Kamalika Chaudhuri

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 146

Top Venues

CoRR

ICML

NIPS

AISTATS

Publications

Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri
Metric Differential Privacy at the User-Level. CoRR (2024)
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 (2024)
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 (2024)
Robi Bhattacharjee, Nick Rittler, Kamalika Chaudhuri
Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift. CoRR (2024)
Zhifeng Kong, Kamalika Chaudhuri
Data Redaction from Conditional Generative Models. SaTML (2024)
Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri
Déjà Vu Memorization in Vision-Language Models. CoRR (2024)
Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri
FairProof : Confidential and Certifiable Fairness for Neural Networks. CoRR (2024)
Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds. CoRR (2024)
Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo
Differentially Private Representation Learning via Image Captioning. CoRR (2024)
Christian Tomani, Kamalika Chaudhuri, Ivan Evtimov, Daniel Cremers, Mark Ibrahim
Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations. CoRR (2024)
Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo
Privacy Amplification for the Gaussian Mechanism via Bounded Support. CoRR (2024)
Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri
XAudit : A Learning-Theoretic Look at Auditing with Explanations. Trans. Mach. Learn. Res. 2024 (2024)
Tatsuki Koga, Kamalika Chaudhuri, David Page
Differentially Private Multi-Site Treatment Effect Estimation. SaTML (2024)
Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar
On Differentially Private U Statistics. CoRR (2024)
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 (2024)
Ruihan Wu, Pengrun Huang, Kamalika Chaudhuri
Better Membership Inference Privacy Measurement through Discrepancy. CoRR (2024)
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)
Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
Measuring Privacy Loss in Distributed Spatio-Temporal Data. CoRR (2024)
Tatsuki Koga, Kamalika Chaudhuri, David Page
Differentially Private Multi-Site Treatment Effect Estimation. CoRR (2023)
Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. ICML (2023)
Zhifeng Kong, Kamalika Chaudhuri
Data Redaction from Pre-trained GANs. SaTML (2023)
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 (2023)
Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo
ViP: A Differentially Private Foundation Model for Computer Vision. CoRR (2023)
Nicholas Rittler, Kamalika Chaudhuri
Agnostic Multi-Group Active Learning. NeurIPS (2023)
Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri
Can Membership Inferencing be Refuted? CoRR (2023)
Zhifeng Kong, Kamalika Chaudhuri
Data Redaction from Conditional Generative Models. CoRR (2023)
Kamalika Chaudhuri, David Lopez-Paz
Unified Uncertainty Calibration. CoRR (2023)
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)
Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri
Robust Empirical Risk Minimization with Tolerance. ALT (2023)
Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz
Why does Throwing Away Data Improve Worst-Group Error? ICML (2023)
Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri
Data-Copying in Generative Models: A Formal Framework. CoRR (2023)
Nick Rittler, Kamalika Chaudhuri
Agnostic Multi-Group Active Learning. CoRR (2023)
Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri
Probing Predictions on OOD Images via Nearest Categories. Trans. Mach. Learn. Res. 2023 (2023)
Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri
Data-Copying in Generative Models: A Formal Framework. ICML (2023)
Nicholas Rittler, Kamalika Chaudhuri
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors. ICML (2023)
Ruihan Wu, Chuan Guo, Kamalika Chaudhuri
Large-Scale Public Data Improves Differentially Private Image Generation Quality. CoRR (2023)
Martín Arjovsky, Kamalika Chaudhuri, David Lopez-Paz
Throwing Away Data Improves Worst-Class Error in Imbalanced Classification. CoRR (2022)
Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
Privacy-aware compression for federated data analysis. UAI (2022)
Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri
Forgeability and Membership Inference Attacks. AISec@CCS (2022)
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Differentially Private Subgraph Counting in the Shuffle Model. CoRR (2022)
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Communication-Efficient Triangle Counting under Local Differential Privacy. USENIX Security Symposium (2022)
Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri
A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models. CoRR (2022)
Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri
Thompson Sampling for Robust Transfer in Multi-Task Bandits. ICML (2022)
Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri
Robustness of Locally Differentially Private Graph Analysis Against Poisoning. CoRR (2022)
Casey Meehan, Khalil Mrini, Kamalika Chaudhuri
Sentence-level Privacy for Document Embeddings. ACL (1) (2022)
Casey Meehan, Khalil Mrini, Kamalika Chaudhuri
Sentence-level Privacy for Document Embeddings. CoRR (2022)
Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta
Privacy Amplification via Shuffling for Linear Contextual Bandits. ALT (2022)
Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha
Privacy Implications of Shuffling. ICLR (2022)
Nick Rittler, Kamalika Chaudhuri
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors. CoRR (2022)
Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten
Bounding Training Data Reconstruction in Private (Deep) Learning. ICML (2022)
Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri
Robust Empirical Risk Minimization with Tolerance. CoRR (2022)
Zhifeng Kong, Kamalika Chaudhuri
Forgetting Data from Pre-trained GANs. CoRR (2022)
Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
Privacy-Aware Compression for Federated Data Analysis. CoRR (2022)
Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten
Bounding Training Data Reconstruction in Private (Deep) Learning. CoRR (2022)
Yao-Yuan Yang, Kamalika Chaudhuri
Understanding Rare Spurious Correlations in Neural Networks. CoRR (2022)
Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri
Thompson Sampling for Robust Transfer in Multi-Task Bandits. CoRR (2022)
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model. CCS (2022)
Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
Privacy Amplification by Subsampling in Time Domain. AISTATS (2022)
Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat
The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning. CoRR (2022)
Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
Privacy Amplification by Subsampling in Time Domain. CoRR (2022)
Robi Bhattacharjee, Kamalika Chaudhuri
Consistent Non-Parametric Methods for Maximizing Robustness. NeurIPS (2021)
Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
Connecting Interpretability and Robustness in Decision Trees through Separation. CoRR (2021)
Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta
Privacy Amplification via Shuffling for Linear Contextual Bandits. CoRR (2021)
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou
Approximate Data Deletion from Machine Learning Models. AISTATS (2021)
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Communication-Efficient Triangle Counting under Local Differential Privacy. CoRR (2021)
Zhifeng Kong, Kamalika Chaudhuri
Universal Approximation of Residual Flows in Maximum Mean Discrepancy. CoRR (2021)
Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation. AISTATS (2021)
Jacob Imola, Kamalika Chaudhuri
Privacy Amplification Via Bernoulli Sampling. CoRR (2021)
Chhavi Yadav, Kamalika Chaudhuri
Behavior of k-NN as an Instance-Based Explanation Method. PKDD/ECML Workshops (1) (2021)
Casey Meehan, Kamalika Chaudhuri
Location Trace Privacy Under Conditional Priors. AISTATS (2021)
Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
Connecting Interpretability and Robustness in Decision Trees through Separation. ICML (2021)
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. J. Mach. Learn. Res. 22 (2021)
Chhavi Yadav, Kamalika Chaudhuri
Behavior of k-NN as an Instance-Based Explanation Method. CoRR (2021)
Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri
Sample Complexity of Robust Linear Classification on Separated Data. ICML (2021)
Zhifeng Kong, Kamalika Chaudhuri
Understanding Instance-based Interpretability of Variational Auto-Encoders. CoRR (2021)
Zhifeng Kong, Kamalika Chaudhuri
Understanding Instance-based Interpretability of Variational Auto-Encoders. NeurIPS (2021)
Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri, Somesh Jha
A Shuffling Framework for Local Differential Privacy. CoRR (2021)
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Locally Differentially Private Analysis of Graph Statistics. USENIX Security Symposium (2021)
Casey Meehan, Kamalika Chaudhuri
Location Trace Privacy Under Conditional Priors. CoRR (2021)
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. AISTATS (2021)
Robi Bhattacharjee, Kamalika Chaudhuri
Consistent Non-Parametric Methods for Adaptive Robustness. CoRR (2021)
James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling
Variational Bayes in Private Settings (VIPS) (Extended Abstract). IJCAI (2020)
Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri
Sample Complexity of Adversarially Robust Linear Classification on Separated Data. CoRR (2020)
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. CoRR (2020)
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 (2020)
Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Russ R. Salakhutdinov, Kamalika Chaudhuri
A Closer Look at Accuracy vs. Robustness. NeurIPS (2020)
Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi
Successive Refinement of Privacy. CoRR (2020)
Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi
Successive Refinement of Privacy. IEEE J. Sel. Areas Inf. Theory 1 (3) (2020)
Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri
Multitask Bandit Learning through Heterogeneous Feedback Aggregation. CoRR (2020)
Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri
Close Category Generalization. CoRR (2020)
Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling
Variational Bayes In Private Settings (VIPS). J. Artif. Intell. Res. 68 (2020)
Yunhui Guo, Xiaofan Yu, Kamalika Chaudhuri, Tajana Rosing
Efficient Distributed Training in Heterogeneous Mobile Networks with Active Sampling. MSN (2020)
Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta
A Non-Parametric Test to Detect Data-Copying in Generative Models. CoRR (2020)
Zhifeng Kong, Kamalika Chaudhuri
The Expressive Power of a Class of Normalizing Flow Models. CoRR (2020)
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Locally Differentially Private Analysis of Graph Statistics. CoRR (2020)
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan
Exploring Connections Between Active Learning and Model Extraction. USENIX Security Symposium (2020)
Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri
Robustness for Non-Parametric Classification: A Generic Attack and Defense. AISTATS (2020)
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)
Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta
A Three Sample Hypothesis Test for Evaluating Generative Models. AISTATS (2020)
Zhifeng Kong, Kamalika Chaudhuri
The Expressive Power of a Class of Normalizing Flow Models. AISTATS (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Kamalika Chaudhuri