← Authors

Kamalika Chaudhuri

65 papers · 2008–2025 · 10 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (20) 🧭 Keyword Pioneer 🌍 Conference Polyglot (10) 🐝 Cross-Pollinator (11) 🌈 Renaissance Researcher (9) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (17) 🐣 Hot Topic Early Bird 🏠 Conference Loyalist (26) 🌟 Keyword Trendsetter Combo (3) πŸ‘‘ Domain Dominant (3) πŸ† Keyword Champion (2) 🌱 Topic Pioneer πŸ”¬ Deep Specialist (23) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (84) ⚑ Prolific Year (9) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter ❓ The Questioner (3) πŸ’Ž Century Club (65) πŸ”₯ Unstoppable (18)

Conferences

NIPS (26) ICML (17) AISTATS (9) COLT (3) JMLR (3) ALT (2) ICLR (2) ACL (1) IJCAI (1) UAI (1)

Top co-authors

Chuan Guo (8) Chicheng Zhang (6) Casey Meehan (6) Robi Bhattacharjee (5) Sanjoy Dasgupta (4) Tara Javidi (3) Yao-Yuan Yang (3) Somesh Jha (3) Daniel J. Hsu (3) Songbai Yan (3)

Research topics

Differential Privacy (9) Statistics (1)

Keywords

differential privacy (19) active learning (10) sample complexity (7) label complexity (6) adversarial robustness (6) privacy-preserving learning (4) convergence rate (4) generative model (4) robust classification (4) regret bound (4) online learning (4) privacy amplification (3) non-parametric method (3) nearest neighbor (3) logistic regression (3) machine learning (3) statistical learning (3) multi-armed bandit (3) bayes optimal classifier (3) empirical risk minimization (2)

Papers

Auditing $f$-differential privacy in one run ICML 2025
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs ICML 2025
ViP: A Differentially Private Foundation Model for Computer Vision ICML 2024
FairProof : Confidential and Certifiable Fairness for Neural Networks ICML 2024
Differentially Private Representation Learning via Image Captioning ICML 2024
Measuring Dejavu Memorization Efficiently NIPS 2024
DΓ©jΓ  Vu Memorization in Vision–Language Models NIPS 2024
Effective pruning of web-scale datasets based on complexity of concept clusters ICLR 2024
Distribution Learning with Valid Outputs Beyond the Worst-Case NIPS 2024
On Differentially Private U Statistics NIPS 2024
Data-Copying in Generative Models: A Formal Framework ICML 2023
Do SSL Models Have DΓ©jΓ  Vu? A Case of Unintended Memorization in Self-supervised Learning NIPS 2023
Robust Empirical Risk Minimization with Tolerance ALT 2023
Agnostic Multi-Group Active Learning NIPS 2023
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors ICML 2023
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design ICML 2023
Why does Throwing Away Data Improve Worst-Group Error? ICML 2023
Privacy Amplification by Subsampling in Time Domain AISTATS 2022
Thompson Sampling for Robust Transfer in Multi-Task Bandits ICML 2022
Bounding Training Data Reconstruction in Private (Deep) Learning ICML 2022
Privacy-aware compression for federated data analysis UAI 2022
Privacy Implications of Shuffling ICLR 2022
Sentence-level Privacy for Document Embeddings ACL 2022
Privacy Amplification via Shuffling for Linear Contextual Bandits ALT 2022
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation AISTATS 2021
Understanding Instance-based Interpretability of Variational Auto-Encoders NIPS 2021
Consistent Non-Parametric Methods for Maximizing Robustness NIPS 2021
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning AISTATS 2021
Approximate Data Deletion from Machine Learning Models AISTATS 2021
Location Trace Privacy Under Conditional Priors AISTATS 2021
Sample Complexity of Robust Linear Classification on Separated Data ICML 2021
Connecting Interpretability and Robustness in Decision Trees through Separation ICML 2021
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning JMLR 2021
When are Non-Parametric Methods Robust? ICML 2020
Variational Bayes in Private Settings (VIPS) (Extended Abstract) IJCAI 2020
A Closer Look at Accuracy vs. Robustness NIPS 2020
A Three Sample Hypothesis Test for Evaluating Generative Models AISTATS 2020
Robustness for Non-Parametric Classification: A Generic Attack and Defense AISTATS 2020
The Expressive Power of a Class of Normalizing Flow Models AISTATS 2020
The Label Complexity of Active Learning from Observational Data NIPS 2019
Capacity Bounded Differential Privacy NIPS 2019
Active Learning with Logged Data ICML 2018
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples ICML 2018
Approximation and Convergence Properties of Generative Adversarial Learning NIPS 2017
Renyi Differential Privacy Mechanisms for Posterior Sampling NIPS 2017
Active Heteroscedastic Regression ICML 2017
Active Learning from Imperfect Labelers NIPS 2016
The Extended Littlestone’s Dimension for Learning with Mistakes and Abstentions COLT 2016
Convergence Rates of Active Learning for Maximum Likelihood Estimation NIPS 2015
Learning from Data with Heterogeneous Noise using SGD AISTATS 2015
Active Learning from Weak and Strong Labelers NIPS 2015
Spectral Learning of Large Structured HMMs for Comparative Epigenomics NIPS 2015
The Large Margin Mechanism for Differentially Private Maximization NIPS 2014
Rates of Convergence for Nearest Neighbor Classification NIPS 2014
Beyond Disagreement-Based Agnostic Active Learning NIPS 2014
A Near-Optimal Algorithm for Differentially-Private Principal Components JMLR 2013
A Stability-based Validation Procedure for Differentially Private Machine Learning NIPS 2013
Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model COLT 2012
Near-optimal Differentially Private Principal Components NIPS 2012
Differentially Private Empirical Risk Minimization JMLR 2011
Spectral Methods for Learning Multivariate Latent Tree Structure NIPS 2011
Sample Complexity Bounds for Differentially Private Learning COLT 2011
Rates of convergence for the cluster tree NIPS 2010
A Parameter-free Hedging Algorithm NIPS 2009
Privacy-preserving logistic regression NIPS 2008