13 citations · 22 across the 4 of their papers we have counts for
4 papers
An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
Antonio Orvieto, Lin Xiao
We consider the problem of minimizing the average of a large number of smooth but possibly non-convex functions. In the context of most machine learning applications, each loss fun…
Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints
Reese Pathak, Martin J. Wainwright, Lin Xiao
Estimation problems with constrained parameter spaces arise in various settings. In many of these problems, the observations available to the statistician can be modelled as arisin…
On the Convergence Rates of Policy Gradient Methods
Lin Xiao
We consider infinite-horizon discounted Markov decision problems with finite state and action spaces and study the convergence rates of the projected policy gradient method and a g…
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
Yuchen Zhang, Lin Xiao
We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning. We…