
Behavioral Experiments for Understanding Catastrophic Forgetting
In this paper we explore whether the fundamental tool of experimental ps...
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Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment
In this paper we revisit the 2014 NeurIPS experiment that examined incon...
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Exploring the potential of flowbased programming for machine learning deployment in comparison with serviceoriented architectures
Despite huge successes reported by the field of machine learning, such a...
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Solving Schrödinger Bridges via Maximum Likelihood
The Schrödinger bridge problem (SBP) finds the most likely stochastic ev...
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Challenges in Deploying Machine Learning: a Survey of Case Studies
In recent years, machine learning has received increased interest both a...
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Empirical Bayes Transductive MetaLearning with Synthetic Gradients
We propose a metalearning approach that learns from multiple tasks in a...
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Differentially Private Regression and Classification with Sparse Gaussian Processes
A continuing challenge for machine learning is providing methods to perf...
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Variational Information Distillation for Knowledge Transfer
Transferring knowledge from a teacher neural network pretrained on the s...
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Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design
Machine learning solutions, in particular those based on deep learning m...
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Transferring Knowledge across Learning Processes
In complex transfer learning scenarios new tasks might not be tightly li...
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Gaussian Process Regression for Binned Data
Many datasets are in the form of tables of binned data. Performing regre...
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AutoDifferentiating Linear Algebra
Development systems for deep learning, such as Theano, Torch, TensorFlow...
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Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
This paper is concerned with estimation and stochastic control in physic...
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Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Often in machine learning, data are collected as a combination of multip...
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Living Together: Mind and Machine Intelligence
In this paper we consider the nature of the machine intelligences we hav...
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Data Readiness Levels
Application of models to data is fraught. Datagenerating collaborators ...
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Preferential Bayesian Optimization
Bayesian optimization (BO) has emerged during the last few years as an e...
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Manifold Alignment Determination: finding correspondences across different data views
We present Manifold Alignment Determination (MAD), an algorithm for lear...
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Differentially Private Gaussian Processes
A major challenge for machine learning is increasing the availability of...
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Chained Gaussian Processes
Gaussian process models are flexible, Bayesian nonparametric approaches...
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Multiview Learning as a Nonparametric Nonlinear InterBattery Factor Analysis
Factor analysis aims to determine latent factors, or traits, which summa...
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Recurrent Gaussian Processes
We define Recurrent Gaussian Processes (RGP) models, a general family of...
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GLASSES: Relieving The Myopia Of Bayesian Optimisation
We present GLASSES: Global optimisation with LookAhead through Stochast...
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Semidescribed and semisupervised learning with Gaussian processes
Propagating input uncertainty through nonlinear Gaussian process (GP) m...
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Batch Bayesian Optimization via Local Penalization
The popularity of Bayesian optimization methods for efficient exploratio...
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Bayesian Optimization for Synthetic Gene Design
We address the problem of synthetic gene design using Bayesian optimizat...
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Nested Variational Compression in Deep Gaussian Processes
Deep Gaussian processes provide a flexible approach to probabilistic mod...
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Metrics for Probabilistic Geometries
We investigate the geometrical structure of probabilistic generative dim...
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Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
The Gaussian process latent variable model (GPLVM) provides a flexible ...
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Fast nonparametric clustering of structured timeseries
In this publication, we combine two Bayesian nonparametric models: the ...
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Gaussian Processes for Big Data
We introduce stochastic variational inference for Gaussian process model...
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Mixture Representations for Inference and Learning in Boltzmann Machines
Boltzmann machines are undirected graphical models with twostate stocha...
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Deep Gaussian Processes
In this paper we introduce deep Gaussian process (GP) models. Deep GPs a...
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Fast Variational Inference in the Conjugate Exponential Family
We present a general method for deriving collapsed variational inference...
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Variational Gaussian Process Dynamical Systems
High dimensional time series are endemic in applications of machine lear...
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Linear Latent Force Models using Gaussian Processes
Purely data driven approaches for machine learning present difficulties ...
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Kernels for VectorValued Functions: a Review
Kernel methods are among the most popular techniques in machine learning...
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Residual Component Analysis
Probabilistic principal component analysis (PPCA) seeks a low dimensiona...
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A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models
We introduce a new perspective on spectral dimensionality reduction whic...
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Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes
Interest in multioutput kernel methods is increasing, whether under the ...
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Sparse Convolved Multiple Output Gaussian Processes
Recently there has been an increasing interest in methods that deal with...
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Neil D. Lawrence
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Director of Machine Learning, Amazon Research Cambridge and Professor of Machine Learning, University of Sheffield