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04/24/2023
Causal fault localisation in dataflow systems
Dataflow computing was shown to bring significant benefits to multiple n...
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04/15/2023
Dimensionality Reduction as Probabilistic Inference
Dimensionality reduction (DR) algorithms compress high-dimensional data ...
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03/16/2023
Dataflow graphs as complete causal graphs
Component-based development is one of the core principles behind modern ...
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03/07/2023
AI for Science: An Emerging Agenda
This report documents the programme and the outcomes of Dagstuhl Seminar...
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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...
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10/29/2022
Ice Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate. However, before...
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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...
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06/13/2022
Modeling the Machine Learning Multiverse
Amid mounting concern about the reliability and credibility of machine l...
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05/26/2022
The Effect of Task Ordering in Continual Learning
We investigate the effect of task ordering on continual learning perform...
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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 ...
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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...
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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...
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11/20/2021
Bayesian Learning via Neural Schrödinger-Föllmer Flows
In this work we explore a new framework for approximate Bayesian inferen...
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10/25/2021
Emulation of physical processes with Emukit
Decision making in uncertain scenarios is an ubiquitous challenge in rea...
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10/20/2021
Behavioral Experiments for Understanding Catastrophic Forgetting
In this paper we explore whether the fundamental tool of experimental ps...
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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...
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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...
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06/03/2021
Solving Schrödinger Bridges via Maximum Likelihood
The Schrödinger bridge problem (SBP) finds the most likely stochastic ev...
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11/18/2020
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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04/27/2020
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
We propose a meta-learning approach that learns from multiple tasks in a...
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09/19/2019
Differentially Private Regression and Classification with Sparse Gaussian Processes
A continuing challenge for machine learning is providing methods to perf...
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04/11/2019
Variational Information Distillation for Knowledge Transfer
Transferring knowledge from a teacher neural network pretrained on the s...
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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...
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12/03/2018
Transferring Knowledge across Learning Processes
In complex transfer learning scenarios new tasks might not be tightly li...
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09/06/2018
Gaussian Process Regression for Binned Data
Many datasets are in the form of tables of binned data. Performing regre...
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10/24/2017
Auto-Differentiating Linear Algebra
Development systems for deep learning, such as Theano, Torch, TensorFlow...
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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...
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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...
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05/22/2017
Living Together: Mind and Machine Intelligence
In this paper we consider the nature of the machine intelligences we hav...
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05/05/2017
Data Readiness Levels
Application of models to data is fraught. Data-generating collaborators ...
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04/12/2017
Preferential Bayesian Optimization
Bayesian optimization (BO) has emerged during the last few years as an e...
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01/12/2017
Manifold Alignment Determination: finding correspondences across different data views
We present Manifold Alignment Determination (MAD), an algorithm for lear...
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06/02/2016
Differentially Private Gaussian Processes
A major challenge for machine learning is increasing the availability of...
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04/18/2016
Chained Gaussian Processes
Gaussian process models are flexible, Bayesian non-parametric approaches...
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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...
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11/20/2015
Recurrent Gaussian Processes
We define Recurrent Gaussian Processes (RGP) models, a general family of...
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10/21/2015
GLASSES: Relieving The Myopia Of Bayesian Optimisation
We present GLASSES: Global optimisation with Look-Ahead through Stochast...
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09/03/2015
Semi-described and semi-supervised learning with Gaussian processes
Propagating input uncertainty through non-linear Gaussian process (GP) m...
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05/29/2015
Batch Bayesian Optimization via Local Penalization
The popularity of Bayesian optimization methods for efficient exploratio...
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05/07/2015
Bayesian Optimization for Synthetic Gene Design
We address the problem of synthetic gene design using Bayesian optimizat...
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12/03/2014
Nested Variational Compression in Deep Gaussian Processes
Deep Gaussian processes provide a flexible approach to probabilistic mod...
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11/27/2014
Metrics for Probabilistic Geometries
We investigate the geometrical structure of probabilistic generative dim...
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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 ...
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01/08/2014
Fast nonparametric clustering of structured time-series
In this publication, we combine two Bayesian non-parametric models: the ...
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09/26/2013
Gaussian Processes for Big Data
We introduce stochastic variational inference for Gaussian process model...
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01/30/2013
Mixture Representations for Inference and Learning in Boltzmann Machines
Boltzmann machines are undirected graphical models with two-state stocha...
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11/02/2012
Deep Gaussian Processes
In this paper we introduce deep Gaussian process (GP) models. Deep GPs a...
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06/22/2012
Fast Variational Inference in the Conjugate Exponential Family
We present a general method for deriving collapsed variational inference...
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07/25/2011
Variational Gaussian Process Dynamical Systems
High dimensional time series are endemic in applications of machine lear...
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07/13/2011
