Amazon Technologies, Inc.

Matthias Seeger

Jan Alexander Gasthaus

Giovanni Zappella

Tim Januschowski

Cedric Philippe Archambeau

Rodolphe Jenatton

Valerio Perrone

Lorenzo Stella

Gregory Michael Duncan

Syama Rangapuram

Iaroslav Shcherbatyi

Hyperparameters for tuning a machine learning system may be optimized using Bayesian optimization with constraints. The hyperparameter optimization may be performed for a received training set and received constraints. Respective probabilistic models for the machine learning system and constraint functions may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing entropy search on the respective models, training the machine learning system using the identified values to determine measures of accuracy and constraint metrics, and updating the respective models using the determined measures.

Hyperparameter optimization with operational constraints

A composite time series forecasting model comprising a neural network sub-model and one or more state space sub-models corresponding to individual time series is trained. During training, output of the neural network sub-model is used to determine parameters of the state space sub-models, and a loss function is computed using the values of the time series and probabilistic values generated as output by the state space sub-models. A trained version of the composite model is stored.

Artificial intelligence system combining state space models and neural networks for time series forecasting

With respect to an input data set which contains observation records of a time series, a statistical model which utilizes a likelihood function comprising a latent function is generated. The latent function comprises a combination of a deterministic component and a random process. Parameters of the model are fitted using approximate Bayesian inference, and the model is used to generate probabilistic forecasts corresponding to the input data set.

Intermittent demand forecasting for large inventories

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Berlin, Germany

Matthias Seeger