Search Results for author: Neil Lawrence

Found 14 papers, 2 papers with code

Meta-Surrogate Benchmarking for Hyperparameter Optimization

1 code implementation NeurIPS 2019 Aaron Klein, Zhenwen Dai, Frank Hutter, Neil Lawrence, Javier Gonzalez

Despite the recent progress in hyperparameter optimization (HPO), available benchmarks that resemble real-world scenarios consist of a few and very large problem instances that are expensive to solve.

Hyperparameter Optimization

Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients

no code implementations24 Apr 2019 Yu Chen, Tom Diethe, Neil Lawrence

Conventional models tend to forget the knowledge of previous tasks while learning a new task, a phenomenon known as catastrophic forgetting.

Continual Learning

Deep Gaussian Processes for Multi-fidelity Modeling

1 code implementation18 Mar 2019 Kurt Cutajar, Mark Pullin, Andreas Damianou, Neil Lawrence, Javier González

Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models.

Decision Making Gaussian Processes +1

Continual Learning in Practice

no code implementations12 Mar 2019 Tom Diethe, Tom Borchert, Eno Thereska, Borja Balle, Neil Lawrence

This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives.

AutoML Continual Learning

Structured Variationally Auto-encoded Optimization

no code implementations ICML 2018 Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai, Neil Lawrence

We tackle the problem of optimizing a black-box objective function defined over a highly-structured input space.

Transfer Learning Variational Inference

Intrinsic Gaussian processes on complex constrained domains

no code implementations3 Jan 2018 Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil Lawrence, David Dunson

in-GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces.

Gaussian Processes

Parallelizable sparse inverse formulation Gaussian processes (SpInGP)

no code implementations25 Oct 2016 Alexander Grigorievskiy, Neil Lawrence, Simo Särkkä

We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models.

Gaussian Processes

Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology

no code implementations17 Oct 2016 Mu Niu, Zhenwen Dai, Neil Lawrence, Kolja Becker

The spatio-temporal field of protein concentration and mRNA expression are reconstructed without explicitly solving the partial differential equation.

Bayesian Inference Gaussian Processes

Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model

no code implementations30 Jun 2016 Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil Lawrence

Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data.

Variational Inference

Variational Auto-encoded Deep Gaussian Processes

no code implementations19 Nov 2015 Zhenwen Dai, Andreas Damianou, Javier González, Neil Lawrence

We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model.

Gaussian Processes

Spike and Slab Gaussian Process Latent Variable Models

no code implementations10 May 2015 Zhenwen Dai, James Hensman, Neil Lawrence

The Gaussian process latent variable model (GP-LVM) is a popular approach to non-linear probabilistic dimensionality reduction.

Dimensionality Reduction Gaussian Processes +1

Gaussian Process Models with Parallelization and GPU acceleration

no code implementations18 Oct 2014 Zhenwen Dai, Andreas Damianou, James Hensman, Neil Lawrence

In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration.

Overlapping Mixtures of Gaussian Processes for the Data Association Problem

no code implementations16 Aug 2011 Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil Lawrence

In this work we introduce a mixture of GPs to address the data association problem, i. e. to label a group of observations according to the sources that generated them.

Gaussian Processes Multi-Object Tracking

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