← Authors

Raef Bassily

28 papers · 2017–2025 · 6 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (8) 🐝 Cross-Pollinator (10) 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (34) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ”¬ Deep Specialist (23) πŸ† Keyword Champion (2) 🌱 Topic Pioneer πŸ—ƒοΈ Keyword Collector (82) πŸ’Ž Century Club (28) πŸ”₯ Unstoppable (9) πŸ“ˆ Trend Setter ⚑ Prolific Year (5)

Conferences

NIPS (12) ICML (7) ALT (3) COLT (3) AISTATS (2) JMLR (1)

Top co-authors

CristΓ³bal GuzmΓ‘n (8) Michael Menart (7) Enayat Ullah (4) Shay Moran (4) Raman Arora (4) Mehryar Mohri (4) Ananda Theertha Suresh (3) Anupama Nandi (3) Xinyu Zhou (3) Abhradeep Guha Thakurta (3)

Research topics

Differential Privacy (6) Privacy (1)

Keywords

differential privacy (23) sample complexity (4) stochastic gradient descent (4) public datum (4) stochastic convex optimization (4) stochastic optimization (4) private learning (4) generalization guarantee (3) generalized linear model (3) vc dimension (3) local privacy (3) pac learning (2) neural network (2) model selection (2) excess population risk (2) margin guarantee (2) heavy hitter (2) non-convex optimization (2) convergence rate (2) mean estimation (1)

Papers

Private Model Personalization Revisited ICML 2025
Public-data Assisted Private Stochastic Optimization: Power and Limitations NIPS 2024
Differentially Private Worst-group Risk Minimization ICML 2024
Differentially Private Domain Adaptation with Theoretical Guarantees ICML 2024
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates ALT 2024
Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry NIPS 2024
User-level Private Stochastic Convex Optimization with Optimal Rates ICML 2023
Principled Approaches for Private Adaptation from a Public Source AISTATS 2023
Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap COLT 2023
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization ICML 2023
Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees COLT 2022
Differentially Private Learning with Margin Guarantees NIPS 2022
Differentially Private Generalized Linear Models Revisited NIPS 2022
Task-level Differentially Private Meta Learning NIPS 2022
Non-Euclidean Differentially Private Stochastic Convex Optimization COLT 2021
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings NIPS 2021
Learning from Mixtures of Private and Public Populations NIPS 2020
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses NIPS 2020
Practical Locally Private Heavy Hitters JMLR 2020
Private Query Release Assisted by Public Data ICML 2020
Privately Answering Classification Queries in the Agnostic PAC Model ALT 2020
Linear Queries Estimation with Local Differential Privacy AISTATS 2019
Limits of Private Learning with Access to Public Data NIPS 2019
Private Stochastic Convex Optimization with Optimal Rates NIPS 2019
Learners that Use Little Information ALT 2018
Model-Agnostic Private Learning NIPS 2018
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning ICML 2018
Practical Locally Private Heavy Hitters NIPS 2017