368 citations · 679 across the 19 of their papers we have counts for
5 papers · 1 filter
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
Matthias Seeger, Syama Rangapuram, Yuyang Wang +4
We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to demand forecasting in the context of a large e-commerce platform,…
Constrained 1-Spectral Clustering
Syama Sundar Rangapuram, Matthias Hein
An important form of prior information in clustering comes in form of cannot-link and must-link constraints. We present a generalization of the popular spectral clustering techniqu…
Tight Continuous Relaxation of the Balanced -Cut Problem
Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the standard graph-based clustering methods. Existing methods for the computation of multiple clus…
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
Matthias Hein, Simon Setzer, Leonardo Jost +1
Hypergraphs allow one to encode higher-order relationships in data and are thus a very flexible modeling tool. Current learning methods are either based on approximations of the hy…
Constrained fractional set programs and their application in local clustering and community detection
Thomas Bühler, Syama Sundar Rangapuram, Simon Setzer +1
The (constrained) minimization of a ratio of set functions is a problem frequently occurring in clustering and community detection. As these optimization problems are typically NP-…