Publications

Model Comparison for Semantic Grouping

Francisco Vargas, Kamen Brestnichki, Nils Hammerla

Proceedings of the 36th International Conference on Machine Learning, :

Multilingual Factor Analysis

Francisco Vargas, Kamen Brestnichki, Alex Papadopoulos-Korfiatis, Nils Hammerla

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, :

Bottom-up Data Trusts: Disturbing the 'One Size Fits All' Approach to Data Governance

Sylvie Delacroix, Neil D. Lawrence

International Data Privacy Law, Oxford Academic 9(4):236-252

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas Damianou

International Conference on Learning Representations, :

Sparse Gaussian Processes with Spherical Harmonic Features

Vincent Dutordoir, Nicolas Durrande, James Hensman

Proceedings of the 37th International Conference on Machine Learning, :

Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation

Francisco Vargas, Ryan Cotterell

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, :

Democratising the Digital Revolution: The Role of Data Governance

Sylvie Delacroix, Joelle Pineau, Jessica Montgomery

Reflections on Artificial Intelligence for Humanity, :

Data trusts: from theory to practice (Working Paper 1)

Data Trusts Initiative

The Data Trusts Initiative:

Decision-making with Uncertainty

Peter J. Diggle, Tim Gowers, Frank Kelly, Neil D. Lawrence

Significance, 17(6):12-12

From Research Data Ethics Principles to Practice: Data Trusts as a Governance Tool

Sylvie Delacroix, Jessica Montgomery

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Gaussian Process Latent Variable Flows for Massively Missing Data

Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence

Third Symposium on Advances in Approximate Bayesian Inference, :

International Perspectives on the Development of Data Institutions (Working Paper 2)

Data Trusts Initiative

The Data Trusts Initiative:

Optimal marker gene selection for cell type discrimination in single cell analyses

Bianca Dumitrascu, Soledad Villar, Dustin G. Mixon, Barbara Englehardt

Nature Communications, 12(1186):

Exploring Legal Mechanisms for Data Stewardship

The Ada Lovelace Institute, The AI Council, The Data Trusts Initiative

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Creating a European AI Powerhouse: A Strategic Research Agenda from the European Learning and Intelligent Systems Excellence (ELISE) consortium

ELISE Network

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

Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek

Journal of Machine Learning Research, 22(8):1-51

A Research Agenda for Data Trusts (Working Paper 3)

Data Trusts Initiative

The Data Trusts Initiative:

Data Governance in the 21st century: Citizen Dialogue and the Development of Data Trusts

Jessica Montgomery, Neil D. Lawrence

Future Directions for Citizen Science and Public Policy, CSaP:

Solving Schrödinger Bridges via Maximum Likelihood

Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, Neil D. Lawrence

Entropy, 23(9):1134

Deep learning for Bioimage Analysis in Developmental Biology

Adrien Hallou, Hannah G. Yevick, Bianca Dumitrascu, Virginie Uhlmann

Development, 148(18):

Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment

Corinna Cortes, Neil D. Lawrence

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Differentially Private Regression and Classification with Sparse Gaussian Processes

Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence

Journal of Machine Learning Research, 22(188):1-41

Efficient Representations for Privacy-Preserving Inference

Han Xuanyuan, Francisco Vargas, Stephen Cummins

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Multimodal Graph Coarsening for Interpretable, MRI-Based Brain Graph Neural Network

Isaac Sebenius, Alexander Campbell, Sarah E. Morgan, Edward T. Bullmore, Pietro Liò

IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), :

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande

Advances in Neural Information Processing Systems 34 (NeurIPS 2021), :

Natural Language Processing markers in First Episode Psychosis and People at Clinical High-risk

Sarah E. Morgan, Kelly Diederen, Petra E. Vértes, Samantha H. Y. Ip, Bo Wang, Bethany Thompson, Arsime Demjaha, Andrea De Micheli, Dominic Oliver, Maria Liakata, Paolo Fusar-Poli, Tom J. Spencer, Philip McGuire

Translational Psychiatry, 11(630):

Benchmarking Real-Time Reinforcement Learning

Pierre Thodoroff, Wenyu Li, Neil D. Lawrence

Pre-registration Workshop at NeurIPS 2021, :

Towards Better Data Discovery and Collection with Flow-Based Programming

Andrei Paleyes, Christian Cabrera, Neil D. Lawrence

Neurips Data-Centric AI Workshop (DCAI), :

Shooting Schrödinger's Cat

David Lopes Fernandes, Francisco Vargas, Carl Henrik Ek, Neill D. F. Campbell

Fourth Symposium on Advances in Approximate Bayesian Inference, :

Adversarial Concept Erasure in Kernel Space

Shauli Ravfogel, Francisco Vargas, Yoav Goldberg, Ryan Cotterell

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Bayesian Learning via Neural Schrödinger-Föllmer Flows

Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken

Fourth Symposium on Advances in Approximate Bayesian Inference, :

Challenges in Machine Learning Deployment: A Survey of Case Studies

Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence

ACM Comput. Surv., Association for Computing Machinery:

Challenges in Machine Learning Deployment: A Survey of Case Studies

Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence

ACM Comput. Surv., Association for Computing Machinery:

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

Andrei Paleyes, Christian Cabrera, Neil D. Lawrence

1st International Conference on AI Engineering – Software Engineering for AI, :

Machine Learning from Innovation to Deployment: A Strategic Research Agenda for AutoAI

The AutoAI Research Team

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

Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G.H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence

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Behavioral experiments for understanding catastrophic forgetting

Samuel J. Bell, Neil D. Lawrence

AI Evaluation Beyond Metrics (EBeM), IJCAI, :

Desiderata for next generation of ML model serving

Sherif Akoush, Andrei Paleyes, Arnaud van Looveren, Clive Cox

NeurIPS Workshop on Challenges in Deploying and Monitoring Machine Learning Systems (DMML), :

Modeling the Machine Learning Multiverse

Samuel J. Bell, Onno P. Kampman, Jesse Dodge, Neil D. Lawrence

Advances in Neural Information Processing Systems (NeurIPS), :

Bayesian learning via neural Schrödinger–Föllmer flows

Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken

Statistics and Computing, 33(3):

The UK Large Language Models Opportunity

The AI Council

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

Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence

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AI for Science: an emerging agenda

Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery

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Dataflow graphs as complete causal graphs

Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence

2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering Approaches, :

Dataflow graphs as complete causal graphs

Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence

2nd International Conference on AI Engineering – Software Engineering for AI, :

The UK foundation Models Opportunity

The AI Council

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Dimensionality Reduction as Probabilistic Inference

Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence

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Predicting Ruthenium Catalysed Hydrogenation of Esters Using Machine Learning

Challenger Mishra, Niklas von Wolff, Abhinav Tripathi, Claire N. Brodie, Neil D. Lawrence, Aditya Ravuri, Éric Brémond, Annika Preiss, Amit Kumar

Digital Discovery, RSC 2:

Causal fault localisation in dataflow systems

Andrei Paleyes, Neil D. Lawrence

Proceedings of the 3rd Workshop on Machine Learning and Systems (EuroMLSys), :

Letter Warning about Simplistic Narratives around AI

Neil D. Lawrence

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Multi-fidelity experimental design for ice-sheet simulation

Pierre Thodoroff, Markus Kaiser, Rosie Williams, Robert Arthern, Scott Hosking, Neil D. Lawrence, James Byrne, Ieva Kazlauskaite

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

Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes

3rd Workshop on Bias and Fairness in AI (BIAS), ECML 2023, :

Self-sustaining software systems (S4): Towards improved interpretability and adaptation

Christian Cabrera, Andrei Paleyes, Neil D. Lawrence

Proceedings of the 1st International Workshop on New Trends in Software Engineering, :

Enhancing patient stratification and interpretability through class-contrastive and feature attribution techniques

Sharday Olowu, Neil D. Lawrence, Soumya Banerjee

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Scalable Amortized GPLVMs for Single Cell Transcriptomics Data

Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence

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Requirements are All You Need: The Final Frontier for End-User Software Engineering

Diana Robinson, Christian Cabrera, Andrew D. Gordon, Neil D. Lawrence, Lars Mennen

ACM Transactions on Software Engineering and Methodology, :

Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE

Aditya Ravuri, Neil D. Lawrence

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The Atomic Human: Understanding ourselves in the age of AI

Neil D. Lawrence

Allen Lane:

Can causality accelerate experimentation in software systems?

Andrei Paleyes, Han-Bo Li, Neil D. Lawrence

Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering, :

On Feature Learning for Titi Monkey Activity Detection

Aditya Ravuri, Jen Muir, Neil D. Lawrence

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Accelerating AI for science: open data science for science

Neil D. Lawrence, Jessica Montgomery

Royal Society Open Science, 11(8):

The Systems Engineering Approach in Times of Large Language Models

Christian Cabrera, Victor Bastidas, Jennifer Schooling, Neil D. Lawrence

58th Hawaii International Conference on System Sciences (HICSS-58), :

Increasing data sharing and use for social good: Lessons from Africa’s data-sharing practices during the COVID-19 response

Morine Amutorine, Neil D. Lawrence, Jessica Montgomery

Data & Policy, 6: