173 citations · 211 across the 10 of their papers we have counts for
5 papers · 1 filter
Importance Sampling via Local Sensitivity
Anant Raj, Cameron Musco, Lester Mackey
Given a loss function that can be written as the sum of losses over a large set of inputs , it is often desirable to approximate $…
Single Point Transductive Prediction
Nilesh Tripuraneni, Lester Mackey
Standard methods in supervised learning separate training and prediction: the model is fit independently of any test points it may encounter. However, can knowledge of the next tes…
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
Xuechen Li, Denny Wu, Lester Mackey +1
Sampling with Markov chain Monte Carlo methods often amounts to discretizing some continuous-time dynamics with numerical integration. In this paper, we establish the convergence r…
Stein Point Markov Chain Monte Carlo
Wilson Ye Chen, Alessandro Barp, François-Xavier Briol +4
An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a cla…
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions
Ashia Wilson, Lester Mackey, Andre Wibisono
We present a family of algorithms, called descent algorithms, for optimizing convex and non-convex functions. We also introduce a new first-order algorithm, called rescaled gradien…