activity
20162022
most citedLimit Laws for Empirical Optimal Solutions in Stochastic Linear Programs

6 citations · 6 across the 2 of their papers we have counts for

collaborators

7 papers

stat.ME2022

Transportation-Based Functional ANOVA and PCA for Covariance Operators

Valentina Masarotto, Victor M. Panaretos, Yoav Zemel

We consider the problem of comparing several samples of stochastic processes with respect to their second-order structure, and describing the main modes of variation in this second…

stat.CO2020

Randomised Wasserstein Barycenter Computation: Resampling with Statistical Guarantees

Florian Heinemann, Axel Munk, Yoav Zemel

We propose a hybrid resampling method to approximate finitely supported Wasserstein barycenters on large-scale datasets, which can be combined with any exact solver. Nonasymptotic…

math.ST20206 cited

Limit Laws for Empirical Optimal Solutions in Stochastic Linear Programs

Marcel Klatt, Axel Munk, Yoav Zemel

We consider a general linear program in standard form whose right-hand side constraint vector is subject to random perturbations. This defines a stochastic linear program for which…

stat.ME2018

Bayesian semiparametric modelling of phase-varying point processes

Bastian Galasso, Yoav Zemel, Miguel de Carvalho

We propose a Bayesian semiparametric approach for registration of multiple point processes. Our approach entails modelling the mean measures of the phase-varying point processes wi…

stat.ME2018

Statistical Aspects of Wasserstein Distances

Victor M. Panaretos, Yoav Zemel

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to…

stat.CO2018

Optimal Transport: Fast Probabilistic Approximation with Exact Solvers

Max Sommerfeld, Jörn Schrieber, Yoav Zemel +1

We propose a simple subsampling scheme for fast randomized approximate computation of optimal transport distances. This scheme operates on a random subset of the full data and can…