20 citations · 30 across the 5 of their papers we have counts for
10 papers
Exponential Smoothing for Time Series of Random Objects
Takuo Matsubara, Peiwen Jiang, Wilson Ye Chen +1
Time series of random objects, such as covariance matrices, probability distributions, and functional data, call for forecasting methods that do not rely on standard arithmetic ope…
Wasserstein Exponential Smoothing for Distributional Time Series Forecasting
Takuo Matsubara, Peiwen Jiang, Minh-Ngoc Tran +1
Distributional time series arise when each temporal observation is a probability distribution rather than a scalar. We propose Wasserstein exponential smoothing (WES), a one-parame…
Optimal Thinning of MCMC Output
Marina Riabiz, Wilson Chen, Jon Cockayne +4
The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced. T…
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…
Stein Points
Wilson Ye Chen, Lester Mackey, Jackson Gorham +2
An important task in computational statistics and machine learning is to approximate a posterior distribution with an empirical measure supported on a set of representative…
Factor graph fragmentization of expectation propagation
Wilson Y. Chen, Matt P. Wand
Expectation propagation is a general approach to fast approximate inference for graphical models. The existing literature treats models separately when it comes to deriving and cod…