Jonathan Ullman
26 papers · 2015–2025 · 6 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (10) 🌍 Conference Polyglot (6) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (10)
🌉
Interdisciplinary Bridge
🐣
Hot Topic Early Bird
🏆
Keyword Champion
(4)
👑
Triple Crown
🔬
Deep Specialist
(20)
🔥
Unstoppable
(11)
⚡
Prolific Year
(6)
💎
Century Club
(26)
❓
The Questioner
📈
Trend Setter
🗃️
Keyword Collector
(85)
Conferences
NIPS (10)
COLT (8)
ICML (5)
AISTATS (1)
ALT (1)
ICLR (1)
Top co-authors
Research topics
Keywords
differential privacy
(19)
sample complexity
(6)
private algorithm
(4)
gaussian distribution
(3)
statistical query
(3)
covariance estimation
(3)
mean estimation
(3)
query release
(2)
parameter estimation
(2)
subgaussian distribution
(2)
stochastic gradient descent
(2)
public datum
(2)
statistical estimation
(2)
lower bound
(2)
product distribution
(2)
multitask learning
(2)
data poisoning
(1)
robust statistics
(1)
few-shot learning
(1)
learning theory
(1)
Papers
Privacy in Metalearning and Multitask Learning: Modeling and Separations
AISTATS 2025
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
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
ICLR 2024
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes
COLT 2024
Private Geometric Median
NIPS 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
NIPS 2021
Leveraging Public Data for Practical Private Query Release
ICML 2021
Private Query Release Assisted by Public Data
ICML 2020
Private Identity Testing for High-Dimensional Distributions
NIPS 2020
CoinPress: Practical Private Mean and Covariance Estimation
NIPS 2020
Auditing Differentially Private Machine Learning: How Private is Private SGD?
NIPS 2020
Efficient Private Algorithms for Learning Large-Margin Halfspaces
ALT 2020
Private Mean Estimation of Heavy-Tailed Distributions
COLT 2020
Privately Learning High-Dimensional Distributions
COLT 2019
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy
NIPS 2019
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
NIPS 2019
Differentially Private Fair Learning
ICML 2019
Local Differential Privacy for Evolving Data
NIPS 2018
The Limits of Post-Selection Generalization
NIPS 2018
The Price of Selection in Differential Privacy
COLT 2017
Privacy Odometers and Filters: Pay-as-you-Go Composition
NIPS 2016
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery
COLT 2015