Gautam Kamath
Assistant Professor of Computer Science
Works on differentially private machine learning and statistics, robust estimation and algorithmic statistics.
Gautam Kamath's lab at NYU works on learning theory & optimization and trustworthy & safe ML. 29 papers since 2023, 16 at top venues, mostly NeurIPS and ICML. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 55%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 3 papers2026 · T1 3 · collecting
- 12 papers2025 · T1 6
- 7 papers2024 · T1 2
- 7 papers2023 · T1 5
T1 venuesOtherVenues: NeurIPS, ICML, AAAI, ALT, COLT
Research focuspaper titles that use each topic's words
- differentially5 papers
- differentially private5 papers
- Learning theory & optimization3 papers
- statistics1 paper
- Trustworthy & safe ML0 papers
Paper list
From the lab homepage.
20263 papers
202512 papers
- Journal of the American Statistical Association, 120(552)A Bias-Accuracy-Privacy Trilemma for Statistical Estimation (opens in a new tab)
- SaTMLAvoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition (opens in a new tab)
- NeurIPS (T1)BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection (opens in a new tab)
- SaTMLChoosing Public Datasets for Private Machine Learning via Gradient Subspace Distance (opens in a new tab)
- ICLR (T1)Machine Unlearning Fails to Remove Data Poisoning Attacks (opens in a new tab)
- SaTMLMembership Inference Attacks Cannot Prove that a Model Was Trained On Your Data (opens in a new tab)
- ICML (T1)On the Learnability of Distribution Classes with Adaptive Adversaries (opens in a new tab)
- COLT (T1)Optimal Differentially Private Sampling of Unbounded Gaussians (opens in a new tab)
- SODA (T1)Private Mean Estimation with Person-Level Differential Privacy (opens in a new tab)
- NeurIPS (T1)Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph (opens in a new tab)
- IEEE Data Engineering Bulletin, 49(4)Rethinking Benchmarks for Differentially Private Image Classification (opens in a new tab)
- Statistica Sinica, 35(Online Special Issue I)Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy (opens in a new tab)
20247 papers
- Harvard Data Science Review, 6(1)Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment (opens in a new tab)
- ICML (T1)Differentially Private Post-Processing for Fair Regression (opens in a new tab)
- ICML (T1)Disguised Copyright Infringement of Latent Diffusion Models (opens in a new tab)
- SaTMLIndiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors (opens in a new tab)
- ALTNot All Learnable Distribution Classes are Privately Learnable (opens in a new tab)
- Proceedings of the International Conference on Machine Learning (ICML ). Oral PresentationPosition: Considerations for Differentially Private Learning with Large-Scale Public Pretraining (opens in a new tab)
- IEEE BITS the Information Theory Magazine, 4(3)The Broader Landscape of Robustness in Algorithmic Statistics (opens in a new tab)
20237 papers
- NeurIPS (T1)Distribution Learnability and Robustness (opens in a new tab)
- ICML (T1)Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks (opens in a new tab)
- NeurIPS (T1)Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks (opens in a new tab)
- Transactions on Machine Learning Research (TMLR)Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent (opens in a new tab)
- NeurIPS (T1)Private Distribution Learning with Public Data: The View from Sample Compression (opens in a new tab)
- Transactions on Machine Learning Research (TMLR), . Survey CertificationPrivate GANs, Revisited (opens in a new tab)
- STOC (T1)Robustness Implies Privacy in Statistical Estimation (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
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- Fee
- See program page
Not yet confirmed for this intake.
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Data
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