← Authors

Adam Smith

27 papers · 2012–2025 · 7 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer πŸƒ Academic Marathon (13) 🐝 Cross-Pollinator (14) πŸ—ΊοΈ Taxonomy Completionist (30) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (7) πŸ”¬ Deep Specialist (15) πŸ† Keyword Champion πŸ—ƒοΈ Keyword Collector (107) πŸ“ˆ Trend Setter πŸ’Ž Century Club (27) πŸ”₯ Unstoppable (8) ⚑ Prolific Year (5)

Conferences

NIPS (13) COLT (6) ICML (3) AISTATS (2) ALT (1) ICLR (1) JMLR (1)

Top co-authors

Abhradeep Guha Thakurta (7) Gavin Brown (5) Maryam Aliakbarpour (4) Jonathan Ullman (4) Satchit Sivakumar (3) Marika Swanberg (3) Sofya Raskhodnikova (3) Mark Bun (3) Marco Gaboardi (2) Thomas Steinke (2)

Research topics

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

Keywords

differential privacy (15) convex optimization (4) sample complexity (3) learning theory (3) empirical risk minimization (3) mean estimation (2) streaming algorithm (2) covariance estimation (2) user-level privacy (2) total variation distance (2) statistical query (2) lower bound (2) subgaussian distribution (2) few-shot learning (1) feature selection (1) stochastic gradient descent (1) density estimation (1) high-dimensional statistics (1) efficient computing (1) online learning (1)

Papers

It’s My Data Too: Private ML for Datasets with Multi-User Training Examples ICML 2025
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD ICLR 2025
Privacy in Metalearning and Multitask Learning: Modeling and Separations AISTATS 2025
Metalearning with Very Few Samples Per Task COLT 2024
Auditing Privacy Mechanisms via Label Inference Attacks NIPS 2024
Optimal Hypothesis Selection in (Almost) Linear Time NIPS 2024
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation ICML 2024
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Extended Abstract COLT 2024
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions COLT 2023
The Price of Differential Privacy under Continual Observation ICML 2023
Hypothesis Selection with Memory Constraints NIPS 2023
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation NIPS 2023
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams NIPS 2022
Strong Memory Lower Bounds for Learning Natural Models COLT 2022
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation NIPS 2021
Differentially Private Sampling from Distributions NIPS 2021
Differentially Private Model Personalization NIPS 2021
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy JMLR 2020
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space NIPS 2020
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis AISTATS 2020
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations ALT 2019
The Limits of Post-Selection Generalization NIPS 2018
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization NIPS 2018
Private Graphon Estimation for Sparse Graphs NIPS 2015
Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso COLT 2013
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings NIPS 2013
Private Convex Empirical Risk Minimization and High-dimensional Regression COLT 2012