989 citations · 1.3k across the 20 of their papers we have counts for
3 papers · 1 filter
Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
Valerio Perrone, Rodolphe Jenatton, Matthias Seeger +1
Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization. Typically, BO is powered by a Gaussian process (GP), whose algorithmic compl…
Auto-Differentiating Linear Algebra
Matthias Seeger, Asmus Hetzel, Zhenwen Dai +2
Development systems for deep learning (DL), such as Theano, Torch, TensorFlow, or MXNet, are easy-to-use tools for creating complex neural network models. Since gradient computatio…
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,…