Search Results for author:
Found 6 papers, 4 papers with code
Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.
MLJ: A Julia package for composable machine learning
MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages.
Model-based Asynchronous Hyperparameter and Neural Architecture Search
We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization.
Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration.
Methodology Computation
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
The posterior server allows scalable and robust Bayesian learning in cases where a data set is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data.
Expectation Particle Belief Propagation
The computational complexity of our algorithm at each iteration is quadratic in the number of particles.
