Adam Smith
27 papers · 2012–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π Interdisciplinary Bridge π Conference Polyglot (7) π§ Keyword Pioneer π£ Hot Topic Early Bird π Academic Marathon (13)
π
Interdisciplinary Bridge
π
Conference Polyglot
(7)
π¬
Deep Specialist
(15)
π
Keyword Champion
ποΈ
Keyword Collector
(107)
π
Trend Setter
π
Century Club
(27)
π₯
Unstoppable
(8)
β‘
Prolific Year
(5)
Conferences
NIPS (13)
COLT (6)
ICML (3)
AISTATS (2)
ALT (1)
ICLR (1)
JMLR (1)
Top co-authors
Research topics
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
Itβs 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