Case. CoRR abs/2410.16253 (2024) - [i89]Saeed Mahloujifar, Luca Melis, Kamalika Chaudhuri:
Auditing f-Differential Privacy in One Run. CoRR abs/2410.22235 (2024) - [i88]Tatsuki Koga, Ruihan Wu, Kamalika Chaudhuri:
Privacy-Preserving Retrieval Augmented Generation with Differential Privacy. CoRR abs/2412.04697 (2024) - 2023
- [j15]Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri:
Probing Predictions on OOD Images via Nearest Categories. Trans. Mach. Learn. Res. 2023 (2023) - [c92]Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri:
Robust Empirical Risk Minimization with Tolerance. ALT 2023: 182-203 - [c91]Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri:
Data-Copying in Generative Models: A Formal Framework. ICML 2023: 2364-2396 - [c90]Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz:
Why does Throwing Away Data Improve Worst-Group Error? ICML 2023: 4144-4188 - [c89]Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat:
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. ICML 2023: 11888-11904 - [c88]Nicholas Rittler, Kamalika Chaudhuri:
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors. ICML 2023: 29103-29129 - [c87]Casey Meehan, Florian Bordes, Pascal Vincent, Kamalika Chaudhuri, Chuan Guo:
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning. NeurIPS 2023 - [c86]Nicholas Rittler, Kamalika Chaudhuri:
Agnostic Multi-Group Active Learning. NeurIPS 2023 - [c85]Zhifeng Kong, Kamalika Chaudhuri:
Data Redaction from Pre-trained GANs. SaTML 2023: 638-677 - [i87]Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri:
Data-Copying in Generative Models: A Formal Framework. CoRR abs/2302.13181 (2023) - [i86]Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri:
Can Membership Inferencing be Refuted? CoRR abs/2303.03648 (2023) - [i85]Casey Meehan, Florian Bordes, Pascal Vincent, Kamalika Chaudhuri, Chuan Guo:
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning. CoRR abs/2304.13850 (2023) - [i84]Zhifeng Kong, Kamalika Chaudhuri:
Data Redaction from Conditional Generative Models. CoRR abs/2305.11351 (2023) - [i83]Nick Rittler, Kamalika Chaudhuri:
Agnostic Multi-Group Active Learning. CoRR abs/2306.01922 (2023) - [i82]Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
ViP: A Differentially Private Foundation Model for Computer Vision. CoRR abs/2306.08842 (2023) - [i81]Ruihan Wu, Chuan Guo, Kamalika Chaudhuri:
Large-Scale Public Data Improves Differentially Private Image Generation Quality. CoRR abs/2309.00008 (2023) - [i80]Kamalika Chaudhuri, David Lopez-Paz:
Unified Uncertainty Calibration. CoRR abs/2310.01202 (2023) - [i79]Tatsuki Koga, Kamalika Chaudhuri, David Page:
Differentially Private Multi-Site Treatment Effect Estimation. CoRR abs/2310.06237 (2023) - 2022
- [c84]Casey Meehan, Khalil Mrini, Kamalika Chaudhuri:
Sentence-level Privacy for Document Embeddings. ACL (1) 2022: 3367-3380 - [c83]Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri:
Privacy Amplification by Subsampling in Time Domain. AISTATS 2022: 4055-4069 - [c82]Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta:
Privacy Amplification via Shuffling for Linear Contextual Bandits. ALT 2022: 381-407 - [c81]Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri:
Forgeability and Membership Inference Attacks. AISec@CCS 2022: 25-31 - [c80]Jacob Imola, Takao Murakami, Kamalika Chaudhuri:
Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model. CCS 2022: 1505-1519 - [c79]Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha:
Privacy Implications of Shuffling. ICLR 2022 - [c78]Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten:
Bounding Training Data Reconstruction in Private (Deep) Learning. ICML 2022: 8056-8071 - [c77]Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri:
Thompson Sampling for Robust Transfer in Multi-Task Bandits. ICML 2022: 23363-23416 - [c76]Kamalika Chaudhuri, Chuan Guo, Mike Rabbat:
Privacy-aware compression for federated data analysis. UAI 2022: 296-306 - [c75]Jacob Imola, Takao Murakami, Kamalika Chaudhuri:
Communication-Efficient Triangle Counting under Local Differential Privacy. USENIX Security Symposium 2022: 537-554 - [e4]Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, Sivan Sabato:
International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research 162, PMLR 2022 [contents] - [i78]Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri:
Privacy Amplification by Subsampling in Time Domain. CoRR abs/2201.04762 (2022) - [i77]Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten:
Bounding Training Data Reconstruction in Private (Deep) Learning. CoRR abs/2201.12383 (2022) - [i76]Yao-Yuan Yang, Kamalika Chaudhuri:
Understanding Rare Spurious Correlations in Neural Networks. CoRR abs/2202.05189 (2022) - [i75]Kamalika Chaudhuri, Chuan Guo, Mike Rabbat:
Privacy-Aware Compression for Federated Data Analysis. CoRR abs/2203.08134 (2022) - [i74]Jacob Imola, Takao Murakami, Kamalika Chaudhuri:
Differentially Private Subgraph Counting in the Shuffle Model. CoRR abs/2205.01429 (2022) - [i73]Casey Meehan, Khalil Mrini, Kamalika Chaudhuri:
Sentence-level Privacy for Document Embeddings. CoRR abs/2205.04605 (2022) - [i72]Martín Arjovsky, Kamalika Chaudhuri, David Lopez-Paz:
Throwing Away Data Improves Worst-Class Error in Imbalanced Classification. CoRR abs/2205.11672 (2022) - [i71]Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri:
A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models. CoRR abs/2206.04740 (2022) - [i70]Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri:
Thompson Sampling for Robust Transfer in Multi-Task Bandits. CoRR abs/2206.08556 (2022) - [i69]Zhifeng Kong, Kamalika Chaudhuri:
Forgetting Data from Pre-trained GANs. CoRR abs/2206.14389 (2022) - [i68]Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri:
Robust Empirical Risk Minimization with Tolerance. CoRR abs/2210.00635 (2022) - [i67]Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri:
Robustness of Locally Differentially Private Graph Analysis Against Poisoning. CoRR abs/2210.14376 (2022) - [i66]Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat:
The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning. CoRR abs/2211.03942 (2022) - [i65]Nick Rittler, Kamalika Chaudhuri:
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors. CoRR abs/2211.10773 (2022) - 2021
- [j14]Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. J. Mach. Learn. Res. 22: 262:1-262:44 (2021) - [c74]Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. AISTATS 2021: 838-846 - [c73]Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri:
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation. AISTATS 2021: 1531-1539 - [c72]Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou:
Approximate Data Deletion from Machine Learning Models. AISTATS 2021: 2008-2016 - [c71]Casey Meehan, Kamalika Chaudhuri:
Location Trace Privacy Under Conditional Priors. AISTATS 2021: 2881-2889 - [c70]Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri:
Sample Complexity of Robust Linear Classification on Separated Data. ICML 2021: 884-893 - [c69]Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri:
Connecting Interpretability and Robustness in Decision Trees through Separation. ICML 2021: 7839-7849 - [c68]Zhifeng Kong, Kamalika Chaudhuri:
Understanding Instance-based Interpretability of Variational Auto-Encoders. NeurIPS 2021: 2400-2412 - [c67]Robi Bhattacharjee, Kamalika Chaudhuri:
Consistent Non-Parametric Methods for Maximizing Robustness. NeurIPS 2021: 9036-9048 - [c66]Chhavi Yadav, Kamalika Chaudhuri:
Behavior of k-NN as an Instance-Based Explanation Method. PKDD/ECML Workshops (1) 2021: 90-96 - [c65]Jacob Imola, Takao Murakami, Kamalika Chaudhuri:
Locally Differentially Private Analysis of Graph Statistics. USENIX Security Symposium 2021: 983-1000 - [i64]Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri:
Connecting Interpretability and Robustness in Decision Trees through Separation. CoRR abs/2102.07048 (2021) - [i63]Robi Bhattacharjee, Kamalika Chaudhuri:
Consistent Non-Parametric Methods for Adaptive Robustness. CoRR abs/2102.09086 (2021) - [i62]Casey Meehan, Kamalika Chaudhuri:
Location Trace Privacy Under Conditional Priors. CoRR abs/2102.11955 (2021) - [i61]Zhifeng Kong, Kamalika Chaudhuri:
Universal Approximation of Residual Flows in Maximum Mean Discrepancy. CoRR abs/2103.05793 (2021) - [i60]Jacob Imola, Kamalika Chaudhuri:
Privacy Amplification Via Bernoulli Sampling. CoRR abs/2105.10594 (2021) - [i59]Zhifeng Kong, Kamalika Chaudhuri:
Understanding Instance-based Interpretability of Variational Auto-Encoders. CoRR abs/2105.14203 (2021) - [i58]Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha:
A Shuffling Framework for Local Differential Privacy. CoRR abs/2106.06603 (2021) - [i57]Chhavi Yadav, Kamalika Chaudhuri:
Behavior of k-NN as an Instance-Based Explanation Method. CoRR abs/2109.06999 (2021) - [i56]Jacob Imola, Takao Murakami, Kamalika Chaudhuri:
Communication-Efficient Triangle Counting under Local Differential Privacy. CoRR abs/2110.06485 (2021) - [i55]Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta:
Privacy Amplification via Shuffling for Linear Contextual Bandits. CoRR abs/2112.06008 (2021) - 2020
- [j13]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Variational Bayes In Private Settings (VIPS). J. Artif. Intell. Res. 68: 109-157 (2020) - [j12]Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi:
Successive Refinement of Privacy. IEEE J. Sel. Areas Inf. Theory 1(3): 745-759 (2020) - [c64]Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri:
Robustness for Non-Parametric Classification: A Generic Attack and Defense. AISTATS 2020: 941-951 - [c63]Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta:
A Three Sample Hypothesis Test for Evaluating Generative Models. AISTATS 2020: 3546-3556 - [c62]Zhifeng Kong, Kamalika Chaudhuri:
The Expressive Power of a Class of Normalizing Flow Models. AISTATS 2020: 3599-3609 - [c61]Robi Bhattacharjee, Kamalika Chaudhuri:
When are Non-Parametric Methods Robust? ICML 2020: 832-841 - [c60]James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling:
Variational Bayes in Private Settings (VIPS) (Extended Abstract). IJCAI 2020: 5050-5054 - [c59]Yunhui Guo, Xiaofan Yu, Kamalika Chaudhuri, Tajana Rosing:
Efficient Distributed Training in Heterogeneous Mobile Networks with Active Sampling. MSN 2020: 174-181 - [c58]Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri:
A Closer Look at Accuracy vs. Robustness. NeurIPS 2020 - [c57]Benjamin Cosman, Madeline Endres, Georgios Sakkas, Leon Medvinsky, Yao-Yuan Yang, Ranjit Jhala, Kamalika Chaudhuri, Westley Weimer:
PABLO: Helping Novices Debug Python Code Through Data-Driven Fault Localization. SIGCSE 2020: 1047-1053 - [c56]Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan:
Exploring Connections Between Active Learning and Model Extraction. USENIX Security Symposium 2020: 1309-1326 - [i54]Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Y. Zou:
Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations. CoRR abs/2002.10077 (2020) - [i53]Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri:
Adversarial Robustness Through Local Lipschitzness. CoRR abs/2003.02460 (2020) - [i52]Robi Bhattacharjee, Kamalika Chaudhuri:
When are Non-Parametric Methods Robust? CoRR abs/2003.06121 (2020) - [i51]Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta:
A Non-Parametric Test to Detect Data-Copying in Generative Models. CoRR abs/2004.05675 (2020) - [i50]Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi:
Successive Refinement of Privacy. CoRR abs/2005.11651 (2020) - [i49]Zhifeng Kong, Kamalika Chaudhuri:
The Expressive Power of a Class of Normalizing Flow Models. CoRR abs/2006.00392 (2020) - [i48]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:
Trustworthy AI Inference Systems: An Industry Research View. CoRR abs/2008.04449 (2020) - [i47]Jacob Imola, Takao Murakami, Kamalika Chaudhuri:
Locally Differentially Private Analysis of Graph Statistics. CoRR abs/2010.08688 (2020) - [i46]Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri:
Multitask Bandit Learning through Heterogeneous Feedback Aggregation. CoRR abs/2010.15390 (2020) - [i45]Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. CoRR abs/2011.03186 (2020) - [i44]Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri:
Close Category Generalization. CoRR abs/2011.08485 (2020) - [i43]Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri:
Sample Complexity of Adversarially Robust Linear Classification on Separated Data. CoRR abs/2012.10794 (2020)
2010 – 2019
- 2019
- [c55]Joseph Geumlek, Kamalika Chaudhuri:
Profile-based Privacy for Locally Private Computations. ISIT 2019: 537-541 - [c54]Songbai Yan, Kamalika Chaudhuri, Tara Javidi:
The Label Complexity of Active Learning from Observational Data. NeurIPS 2019: 1808-1817 - [c53]Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala:
Capacity Bounded Differential Privacy. NeurIPS 2019: 3469-3478 - [e3]Kamalika Chaudhuri, Masashi Sugiyama:
The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 16-18 April 2019, Naha, Okinawa, Japan. Proceedings of Machine Learning Research 89, PMLR 2019 [contents] - [e2]Kamalika Chaudhuri, Ruslan Salakhutdinov:
Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research 97, PMLR 2019 [contents] - [i42]Joseph Geumlek, Kamalika Chaudhuri:
Profile-Based Privacy for Locally Private Computations. CoRR abs/1903.09084 (2019) - [i41]Yizhen Wang, Kamalika Chaudhuri:
An Investigation of Data Poisoning Defenses for Online Learning. CoRR abs/1905.12121 (2019) - [i40]Songbai Yan, Kamalika Chaudhuri, Tara Javidi:
The Label Complexity of Active Learning from Observational Data. CoRR abs/1905.12791 (2019) - [i39]Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri:
Adversarial Examples for Non-Parametric Methods: Attacks, Defenses and Large Sample Limits. CoRR abs/1906.03310 (2019) - [i38]Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala:
Capacity Bounded Differential Privacy. CoRR abs/1907.02159 (2019) - [i37]Casey Meehan, Kamalika Chaudhuri:
Location Trace Privacy Under Conditional Priors. CoRR abs/1912.04228 (2019) - 2018
- [j11]Kamalika Chaudhuri, Claudio Gentile:
Special Issue on ALT 2015: Guest Editors' Introduction. Theor. Comput. Sci. 716: 1-3 (2018) - [c52]Yizhen Wang, Somesh Jha, Kamalika Chaudhuri:
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples. ICML 2018: 5120-5129 - [c51]Songbai Yan, Kamalika Chaudhuri, Tara Javidi:
Active Learning with Logged Data. ICML 2018: 5517-5526 - [i36]Chicheng Zhang, Eran A. Mukamel, Kamalika Chaudhuri:
Spectral Learning of Binomial HMMs for DNA Methylation Data. CoRR abs/1802.02498 (2018) - [i35]Songbai Yan, Kamalika Chaudhuri, Tara Javidi:
Active Learning with Logged Data. CoRR abs/1802.09069 (2018) - [i34]Yizhen Wang, Kamalika Chaudhuri:
Data Poisoning Attacks against Online Learning. CoRR abs/1808.08994 (2018) - [i33]Shuang Song, Susan Little, Sanjay Mehta, Staal Amund Vinterbo, Kamalika Chaudhuri:
Differentially Private Continual Release of Graph Statistics. CoRR abs/1809.02575 (2018) - [i32]Shuang Liu, Kamalika Chaudhuri:
The Inductive Bias of Restricted f-GANs. CoRR abs/1809.04542 (2018) - [i31]Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan:
Model Extraction and Active Learning. CoRR abs/1811.02054 (2018) - 2017
- [j10]Eric L. Seidel, Huma Sibghat, Kamalika Chaudhuri, Westley Weimer, Ranjit Jhala:
Learning to blame: localizing novice type errors with data-driven diagnosis. Proc. ACM Program. Lang. 1(OOPSLA): 60:1-60:27 (2017) - [c50]Shuang Song, Kamalika Chaudhuri:
Composition properties of inferential privacy for time-series data. Allerton 2017: 814-821 - [c49]Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan:
Active Heteroscedastic Regression. ICML 2017: 694-702 - [c48]Joseph Geumlek, Shuang Song, Kamalika Chaudhuri:
Renyi Differential Privacy Mechanisms for Posterior Sampling. NIPS 2017: 5289-5298 - [c47]Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri:
Approximation and Convergence Properties of Generative Adversarial Learning. NIPS 2017: 5545-5553 - [c46]Shuang Song, Yizhen Wang, Kamalika Chaudhuri:
Pufferfish Privacy Mechanisms for Correlated Data. SIGMOD Conference 2017: 1291-1306 - [c45]Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton:
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics. SIGMOD Conference 2017: 1307-1322 - [i30]Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri:
Approximation and Convergence Properties of Generative Adversarial Learning. CoRR abs/1705.08991 (2017) - [i29]Yizhen Wang, Somesh Jha, Kamalika Chaudhuri:
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples. CoRR abs/1706.03922 (2017) - [i28]Shuang Song, Kamalika Chaudhuri:
Composition Properties of Inferential Privacy for Time-Series Data. CoRR abs/1707.02702 (2017) - [i27]Eric L. Seidel, Huma Sibghat, Kamalika Chaudhuri, Westley Weimer, Ranjit Jhala:
Learning to Blame: Localizing Novice Type Errors with Data-Driven Diagnosis. CoRR abs/1708.07583 (2017) - [i26]Joseph Geumlek, Shuang Song, Kamalika Chaudhuri:
Rényi Differential Privacy Mechanisms for Posterior Sampling. CoRR abs/1710.00892 (2017) - 2016
- [c44]Chicheng Zhang, Kamalika Chaudhuri:
The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions. COLT 2016: 1584-1616 - [c43]Songbai Yan, Kamalika Chaudhuri, Tara Javidi:
Active Learning from Imperfect Labelers. NIPS 2016: 2128-2136 - [c42]James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri:
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. UAI 2016 - [i25]Julian Yarkony, Kamalika Chaudhuri:
Convex Optimization For Non-Convex Problems via Column Generation. CoRR abs/1602.04409 (2016) - [i24]Yizhen Wang, Shuang Song, Kamalika Chaudhuri:
Privacy-preserving Analysis of Correlated Data. CoRR abs/1603.03977 (2016) - [i23]James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri:
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. CoRR abs/1603.07294 (2016) - [i22]Chicheng Zhang, Kamalika Chaudhuri:
The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions. CoRR abs/1604.06162 (2016) - [i21]Mijung Park, Jimmy Foulds, Kamalika Chaudhuri, Max Welling:
Practical Privacy For Expectation Maximization. CoRR abs/1605.06995 (2016) - [i20]Xi Wu, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton:
Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics. CoRR abs/1606.04722 (2016) - [i19]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Private Topic Modeling. CoRR abs/1609.04120 (2016) - [i18]Songbai Yan, Kamalika Chaudhuri, Tara Javidi:
Active Learning from Imperfect Labelers. CoRR abs/1610.09730 (2016) - [i17]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Variational Bayes In Private Settings (VIPS). CoRR abs/1611.00340 (2016) - 2015
- [j9]Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri:
Bayesian Active Learning With Non-Persistent Noise. IEEE Trans. Inf. Theory 61(7): 4080-4098 (2015) - [c41]Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate:
Learning from Data with Heterogeneous Noise using SGD. AISTATS 2015 - [c40]Songbai Yan, Kamalika Chaudhuri, Tara Javidi:
Active learning from noisy and abstention feedback. Allerton 2015: 1352-1357 - [c39]James Y. Zou, Kamalika Chaudhuri, Adam Tauman Kalai:
Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons. HCOMP 2015: 198-205 - [c38]Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin C. Chen:
Spectral Learning of Large Structured HMMs for Comparative Epigenomics. NIPS 2015: 469-477 - [c37]Chicheng Zhang, Kamalika Chaudhuri:
Active Learning from Weak and Strong Labelers. NIPS 2015: 703-711 - [c36]Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi:
Convergence Rates of Active Learning for Maximum Likelihood Estimation. NIPS 2015: 1090-1098 - [e1]Kamalika Chaudhuri, Claudio Gentile, Sandra Zilles:
Algorithmic Learning Theory - 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings. Lecture Notes in Computer Science 9355, Springer 2015, ISBN 978-3-319-24485-3 [contents] - [i16]James Y. Zou, Kamalika Chaudhuri, Adam Tauman Kalai:
Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons. CoRR abs/1504.00064 (2015) - [i15]Chicheng Zhang, Jimin Song, Kevin C. Chen, Kamalika Chaudhuri:
Spectral Learning of Large Structured HMMs for Comparative Epigenomics. CoRR abs/1506.01744 (2015) - [i14]Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi:
Convergence Rates of Active Learning for Maximum Likelihood Estimation. CoRR abs/1506.02348 (2015) - [i13]Chicheng Zhang, Kamalika Chaudhuri:
Active Learning from Weak and Strong Labelers. CoRR abs/1510.02847 (2015) - 2014
- [j8]Kamalika Chaudhuri, Sanjoy Dasgupta, Samory Kpotufe, Ulrike von Luxburg:
Consistent Procedures for Cluster Tree Estimation and Pruning. IEEE Trans. Inf. Theory 60(12): 7900-7912 (2014) - [c35]Chicheng Zhang, Kamalika Chaudhuri:
Beyond Disagreement-Based Agnostic Active Learning. NIPS 2014: 442-450 - [c34]Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song:
The Large Margin Mechanism for Differentially Private Maximization. NIPS 2014: 1287-1295 - [c33]Kamalika Chaudhuri, Sanjoy Dasgupta:
Rates of Convergence for Nearest Neighbor Classification. NIPS 2014: 3437-3445 - [i12]Kamalika Chaudhuri, Sanjoy Dasgupta:
Rates of Convergence for Nearest Neighbor Classification. CoRR abs/1407.0067 (2014) - [i11]Chicheng Zhang, Kamalika Chaudhuri:
Beyond Disagreement-based Agnostic Active Learning. CoRR abs/1407.2657 (2014) - [i10]Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song:
The Large Margin Mechanism for Differentially Private Maximization. CoRR abs/1409.2177 (2014) - [i9]Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate:
Learning from Data with Heterogeneous Noise using SGD. CoRR abs/1412.5617 (2014) - 2013
- [j7]Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha:
A near-optimal algorithm for differentially-private principal components. J. Mach. Learn. Res. 14(1): 2905-2943 (2013) - [j6]Anand D. Sarwate, Kamalika Chaudhuri:
Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data. IEEE Signal Process. Mag. 30(5): 86-94 (2013) - [c32]Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri:
Extrinsic Jensen-Shannon divergence and noisy Bayesian active learning. Allerton 2013: 1128-1135 - [c31]Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate:
Stochastic gradient descent with differentially private updates. GlobalSIP 2013: 245-248 - [c30]Kamalika Chaudhuri, Staal Amund Vinterbo:
A Stability-based Validation Procedure for Differentially Private Machine Learning. NIPS 2013: 2652-2660 - [i8]Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri:
Noisy Bayesian Active Learning. CoRR abs/1312.2315 (2013) - 2012
- [j5]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:
iDASH: integrating data for analysis, anonymization, and sharing. J. Am. Medical Informatics Assoc. 19(2): 196-201 (2012) - [c29]Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri:
Noisy Bayesian active learning. Allerton Conference 2012: 1626-1633 - [c28]Kamalika Chaudhuri, Daniel J. Hsu:
Convergence Rates for Differentially Private Statistical Estimation. ICML 2012 - [c27]Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha:
Near-optimal Differentially Private Principal Components. NIPS 2012: 998-1006 - [c26]Kamalika Chaudhuri, Fan Chung Graham, Alexander Tsiatas:
Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model. COLT 2012: 35.1-35.23 - [i7]Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu:
An Online Learning-based Framework for Tracking. CoRR abs/1203.3471 (2012) - [i6]Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha:
Near-Optimal Algorithms for Differentially-Private Principal Components. CoRR abs/1207.2812 (2012) - 2011
- [j4]Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate:
Differentially Private Empirical Risk Minimization. J. Mach. Learn. Res. 12: 1069-1109 (2011) - [c25]Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang:
Spectral Methods for Learning Multivariate Latent Tree Structure. NIPS 2011: 2025-2033 - [c24]Kamalika Chaudhuri, Daniel J. Hsu:
Sample Complexity Bounds for Differentially Private Learning. COLT 2011: 155-186 - [i5]Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang:
Spectral Methods for Learning Multivariate Latent Tree Structure. CoRR abs/1107.1283 (2011) - 2010
- [c23]Kamalika Chaudhuri, Sanjoy Dasgupta:
Rates of convergence for the cluster tree. NIPS 2010: 343-351 - [c22]Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu:
An Online Learning-based Framework for Tracking. UAI 2010: 101-108
2000 – 2009
- 2009
- [j3]Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar:
What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTs. Algorithmica 55(1): 157-189 (2009) - [j2]Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar:
A push-relabel approximation algorithm for approximating the minimum-degree MST problem and its generalization to matroids. Theor. Comput. Sci. 410(44): 4489-4503 (2009) - [c21]Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan:
Multi-view clustering via canonical correlation analysis. ICML 2009: 129-136 - [c20]Kamalika Chaudhuri, Constantinos Daskalakis, Robert D. Kleinberg, Henry Lin:
Online Bipartite Perfect Matching With Augmentations. INFOCOM 2009: 1044-1052 - [c19]Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu:
A Parameter-free Hedging Algorithm. NIPS 2009: 297-305 - [i4]Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu:
A parameter-free hedging algorithm. CoRR abs/0903.2851 (2009) - [i3]Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu:
Tracking using explanation-based modeling. CoRR abs/0903.2862 (2009) - [i2]Anand D. Sarwate, Kamalika Chaudhuri, Claire Monteleoni:
Differentially Private Support Vector Machines. CoRR abs/0912.0071 (2009) - [i1]Kamalika Chaudhuri, Sanjoy Dasgupta, Andrea Vattani:
Learning Mixtures of Gaussians using the k-means Algorithm. CoRR abs/0912.0086 (2009) - 2008
- [c18]Kamalika Chaudhuri, Satish Rao:
Learning Mixtures of Product Distributions Using Correlations and Independence. COLT 2008: 9-20 - [c17]Kamalika Chaudhuri, Satish Rao:
Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed Distributions. COLT 2008: 21-32 - [c16]Kamalika Chaudhuri, Andrew McGregor:
Finding Metric Structure in Information Theoretic Clustering. COLT 2008: 391-402 - [c15]Kamalika Chaudhuri, Claire Monteleoni:
Privacy-preserving logistic regression. NIPS 2008: 289-296 - [c14]Kamalika Chaudhuri, Fan Chung Graham, Mohammad Shoaib Jamall:
A Network Coloring Game. WINE 2008: 522-530 - 2007
- [j1]Kamalika Chaudhuri, Anshul Kothari, Rudi Pendavingh, Ram Swaminathan, Robert Endre Tarjan, Yunhong Zhou:
Server Allocation Algorithms for Tiered Systems. Algorithmica 48(2): 129-146 (2007) - [c13]Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, Kunal Talwar:
Privacy, accuracy, and consistency too: a holistic solution to contingency table release. PODS 2007: 273-282 - [c12]Kamalika Chaudhuri, Eran Halperin, Satish Rao, Shuheng Zhou:
A rigorous analysis of population stratification with limited data. SODA 2007: 1046-1055 - 2006
- [c11]Kamalika Chaudhuri, Nina Mishra:
When Random Sampling Preserves Privacy. CRYPTO 2006: 198-213 - [c10]Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar:
A Push-Relabel Algorithm for Approximating Degree Bounded MSTs. ICALP (1) 2006: 191-201 - [c9]Kamalika Chaudhuri, Kevin C. Chen, Radu Mihaescu, Satish Rao:
On the tandem duplication-random loss model of genome rearrangement. SODA 2006: 564-570 - 2005
- [c8]Kamalika Chaudhuri, Satish Rao, Samantha J. Riesenfeld, Kunal Talwar:
What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTs. APPROX-RANDOM 2005: 26-39 - [c7]Kamalika Chaudhuri, Anshul Kothari, Rudi Pendavingh, Ram Swaminathan, Robert Endre Tarjan, Yunhong Zhou:
Server Allocation Algorithms for Tiered Systems. COCOON 2005: 632-643 - [c6]Eric Anderson, Dirk Beyer, Kamalika Chaudhuri, Terence Kelly, Norman Salazar, Cipriano A. Santos, Ram Swaminathan, Robert Endre Tarjan, Janet L. Wiener, Yunhong Zhou:
Deadline scheduling for animation rendering. SIGMETRICS 2005: 384-385 - [c5]Eric Anderson, Dirk Beyer, Kamalika Chaudhuri, Terence Kelly, Norman Salazar, Cipriano A. Santos, Ram Swaminathan, Robert Endre Tarjan, Janet L. Wiener, Yunhong Zhou:
Value-maximizing deadline scheduling and its application to animation rendering. SPAA 2005: 299-308 - 2004
- [c4]Byung-Gon Chun, Kamalika Chaudhuri, Hoeteck Wee, Marco Barreno, Christos H. Papadimitriou, John Kubiatowicz:
Selfish caching in distributed systems: a game-theoretic analysis. PODC 2004: 21-30 - 2003
- [c3]Kamalika Chaudhuri, Brighten Godfrey, Satish Rao, Kunal Talwar:
Paths, Trees, and Minimum Latency Tours. FOCS 2003: 36-45 - [c2]Siddhartha Saha, Kamalika Chaudhuri, Dheeraj Sanghi:
An Extension of Scalable Global IP Anycasting for Load Balancing in the Internet. ICOIN 2003: 161-170 - [c1]Siddhartha Saha, Kamalika Chaudhuri, Dheeraj Sanghi, Pravin Bhagwat:
Location determination of a mobile device using IEEE 802.11b access point signals. WCNC 2003: 1987-1992
Coauthor Index
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