37 citations · 71 across the 10 of their papers we have counts for
20 papers
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States
Julian Zimmert, Naman Agarwal, Satyen Kale
We revisit the classical online portfolio selection problem. It is widely assumed that a trade-off between computational complexity and regret is unavoidable, with Cover's Universa…
The Skellam Mechanism for Differentially Private Federated Learning
Naman Agarwal, Peter Kairouz, Ziyu Liu
We introduce the multi-dimensional Skellam mechanism, a discrete differential privacy mechanism based on the difference of two independent Poisson random variables. To quantify its…
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
Naman Agarwal, Syomantak Chaudhuri, Prateek Jain +2
Q-learning is a popular Reinforcement Learning (RL) algorithm which is widely used in practice with function approximation (Mnih et al., 2015). In contrast, existing theoretical re…
Efficient Methods for Online Multiclass Logistic Regression
Naman Agarwal, Satyen Kale, Julian Zimmert
Multiclass logistic regression is a fundamental task in machine learning with applications in classification and boosting. Previous work (Foster et al., 2018) has highlighted the i…
Acceleration via Fractal Learning Rate Schedules
Naman Agarwal, Surbhi Goel, Cyril Zhang
In practical applications of iterative first-order optimization, the learning rate schedule remains notoriously difficult to understand and expensive to tune. We demonstrate the pr…
A Regret Minimization Approach to Iterative Learning Control
Naman Agarwal, Elad Hazan, Anirudha Majumdar +1
We consider the setting of iterative learning control, or model-based policy learning in the presence of uncertain, time-varying dynamics. In this setting, we propose a new perform…