← Authors

Hilal Asi

21 papers · 2019–2025 · 4 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (4) πŸƒ Academic Marathon (6) 🐝 Cross-Pollinator (11) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (20) πŸ”¬ Deep Specialist (13) πŸ† Keyword Champion (7) πŸ—ƒοΈ Keyword Collector (75) πŸ’Ž Century Club (21) ⚑ Prolific Year (6) πŸ”₯ Unstoppable (7)

Conferences

ICML (9) NIPS (8) AISTATS (2) COLT (2)

Top co-authors

Kunal Talwar (9) Vitaly Feldman (6) John C. Duchi (5) Tomer Koren (4) Daogao Liu (4) Jelani Nelson (2) Karan Chadha (2) Vinod Raman (2) John Duchi (2) Huy Nguyen (2)

Research topics

Differential Privacy (3)

Keywords

differential privacy (13) stochastic convex optimization (7) mean estimation (3) stochastic optimization (3) gradient descent (3) regret bound (3) convex optimization (2) online learning (2) model-based method (2) expert prediction (2) online convex optimization (2) user-level privacy (2) proximal point method (2) principal component analysis (2) gradient estimation (1) privacy-preserving learning (1) distributed learning (1) excess risk (1) sample complexity (1) adversarial robustness (1)

Papers

Faster Rates for Private Adversarial Bandits ICML 2025
Tracking The Best Expert Privately ICML 2025
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages ICML 2024
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions NIPS 2024
Faster Algorithms for User-Level Private Stochastic Convex Optimization NIPS 2024
Universally Instance-Optimal Mechanisms for Private Statistical Estimation COLT 2024
Private Online Learning via Lazy Algorithms NIPS 2024
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates AISTATS 2024
Private Online Prediction from Experts: Separations and Faster Rates COLT 2023
Fast Optimal Locally Private Mean Estimation via Random Projections NIPS 2023
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime ICML 2023
From Robustness to Privacy and Back ICML 2023
Optimal Algorithms for Mean Estimation under Local Differential Privacy ICML 2022
Private optimization in the interpolation regime: faster rates and hardness results ICML 2022
Stochastic Bias-Reduced Gradient Methods NIPS 2021
Private Adaptive Gradient Methods for Convex Optimization ICML 2021
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry ICML 2021
Adapting to function difficulty and growth conditions in private optimization NIPS 2021
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms NIPS 2020
Minibatch Stochastic Approximate Proximal Point Methods NIPS 2020
Modeling simple structures and geometry for better stochastic optimization algorithms AISTATS 2019