Adam Smith
27 papers · 2012–2025 · 7 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (37) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Deep Specialist
(15)
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Century Club Β· Bronze
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Prolific Year Β· Productive
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Keyword Collector
(130)
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Unstoppable Β· Bronze
Top co-authors
Research topics
Machine Learning
(26)
Optimization & Theory
(16)
Application Areas
(15)
Mathematics & Optimization
(12)
Security & Privacy
(11)
Differential Privacy
(9)
Artificial Intelligence
(8)
Core Methods
(7)
Learning Types
(6)
Mathematics
(6)
Core AI
(5)
Optimization
(5)
Learning Paradigms Β· Artificial Intelligence
(3)
Learning Paradigms Β· Machine Learning
(3)
Privacy
(3)
Keywords
differential privacy
(18)
sample complexity
(7)
convex optimization
(4)
learning theory
(3)
empirical risk minimization
(3)
mean estimation
(3)
total variation distance
(2)
sub-gaussian distribution
(2)
model personalization
(2)
lower bound
(2)
streaming algorithm
(2)
privacy auditing
(2)
stochastic gradient descent
(2)
user-level privacy
(2)
covariance estimation
(2)
statistical query
(2)
confidence interval
(2)
multi-task learning
(2)
linear regression
(2)
feature selection
(1)
Papers
Itβs My Data Too: Private ML for Datasets with Multi-User Training Examples
ICML 2025
Privacy in Metalearning and Multitask Learning: Modeling and Separations
AISTATS 2025
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
ICLR 2025
Auditing Privacy Mechanisms via Label Inference Attacks
NeurIPS 2024
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Extended Abstract
COLT 2024
Metalearning with Very Few Samples Per Task
COLT 2024
Optimal Hypothesis Selection in (Almost) Linear Time
NeurIPS 2024
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
ICML 2024
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation
NeurIPS 2023
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions
COLT 2023
Hypothesis Selection with Memory Constraints
NeurIPS 2023
The Price of Differential Privacy under Continual Observation
ICML 2023
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
NeurIPS 2022
Strong Memory Lower Bounds for Learning Natural Models
COLT 2022
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
NeurIPS 2021
Differentially Private Model Personalization
NeurIPS 2021
Differentially Private Sampling from Distributions
NeurIPS 2021
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
JMLR 2020
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis
AISTATS 2020
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
NeurIPS 2020
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations
ALT 2019
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
NeurIPS 2018
The Limits of Post-Selection Generalization
NeurIPS 2018
Private Graphon Estimation for Sparse Graphs
NeurIPS 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
NeurIPS 2013
Private Convex Empirical Risk Minimization and High-dimensional Regression
COLT 2012