Scalable Hyperparameter Transfer Learning

no code implementations • NeurIPS 2018 • Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cedric Archambeau

Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization, such as hyperparameter optimization.

Bayesian Optimization Hyperparameter Optimization +2

Deep State Space Models for Time Series Forecasting

2 code implementations • NeurIPS 2018 • Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski

We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning.

Probabilistic Time Series Forecasting Time Series

Bayesian Intermittent Demand Forecasting for Large Inventories

no code implementations • NeurIPS 2016 • Matthias W. Seeger, David Salinas, Valentin Flunkert

We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics.