N. Lawrence

发表

Covariate Shift by Kernel Mean Matching

Karsten M. Borgwardt, B. Schölkopf, Alex Smola, 2009, NIPS 2009.

Gaussian Processes for Big Data through Stochastic Variational Inference

J. Hensman, N. Lawrence, 2012 .

Deep Gaussian Processes for Large Datasets

A. Damianou, J. Hensman, N. Lawrence, 2014 .

A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics

Neil D. Lawrence, Boaz Lerner, B. Lerner, 2001, Neural Computing & Applications.

A Variational B ayesian Committee of Neural Networks

Neil D. Lawrence, Mehdi Azzouzi, Neil D. Lawrence, 1999 .

Auto-Differentiating Linear Algebra pdf

Neil D. Lawrence, Zhenwen Dai, Matthias W. Seeger, 2017, ArXiv.

Markovian inference in belief networks

Charles M. Bishop, B. Frey, N. Lawrence, 1998 .

An Adversarial View of Covariate Shift and a Minimax Approach

Masashi Sugiyama, Joaquin Quiñonero-Candela, Anton Schwaighofer, 2009 .

Making Implementations Available for the Research Community

N. Lawrence, 2010 .

When Training and Test Sets Are Different: Characterizing Learning Transfer

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Generalised Component Analysis

Michael E. Tipping, Neil D. Lawrence, N. Lawrence, 2003 .

Gaussian process models for periodicity detection

Neil D. Lawrence, M. Rattray, J. Hensman, 2013, 1303.7090.

A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Latent Force Models: Bridging the Divide between Mechanistic and Data Modelling Paradigms

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2015 .

A variational approach to robust Bayesian interpolation

Neil D. Lawrence, Michael E. Tipping, N. Lawrence, 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

Identifying Submodules of Cellular Regulatory Networks

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2006, CMSB.

Flach , P . , & Kull , M . ( 2015 ) . Precision-Recall-Gain Curves : PR Analysis

D. D. Lee, N. Lawrence, C. Cortes, 2018 .

Precision-Recall-Gain Curves: PR Analysis Done Right

Garnett, Masashi Sugiyama, D. D. Lee, 2015 .

Linear latent force models using Gaussian processes.

Neil D. Lawrence, M. Álvarez, N. Lawrence, 2013, IEEE transactions on pattern analysis and machine intelligence.

A Probabilistic Model of Transcription Dynamics applied to Estrogen Signalling in Breast Cancer Cells

Neil D. Lawrence, M. Rattray, A. Honkela, 2013 .

Variational Inducing Kernels for Sparse Convolved Multiple Output G aussian Processes

M. Álvarez, N. Lawrence, M. Titsias, 2009, 0912.3268.

Advances in Intelligent Data Analysis XIV

Bernd Bischl, Neil D. Lawrence, Michèle Sebag, 2015, Lecture Notes in Computer Science.

MEDITERRANEAN CONFERENCE ON INTELLIGENT SYSTEMS AND AUTOMATION

Neil D. Lawrence, Chris Melhuish, Carl Henrik Ek, 2009 .

MOCAP Toolbox for MATLAB

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2005 .

Latent Force Models: Introduction

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2013 .

Gaussian Processes in Practice

Neil D. Lawrence, Anton Schwaighofer, Joaquin Quinonero Candela, 2007 .

Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning

Neil D. Lawrence, Mahesan Niranjan, Joab R. Winkler, 2004 .

Variational Inference in G aussian Processes via Probabilistic Point Assimilation

Neil D. Lawrence, Nathaniel J. King, N. Lawrence, 2005 .

Human Motion Modelling with Gaussian Processes

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2008 .

On Learning Decision Heuristics

Neil D. Lawrence, Daniel D. Lee, N. Lawrence, 2017 .

Advances in Neural Information Processing Systems Workshop on Learning from Multiple Sources

Neil D. Lawrence, Carl Henrik Ek, Philip Hs Torr, 2008 .

Conference on Biomimetic and Biohybrid Systems

Neil D. Lawrence, Tony J. Prescott, Carl Henrik Ek, 2015 .

Hierarchical G aussian Process Latent Variable Models

Neil D. Lawrence, Andrew J. Moore, Neil D. Lawrence, 2007, ICML 2007.

Gaussian Processes and the Null-Category Noise Model

Neil D. Lawrence, Michael I. Jordan, Neil D. Lawrence, 2006, Semi-Supervised Learning.

TP1: Leveraging Complex Prior Knowledge in Learning

Neil D. Lawrence, Marc Dymetman, Neil D. Lawrence, 2008 .

G aussian Processes for Big Data with Stochastic Variational Inference

Neil D. Lawrence, James Hensman, Neil D. Lawrence, 2012 .

Algorithms for Covariate Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

G aussian Process Models for Visualisation of High Dimensional Data

Neil D. Lawrence, N. Lawrence, 2004, NIPS 2004.

Gaussian Processes for Missing Species in Biochemical Systems

Neil D. Lawrence, Magnus Rattray, Michalis K. Titsias, 2010, Learning and Inference in Computational Systems Biology.

Sparse B ayesian Learning: The Informative Vector Machine

Neil D. Lawrence, Ralf Herbrich, Matthias Seeger, 2002 .

International Conference on Learning Representations Workshop track

Neil D. Lawrence, Zhenwen Dai, Carl Henrik Ek, 2016 .

The 3Ds of Machine Learning Systems Design

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2018 .

A Brief Introduction to B ayesian Inference

Neil D. Lawrence, Magnus Rattray, Neil D. Lawrence, 2010 .

On the Training/Test Distributions Gap: A Data Representation Learning Framework

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

A Brief Introduction to Bayesian Inference

Neil D. Lawrence, Magnus Rattray, Neil D. Lawrence, 2010, Learning and Inference in Computational Systems Biology.

Human Motion Modelling through Dimensional Reduction with Gaussian Processes

Neil D. Lawrence, Carl Henrik Ek, Philip H. S. Torr, 2008 .

International workshop on machine learning for multimodal interaction

Neil D. Lawrence, Grégory Rogez, Carl Henrik Ek, 2007 .

Modular Deep Probabilistic Programming

Neil D. Lawrence, Zhenwen Dai, Eric Meissner, 2018 .

When Training and Test Sets Are Different

Neil D. Lawrence, Masashi Sugiyama, Amos Storkey, 2008 .

The Structure of Neural Network Posteriors

Neil D. Lawrence, Mehdi Azzouzi, N. Lawrence, 2001 .

Introduction to Gaussian Processes

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2013 .

Advances in Neural Information Processing Systems 14

Neil D. Lawrence, Christopher M. Bishop, Michael J. Taylor, 2002 .

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

Neil D. Lawrence, N. Lawrence, Andrei Paleyes, 2022, 2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN).