128 citations · 144 across the 12 of their papers we have counts for
12 papers
Projection-free Adaptive Regret with Membership Oracles
Zhou Lu, Nataly Brukhim, Paula Gradu +1
In the framework of online convex optimization, most iterative algorithms require the computation of projections onto convex sets, which can be computationally expensive. To tackle…
Adaptive Online Learning of Quantum States
Xinyi Chen, Elad Hazan, Tongyang Li +3
The problem of efficient quantum state learning, also called shadow tomography, aims to comprehend an unknown -dimensional quantum state through POVMs. Yet, these states are rar…
Non-convex online learning via algorithmic equivalence
Udaya Ghai, Zhou Lu, Elad Hazan
We study an algorithmic equivalence technique between non-convex gradient descent and convex mirror descent. We start by looking at a harder problem of regret minimization in onlin…
Adaptive Gradient Methods with Local Guarantees
Zhou Lu, Wenhan Xia, Sanjeev Arora +1
Adaptive gradient methods are the method of choice for optimization in machine learning and used to train the largest deep models. In this paper we study the problem of learning a…
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence
Daogao Liu, Zhou Lu
Stochastic Gradient Descent (SGD) is among the simplest and most popular methods in optimization. The convergence rate for SGD has been extensively studied and tight analyses have…
The Power of Sampling: Dimension-free Risk Bounds in Private ERM
Yin Tat Lee, Daogao Liu, Zhou Lu
Differentially private empirical risk minimization (DP-ERM) is a fundamental problem in private optimization. While the theory of DP-ERM is well-studied, as large-scale models beco…