GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language

21 Dec 20183 code implementations

Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources.

GAUSSIAN PROCESSES

Bayesian Optimization for Probabilistic Programs

NeurIPS 2016 2 code implementations

We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables.

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

NeurIPS 2018 1 code implementation

A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model.

BAYESIAN OPTIMISATION MODEL SELECTION NEURAL ARCHITECTURE SEARCH

A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms

8 Nov 20181 code implementation

This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms.

PROBABILISTIC PROGRAMMING

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

NeurIPS 2019 1 code implementation

We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning.

ACTIVE LEARNING

Acceleration of expensive computations in Bayesian statistics using vector operations

25 Feb 20191 code implementation

We illustrate the potential of SIMD operations for accelerating Bayesian computations and provide the reader with essential implementation techniques required to exploit modern massively parallel processing environments using standard software development tools.

ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language

31 Jan 20191 code implementation

Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum.

GAUSSIAN PROCESSES PROBABILISTIC PROGRAMMING

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