Jonathan Ullman
26 papers · 2015–2025 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (33) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π¬
Deep Specialist
(21)
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Triple Crown
π
Keyword Champion
(4)
π§
Keyword Pioneer
π
Trend Setter
β
The Questioner
ποΈ
Keyword Collector
(108)
π₯
Unstoppable Β· Silver
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Century Club Β· Bronze
β‘
Prolific Year Β· Productive
Top co-authors
Research topics
Machine Learning
(26)
Application Areas
(21)
Optimization & Theory
(14)
Mathematics & Optimization
(10)
Security & Privacy
(10)
Differential Privacy
(10)
Mathematics
(8)
Core Methods
(7)
Learning Types
(6)
Artificial Intelligence
(5)
Core AI
(3)
Optimization
(3)
Learning Paradigms Β· Artificial Intelligence
(2)
Learning Paradigms Β· Machine Learning
(2)
Computer Science
(1)
Keywords
differential privacy
(20)
sample complexity
(7)
private algorithm
(4)
mean estimation
(4)
covariance estimation
(3)
statistical query
(3)
multi-task learning
(3)
gaussian distribution
(3)
parameter estimation
(2)
stochastic gradient descent
(2)
query release
(2)
product distribution
(2)
lower bound
(2)
data poisoning
(2)
statistical estimation
(2)
public data
(2)
sub-gaussian distribution
(2)
optimal transport
(1)
k-means clustering
(1)
principal component analysis
(1)
Papers
Privacy in Metalearning and Multitask Learning: Modeling and Separations
AISTATS 2025
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
ICLR 2024
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
ICML 2024
Metalearning with Very Few Samples Per Task
COLT 2024
Private Geometric Median
NeurIPS 2024
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes
COLT 2024
From Robustness to Privacy and Back
ICML 2023
Multitask Learning via Shared Features: Algorithms and Hardness
COLT 2023
A Private and Computationally-Efficient Estimator for Unbounded Gaussians
COLT 2022
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
NeurIPS 2021
Leveraging Public Data for Practical Private Query Release
ICML 2021
Auditing Differentially Private Machine Learning: How Private is Private SGD?
NeurIPS 2020
CoinPress: Practical Private Mean and Covariance Estimation
NeurIPS 2020
Efficient Private Algorithms for Learning Large-Margin Halfspaces
ALT 2020
Private Identity Testing for High-Dimensional Distributions
NeurIPS 2020
Private Mean Estimation of Heavy-Tailed Distributions
COLT 2020
Private Query Release Assisted by Public Data
ICML 2020
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
NeurIPS 2019
Differentially Private Fair Learning
ICML 2019
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
NeurIPS 2019
Privately Learning High-Dimensional Distributions
COLT 2019
Local Differential Privacy for Evolving Data
NeurIPS 2018
The Limits of Post-Selection Generalization
NeurIPS 2018
The Price of Selection in Differential Privacy
COLT 2017
Privacy Odometers and Filters: Pay-as-you-Go Composition
NeurIPS 2016
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery
COLT 2015