← Authors

Gautam Kamath

42 papers · 2014–2026 · 6 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (11) 🐝 Cross-Pollinator (11) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (7) πŸ—ΊοΈ Taxonomy Completionist (38) 🌍 Conference Polyglot (6) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (21) πŸ† Keyword Champion (4) πŸ—ƒοΈ Keyword Collector (129) πŸ“ˆ Trend Setter ⚑ Prolific Year (8) πŸ”₯ Unstoppable (9) πŸ’Ž Century Club (41)

Conferences

NIPS (14) ICML (12) COLT (9) ALT (3) AAAI (2) ICLR (2)

Top co-authors

Vikrant Singhal (8) Jonathan Ullman (6) Huanyu Zhang (5) Jayadev Acharya (5) Jerry Li (4) ClΓ©ment L Canonne (4) Alex Bie (4) Constantinos Daskalakis (4) Argyris Mouzakis (4) Yiwei Lu (4)

Research topics

Differential Privacy (8) Privacy (1) Statistics (1) Probability (1)

Keywords

differential privacy (22) sample complexity (9) total variation distance (5) gaussian distribution (4) mean estimation (4) covariance estimation (4) distribution learning (4) hypothesis testing (3) hypothesis selection (3) lower bound (2) proper learning (2) data poisoning (2) parameter estimation (2) heavy-tailed distribution (2) sensitivity analysis (2) robust estimation (2) machine unlearning (2) statistical learning (2) ising model (2) statistical estimation (2)

Papers

Demystifying Foreground-Background Memorization in Diffusion Models AAAI 2026
Machine Unlearning Fails to Remove Data Poisoning Attacks ICLR 2025
Optimal Differentially Private Sampling of Unbounded Gaussians COLT 2025
Algorithmic Learning Theory 2025: Preface ALT 2025
On the Learnability of Distribution Classes with Adaptive Adversaries ICML 2025
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining ICML 2024
Differentially Private Post-Processing for Fair Regression ICML 2024
Not All Learnable Distribution Classes are Privately Learnable ALT 2024
Disguised Copyright Infringement of Latent Diffusion Models ICML 2024
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks ICML 2023
Private Distribution Learning with Public Data: The View from Sample Compression NIPS 2023
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks NIPS 2023
Distribution Learnability and Robustness NIPS 2023
The Price of Tolerance in Distribution Testing COLT 2022
A Private and Computationally-Efficient Estimator for Unbounded Gaussians COLT 2022
Robust Estimation for Random Graphs COLT 2022
Private Estimation with Public Data NIPS 2022
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data ICML 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma NIPS 2022
Differentially Private Fine-tuning of Language Models ICLR 2022
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection AAAI 2022
PAPRIKA: Private Online False Discovery Rate Control ICML 2021
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians ALT 2021
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization NIPS 2021
Remember What You Want to Forget: Algorithms for Machine Unlearning NIPS 2021
Locally Private Hypothesis Selection COLT 2020
Privately Learning Markov Random Fields ICML 2020
Private Identity Testing for High-Dimensional Distributions NIPS 2020
The Discrete Gaussian for Differential Privacy NIPS 2020
CoinPress: Practical Private Mean and Covariance Estimation NIPS 2020
Private Mean Estimation of Heavy-Tailed Distributions COLT 2020
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians NIPS 2019
Privately Learning High-Dimensional Distributions COLT 2019
Sever: A Robust Meta-Algorithm for Stochastic Optimization ICML 2019
Private Hypothesis Selection NIPS 2019
Actively Avoiding Nonsense in Generative Models COLT 2018
INSPECTRE: Privately Estimating the Unseen ICML 2018
Priv’IT: Private and Sample Efficient Identity Testing ICML 2017
Being Robust (in High Dimensions) Can Be Practical ICML 2017
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data NIPS 2017
Optimal Testing for Properties of Distributions NIPS 2015
Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians COLT 2014