Ryan McKenna
10 papers · 2017–2025 · 4 venues · across top CS/AI venues
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Achievements
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🌉 Interdisciplinary Bridge · Bronze 🌍 Conference Polyglot · Bronze 🏃 Academic Marathon · Sprint 🗺️ Taxonomy Completionist (14) 🐣 Hot Topic Early Bird
🏆
Keyword Champion
(2)
💎
Century Club · Bronze
Venues
Top co-authors
Research topics
Machine Learning
(10)
Application Areas
(7)
Artificial Intelligence
(6)
Optimization & Theory
(6)
Security & Privacy
(3)
Bayesian & Probabilistic · Artificial Intelligence
(3)
Core AI
(3)
Differential Privacy
(3)
Bayesian & Probabilistic · Machine Learning
(2)
Deep Learning
(1)
Mathematics & Optimization
(1)
Natural Language Processing
(1)
Core Methods
(1)
Learning Paradigms
(1)
Learning Types
(1)
Keywords
differential privacy
(10)
matrix factorization
(3)
graphical model
(3)
privacy amplification
(3)
correlated noise
(2)
federated learning
(2)
exponential mechanism
(1)
greedy algorithm
(1)
noise correlation
(1)
high-dimensional data
(1)
public data
(1)
synthetic data
(1)
bias-variance tradeoff
(1)
scaling law
(1)
privacy protection
(1)
private training
(1)
private selection
(1)
private data
(1)
sufficient statistics
(1)
collective graphical model
(1)
Papers
It’s My Data Too: Private ML for Datasets with Multi-User Training Examples
ICML 2025
Scaling Laws for Differentially Private Language Models
ICML 2025
Scaling up the Banded Matrix Factorization Mechanism for Large Scale Differentially Private ML
ICLR 2025
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
AISTATS 2024
(Amplified) Banded Matrix Factorization: A unified approach to private training
NeurIPS 2023
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
NeurIPS 2023
Relaxed Marginal Consistency for Differentially Private Query Answering
NeurIPS 2021
Permute-and-Flip: A new mechanism for differentially private selection
NeurIPS 2020
Graphical-model based estimation and inference for differential privacy
ICML 2019
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models
ICML 2017