Neil D. Lawrence

research

∙ 04/24/2023

Causal fault localisation in dataflow systems

Dataflow computing was shown to bring significant benefits to multiple n...

0 Andrei Paleyes, et al. ∙

research

∙ 04/15/2023

Dimensionality Reduction as Probabilistic Inference

Dimensionality reduction (DR) algorithms compress high-dimensional data ...

0 Aditya Ravuri, et al. ∙

research

∙ 03/16/2023

Dataflow graphs as complete causal graphs

Component-based development is one of the core principles behind modern ...

0 Andrei Paleyes, et al. ∙

research

∙ 03/07/2023

AI for Science: An Emerging Agenda

This report documents the programme and the outcomes of Dagstuhl Seminar...

0 Philipp Berens, et al. ∙

research

∙ 02/09/2023

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

With the upsurge of interest in artificial intelligence machine learning...

0 Christian Cabrera, et al. ∙

research

∙ 10/29/2022

Ice Core Dating using Probabilistic Programming

Ice cores record crucial information about past climate. However, before...

0 Aditya Ravuri, et al. ∙

research

∙ 09/14/2022

Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs

Single-cell RNA-seq datasets are growing in size and complexity, enablin...

0 Vidhi Lalchand, et al. ∙

research

∙ 06/13/2022

Modeling the Machine Learning Multiverse

Amid mounting concern about the reliability and credibility of machine l...

0 Samuel J. Bell, et al. ∙

research

∙ 05/26/2022

The Effect of Task Ordering in Continual Learning

We investigate the effect of task ordering on continual learning perform...

0 Samuel J. Bell, et al. ∙

research

∙ 04/27/2022

An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context

As use of data driven technologies spreads, software engineers are more ...

0 Andrei Paleyes, et al. ∙

research

∙ 03/15/2022

Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data

Classically, statistical datasets have a larger number of data points th...

0 Sijia Li, et al. ∙

research

∙ 02/25/2022

Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference

Gaussian process latent variable models (GPLVM) are a flexible and non-l...

0 Vidhi Lalchand, et al. ∙

research

∙ 11/20/2021

Bayesian Learning via Neural Schrödinger-Föllmer Flows

In this work we explore a new framework for approximate Bayesian inferen...

0 Francisco Vargas, et al. ∙

research

∙ 10/25/2021

Emulation of physical processes with Emukit

Decision making in uncertain scenarios is an ubiquitous challenge in rea...

0 Andrei Paleyes, et al. ∙

research

∙ 10/20/2021

Behavioral Experiments for Understanding Catastrophic Forgetting

In this paper we explore whether the fundamental tool of experimental ps...

0 Samuel J. Bell, et al. ∙

research

∙ 09/20/2021

Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment

In this paper we revisit the 2014 NeurIPS experiment that examined incon...

14 Corinna Cortes, et al. ∙

research

∙ 08/09/2021

Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures

Despite huge successes reported by the field of machine learning, such a...

0 Andrei Paleyes, et al. ∙

research

∙ 06/03/2021

Solving Schrödinger Bridges via Maximum Likelihood

The Schrödinger bridge problem (SBP) finds the most likely stochastic ev...

0 Francisco Vargas, et al. ∙

research

∙ 11/18/2020

Challenges in Deploying Machine Learning: a Survey of Case Studies

In recent years, machine learning has received increased interest both a...

0 Andrei Paleyes, et al. ∙

research

∙ 04/27/2020

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

We propose a meta-learning approach that learns from multiple tasks in a...

5 Shell Xu Hu, et al. ∙

research

∙ 09/19/2019

Differentially Private Regression and Classification with Sparse Gaussian Processes

A continuing challenge for machine learning is providing methods to perf...

12 Michael Thomas Smith, et al. ∙

research

∙ 04/11/2019

Variational Information Distillation for Knowledge Transfer

Transferring knowledge from a teacher neural network pretrained on the s...

6 Sungsoo Ahn, et al. ∙

research

∙ 03/26/2019

Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design

Machine learning solutions, in particular those based on deep learning m...

20 Neil D. Lawrence, et al. ∙

research

∙ 12/03/2018

Transferring Knowledge across Learning Processes

In complex transfer learning scenarios new tasks might not be tightly li...

126 Sebastian Flennerhag, et al. ∙

research

∙ 09/06/2018

Gaussian Process Regression for Binned Data

Many datasets are in the form of tables of binned data. Performing regre...

0 Michael Thomas Smith, et al. ∙

research

∙ 10/24/2017

Auto-Differentiating Linear Algebra

Development systems for deep learning, such as Theano, Torch, TensorFlow...

0 Matthias Seeger, et al. ∙

research

∙ 09/15/2017

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

This paper is concerned with estimation and stochastic control in physic...

0 Simo Särkkä, et al. ∙

research

∙ 05/27/2017

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes

Often in machine learning, data are collected as a combination of multip...

0 Zhenwen Dai, et al. ∙

research

∙ 05/22/2017

Living Together: Mind and Machine Intelligence

In this paper we consider the nature of the machine intelligences we hav...

0 Neil D. Lawrence, et al. ∙

research

∙ 05/05/2017

Data Readiness Levels

Application of models to data is fraught. Data-generating collaborators ...

0 Neil D. Lawrence, et al. ∙

research

∙ 04/12/2017

Preferential Bayesian Optimization

Bayesian optimization (BO) has emerged during the last few years as an e...

0 Javier Gonzalez, et al. ∙

research

∙ 01/12/2017

Manifold Alignment Determination: finding correspondences across different data views

We present Manifold Alignment Determination (MAD), an algorithm for lear...

0 Andreas Damianou, et al. ∙

research

∙ 06/02/2016

Differentially Private Gaussian Processes

A major challenge for machine learning is increasing the availability of...

0 Michael Thomas Smith, et al. ∙

research

∙ 04/18/2016

Chained Gaussian Processes

Gaussian process models are flexible, Bayesian non-parametric approaches...

0 Alan D. Saul, et al. ∙

research

∙ 04/17/2016

Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis

Factor analysis aims to determine latent factors, or traits, which summa...

0 Andreas Damianou, et al. ∙

research

∙ 11/20/2015

Recurrent Gaussian Processes

We define Recurrent Gaussian Processes (RGP) models, a general family of...

0 César Lincoln C. Mattos, et al. ∙

research

∙ 10/21/2015

GLASSES: Relieving The Myopia Of Bayesian Optimisation

We present GLASSES: Global optimisation with Look-Ahead through Stochast...

0 Javier Gonzalez, et al. ∙

research

∙ 09/03/2015

Semi-described and semi-supervised learning with Gaussian processes

Propagating input uncertainty through non-linear Gaussian process (GP) m...

0 Andreas Damianou, et al. ∙

research

∙ 05/29/2015

Batch Bayesian Optimization via Local Penalization

The popularity of Bayesian optimization methods for efficient exploratio...

0 Javier Gonzalez, et al. ∙

research

∙ 05/07/2015

Bayesian Optimization for Synthetic Gene Design

We address the problem of synthetic gene design using Bayesian optimizat...

0 Javier Gonzalez, et al. ∙

research

∙ 12/03/2014

Nested Variational Compression in Deep Gaussian Processes

Deep Gaussian processes provide a flexible approach to probabilistic mod...

0 James Hensman, et al. ∙

research

∙ 11/27/2014

Metrics for Probabilistic Geometries

We investigate the geometrical structure of probabilistic generative dim...

0 Alessandra Tosi, et al. ∙

research

∙ 09/08/2014

Variational Inference for Uncertainty on the Inputs of Gaussian Process Models

The Gaussian process latent variable model (GP-LVM) provides a flexible ...

0 Andreas C. Damianou, et al. ∙

research

∙ 01/08/2014

Fast nonparametric clustering of structured time-series

In this publication, we combine two Bayesian non-parametric models: the ...

0 James Hensman, et al. ∙

research

∙ 09/26/2013

Gaussian Processes for Big Data

We introduce stochastic variational inference for Gaussian process model...

0 James Hensman, et al. ∙

research

∙ 01/30/2013

Mixture Representations for Inference and Learning in Boltzmann Machines

Boltzmann machines are undirected graphical models with two-state stocha...

0 Neil D. Lawrence, et al. ∙

research

∙ 11/02/2012

Deep Gaussian Processes

In this paper we introduce deep Gaussian process (GP) models. Deep GPs a...

0 Andreas C. Damianou, et al. ∙

research

∙ 06/22/2012

Fast Variational Inference in the Conjugate Exponential Family

We present a general method for deriving collapsed variational inference...

0 James Hensman, et al. ∙

research

∙ 07/25/2011

Variational Gaussian Process Dynamical Systems

High dimensional time series are endemic in applications of machine lear...

0 Andreas C. Damianou, et al. ∙

research

∙ 07/13/2011

Linear Latent Force Models using Gaussian Processes

Purely data driven approaches for machine learning present difficulties ...

0 Mauricio A. Álvarez, et al. ∙

Neil D. Lawrence

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