Search Results for author: Neil D. Lawrence

Found 59 papers, 22 papers with code

Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation

no code implementations21 Jan 2024 Christian Cabrera, Andrei Paleyes, Neil D. Lawrence

S4 builds knowledge loops between all available knowledge sources that define modern software systems to improve their interpretability and adaptability.

Decision Making

Automated discovery of trade-off between utility, privacy and fairness in machine learning models

no code implementations27 Nov 2023 Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes

Thus the trade-off between fairness, privacy and performance of ML models emerges, and practitioners need a way of quantifying this trade-off to enable deployment decisions.

Bayesian Optimization Decision Making +1

Causal fault localisation in dataflow systems

1 code implementation24 Apr 2023 Andrei Paleyes, Neil D. Lawrence

Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-driven application development.

Causal Inference

Dimensionality Reduction as Probabilistic Inference

no code implementations15 Apr 2023 Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence

Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data.

Dimensionality Reduction Gaussian Processes +1

Dataflow graphs as complete causal graphs

1 code implementation16 Mar 2023 Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence

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

AI for Science: An Emerging Agenda

no code implementations7 Mar 2023 Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike Von Luxburg, Jessica Montgomery

This report summarises the discussions from the seminar and provides a roadmap to suggest how different communities can collaborate to deliver a new wave of progress in AI and its application for scientific discovery.

Modeling the Machine Learning Multiverse

no code implementations13 Jun 2022 Samuel J. Bell, Onno P. Kampman, Jesse Dodge, Neil D. Lawrence

Amid mounting concern about the reliability and credibility of machine learning research, we present a principled framework for making robust and generalizable claims: the multiverse analysis.

BIG-bench Machine Learning Experimental Design

The Effect of Task Ordering in Continual Learning

no code implementations26 May 2022 Samuel J. Bell, Neil D. Lawrence

Connecting to the field of curriculum learning, we show that the effect of task ordering can be exploited to modify continual learning performance, and present a simple approach for doing so.

Continual Learning Experimental Design

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

1 code implementation27 Apr 2022 Andrei Paleyes, Christian Cabrera, Neil D. Lawrence

Data Oriented Architecture (DOA) is an emerging approach that can support data scientists and software developers when addressing such challenges.

BIG-bench Machine Learning

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

1 code implementation15 Mar 2022 Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo

Unfortunately, the original Bigraphical Lasso algorithm is not applicable in case of large p and n due to memory requirements.

Vocal Bursts Valence Prediction

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

no code implementations25 Feb 2022 Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence

We show how this framework is compatible with different latent variable formulations and perform experiments to compare a suite of models.

Benchmarking Dimensionality Reduction +2

Bayesian Learning via Neural Schrödinger-Föllmer Flows

no code implementations pproximateinference AABI Symposium 2022 Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken

In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control (i. e. Schr\"odinger bridges).

Bayesian Inference

Behavioral Experiments for Understanding Catastrophic Forgetting

no code implementations20 Oct 2021 Samuel J. Bell, Neil D. Lawrence

In this paper we explore whether the fundamental tool of experimental psychology, the behavioral experiment, has the power to generate insight not only into humans and animals, but artificial systems too.

Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment

1 code implementation20 Sep 2021 Corinna Cortes, Neil D. Lawrence

Further, with seven years passing since the experiment we find that for \emph{accepted} papers, there is no correlation between quality scores and impact of the paper as measured as a function of citation count.

Towards better data discovery and collection with flow-based programming

1 code implementation9 Aug 2021 Andrei Paleyes, Christian Cabrera, Neil D. Lawrence

Our main conclusion is that FBP shows great potential for providing data-centric infrastructural benefits for deployment of ML.

Management Self-Driving Cars

Solving Schrödinger Bridges via Maximum Likelihood

1 code implementation3 Jun 2021 Francisco Vargas, Pierre Thodoroff, Neil D. Lawrence, Austen Lamacraft

The Schr\"odinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution.

BIG-bench Machine Learning

Challenges in Deploying Machine Learning: a Survey of Case Studies

2 code implementations18 Nov 2020 Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence

In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems.

BIG-bench Machine Learning

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

2 code implementations ICLR 2020 Shell Xu Hu, Pablo G. Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas Damianou

The evidence lower bound of the marginal log-likelihood of empirical Bayes decomposes as a sum of local KL divergences between the variational posterior and the true posterior on the query set of each task.

Few-Shot Image Classification Meta-Learning +3

Differentially Private Regression and Classification with Sparse Gaussian Processes

no code implementations19 Sep 2019 Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence

We experiment with the use of inducing points to provide a sparse approximation and show that these can provide robust differential privacy in outlier areas and at higher dimensions.

Classification Gaussian Processes +2

Modular Deep Probabilistic Programming

1 code implementation ICLR 2019 Zhenwen Dai, Eric Meissner, Neil D. Lawrence

A probabilistic module consists of a set of random variables with associated probabilistic distributions and dedicated inference methods.

Probabilistic Programming Variational Inference

Variational Information Distillation for Knowledge Transfer

2 code implementations CVPR 2019 Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai

We further demonstrate the strength of our method on knowledge transfer across heterogeneous network architectures by transferring knowledge from a convolutional neural network (CNN) to a multi-layer perceptron (MLP) on CIFAR-10.

Knowledge Distillation Transfer Learning

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

no code implementations26 Mar 2019 Neil D. Lawrence

Machine learning solutions, in particular those based on deep learning methods, form an underpinning of the current revolution in "artificial intelligence" that has dominated popular press headlines and is having a significant influence on the wider tech agenda.

BIG-bench Machine Learning

Transferring Knowledge across Learning Processes

4 code implementations ICLR 2019 Sebastian Flennerhag, Pablo G. Moreno, Neil D. Lawrence, Andreas Damianou

Approaches that transfer information contained only in the final parameters of a source model will therefore struggle.

Meta-Learning Transfer Learning

Auto-Differentiating Linear Algebra

no code implementations24 Oct 2017 Matthias Seeger, Asmus Hetzel, Zhenwen Dai, Eric Meissner, Neil D. Lawrence

Development systems for deep learning (DL), such as Theano, Torch, TensorFlow, or MXNet, are easy-to-use tools for creating complex neural network models.

Active Learning Bayesian Optimization +1

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

no code implementations15 Sep 2017 Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence

This article is concerned with learning and stochastic control in physical systems which contain unknown input signals.

Gaussian Processes

Preferential Bayesian Optmization

no code implementations ICML 2017 Javier González, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence

We present a new framework for this scenario that we call Preferential Bayesian Optimization (PBO) and that allows to find the optimum of a latent function that can only be queried through pairwise comparisons, so-called duels.

Bayesian Optimization Recommendation Systems

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes

no code implementations NeurIPS 2017 Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence

Often in machine learning, data are collected as a combination of multiple conditions, e. g., the voice recordings of multiple persons, each labeled with an ID.

Gaussian Processes Test +1

Living Together: Mind and Machine Intelligence

no code implementations22 May 2017 Neil D. Lawrence

In this paper we consider the nature of the machine intelligences we have created in the context of our human intelligence.

Data Readiness Levels

1 code implementation5 May 2017 Neil D. Lawrence

Application of models to data is fraught.


Preferential Bayesian Optimization

no code implementations12 Apr 2017 Javier Gonzalez, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence

Bayesian optimization (BO) has emerged during the last few years as an effective approach to optimizing black-box functions where direct queries of the objective are expensive.

Bayesian Optimization Recommendation Systems

Manifold Alignment Determination: finding correspondences across different data views

no code implementations12 Jan 2017 Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek

We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities.

The Emergence of Organizing Structure in Conceptual Representation

1 code implementation28 Nov 2016 Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum

While this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge.

Inductive Bias

Differentially Private Gaussian Processes

no code implementations2 Jun 2016 Michael Thomas Smith, Max Zwiessele, Neil D. Lawrence

A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals.

Gaussian Processes regression

Chained Gaussian Processes

1 code implementation18 Apr 2016 Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

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

Additive models Gaussian Processes

Recurrent Gaussian Processes

1 code implementation20 Nov 2015 César Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence

We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric models with recurrent GP priors which are able to learn dynamical patterns from sequential data.

Gaussian Processes

GLASSES: Relieving The Myopia Of Bayesian Optimisation

no code implementations21 Oct 2015 Javier González, Michael Osborne, Neil D. Lawrence

We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search.

Bayesian Optimisation

Nested Variational Compression in Deep Gaussian Processes

no code implementations3 Dec 2014 James Hensman, Neil D. Lawrence

Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning.

Gaussian Processes Variational Inference

Metrics for Probabilistic Geometries

no code implementations27 Nov 2014 Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence

We investigate the geometrical structure of probabilistic generative dimensionality reduction models using the tools of Riemannian geometry.

Dimensionality Reduction

Variational Inference for Uncertainty on the Inputs of Gaussian Process Models

no code implementations8 Sep 2014 Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied.

Dimensionality Reduction Gaussian Processes +1

Fast nonparametric clustering of structured time-series

no code implementations8 Jan 2014 James Hensman, Magnus Rattray, Neil D. Lawrence

In this publication, we combine two Bayesian non-parametric models: the Gaussian Process (GP) and the Dirichlet Process (DP).

Clustering Nonparametric Clustering +3

Gaussian Processes for Big Data

8 code implementations26 Sep 2013 James Hensman, Nicolo Fusi, Neil D. Lawrence

We introduce stochastic variational inference for Gaussian process models.

Gaussian Processes Variational Inference

Fast Variational Inference in the Conjugate Exponential Family

no code implementations NeurIPS 2012 James Hensman, Magnus Rattray, Neil D. Lawrence

We present a general method for deriving collapsed variational inference algorithms for probabilistic models in the conjugate exponential family.

Variational Inference

Variational Gaussian Process Dynamical Systems

no code implementations NeurIPS 2011 Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence

Our work builds on recent variational approximations for Gaussian process latent variable models to allow for nonlinear dimensionality reduction simultaneously with learning a dynamical prior in the latent space.

Dimensionality Reduction Time Series +1

Linear Latent Force Models using Gaussian Processes

no code implementations13 Jul 2011 Mauricio A. Álvarez, David Luengo, Neil D. Lawrence

Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate.

Gaussian Processes

Switched Latent Force Models for Movement Segmentation

no code implementations NeurIPS 2010 Mauricio Alvarez, Jan R. Peters, Neil D. Lawrence, Bernhard Schölkopf

Latent force models encode the interaction between multiple related dynamical systems in the form of a kernel or covariance function.

Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes

no code implementations NeurIPS 2008 Ben Calderhead, Mark Girolami, Neil D. Lawrence

We demonstrate the speed and statistical accuracy of our approach using examples of both ordinary and delay differential equations, and provide a comprehensive comparison with current state of the art methods.

Bayesian Inference Gaussian Processes +2

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