We reformulate the computation of the acquisition function in Bayesian optimization (BO) as a probabilistic classification problem, providing advantages in scalability, flexibility, and representational capacity, while casting aside the limitations of tractability constraints on the model.

Our proposed framework uses a joint probabilistic model and stochastic variational inference to improve the performance and robustness of graph convolutional networks (GCNs) in scenarios without input graph data, outperforming state-of-the-art algorithms on semi-supervised classification tasks.

We introduce a model-based method for asynchronous multi-fidelity hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization, achieving substantial speed-ups over current state-of-the-art methods on challenging benchmarks for tabular data, image classification, and language modeling.

One weird trick to make exact inference in Bayesian logistic regression tractable.

This series explores market data provided by official API from Binance, one of the world’s largest cryptocurrency exchanges, using Python. In this post we examine various useful ways to visualize the recent and historical trades.

A short illustrated reference guide to the Knowledge Gradient acquisition function with an implementation from scratch in TensorFlow Probability.

This series explores market data provided by official API from Binance, one of the world’s largest cryptocurrency exchanges, using Python. In this post we examine various useful ways to visualize the orderbook.

A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework.