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

:

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):

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:

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

:

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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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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Causal fault localisation in dataflow systems

Andrei Paleyes, Neil D. Lawrence

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

Publications

Publications

Model Comparison for Semantic Grouping

Multilingual Factor Analysis

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

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

Sparse Gaussian Processes with Spherical Harmonic Features

Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation

Democratising the Digital Revolution: The Role of Data Governance

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

Decision-making with Uncertainty

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

Gaussian Process Latent Variable Flows for Massively Missing Data

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

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

Exploring Legal Mechanisms for Data Stewardship

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

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

A Research Agenda for Data Trusts (Working Paper 3)

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

Solving Schrödinger Bridges via Maximum Likelihood

Deep learning for Bioimage Analysis in Developmental Biology

Differentially Private Regression and Classification with Sparse Gaussian Processes

Efficient Representations for Privacy-Preserving Inference

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

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

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

Benchmarking Real-Time Reinforcement Learning

Towards Better Data Discovery and Collection with Flow-Based Programming

Shooting Schrödinger's Cat

Adversarial Concept Erasure in Kernel Space

Bayesian Learning via Neural Schrödinger-Föllmer Flows

Challenges in Machine Learning Deployment: A Survey of Case Studies

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

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

Behavioral experiments for understanding catastrophic forgetting

Desiderata for next generation of ML model serving

Modeling the Machine Learning Multiverse

Bayesian learning via neural Schrödinger–Föllmer flows

The UK Large Language Models Opportunity

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

Dataflow graphs as complete causal graphs

The UK foundation Models Opportunity

Causal fault localisation in dataflow systems

Letter Warning about Simplistic Narratives around AI