Bayesian Radial Basis Functions of Variable Dimension. - Christopher C. Holmes; Bani K. Mallick (Neural Comput., 1998)

Bayesian wavelet networks for nonparametric regression. - Christopher C. Holmes; Bani K. Mallick (IEEE Trans. Neural Networks Learn. Syst., 2000)

Minimum-Entropy Data Clustering Using Reversible Jump Markov Chain Monte Carlo. - Stephen J. Roberts; Christopher C. Holmes; Dave Denison (ICANN, 2001)

Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo. - Stephen J. Roberts; Christopher C. Holmes; Dave Denison (IEEE Trans. Pattern Anal. Mach. Intell., 2001)

Classification with Bayesian MARS. - Christopher C. Holmes; Dave Denison (Mach. Learn., 2003)

On population-based simulation for static inference. - Ajay Jasra; David A. Stephens; Christopher C. Holmes (Stat. Comput., 2007)

GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population. - Eleni Giannoulatou; Christopher Yau; Stefano Colella; Jiannis Ragoussis; Christopher C. Holmes (Bioinform., 2008)

Interacting sequential Monte Carlo samplers for trans-dimensional simulation. - Ajay Jasra; Arnaud Doucet; David A. Stephens; Christopher C. Holmes (Comput. Stat. Data Anal., 2008)

A boosting approach to structure learning of graphs with and without prior knowledge. - Shahzia Anjum; Arnaud Doucet; Christopher C. Holmes (Bioinform., 2009)

Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency. - Jochen W. Klingelhoefer; Loukas Moutsianas; Christopher C. Holmes (Bioinform., 2009)

Phylogenetic inference under recombination using Bayesian stochastic topology selection. - Alex Webb; John M. Hancock; Christopher C. Holmes (Bioinform., 2009)

A Bayesian approach using covariance of single nucleotide polymorphism data to detect differences in linkage disequilibrium patterns between groups of individuals. - Taane G. Clark; Susana G. Campino; Elisa Anastasi; Sarah Auburn; Yik Y. Teo; Kerrin S. Small; Kirk A. Rockett; Dominic Kwiatkowski; Christopher C. Holmes (Bioinform., 2010)

Stochastic boosting algorithms. - Ajay Jasra; Christopher C. Holmes (Stat. Comput., 2011)

GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples. - Jean-Baptiste Cazier; Christopher C. Holmes; John Broxholme (Bioinform., 2012)

NucleoFinder: a statistical approach for the detection of nucleosome positions. - Jérémie Becker; Christopher Yau; John M. Hancock; Christopher C. Holmes (Bioinform., 2013)

Integrative network-based Bayesian analysis of diverse genomics data. - Wenting Wang; Veerabhadran Baladandayuthapani; Chris C. Holmes; Kim-Anh Do (BMC Bioinform., 2013)

Statistical Inference in Hidden Markov Models using $k$-segment Constraints. - Michalis K. Titsias; Christopher Yau; Christopher C. Holmes (CoRR, 2013)

Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach. - Rémi Bardenet; Arnaud Doucet; Christopher C. Holmes (ICML, 2014)

A review of homomorphic encryption and software tools for encrypted statistical machine learning. - Louis J. M. Aslett; Pedro M. Esperança; Chris C. Holmes (CoRR, 2015)

Encrypted statistical machine learning: new privacy preserving methods. - Louis J. M. Aslett; Pedro M. Esperança; Chris C. Holmes (CoRR, 2015)

PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification. - Babak Hodjat; Hormoz Shahrzad; Risto Miikkulainen; Lawrence Murray; Chris C. Holmes (GPTP, 2016)

Encrypted Accelerated Least Squares Regression. - Pedro M. Esperança; Louis J. M. Aslett; Chris C. Holmes (AISTATS, 2017)

Bayesian Boolean Matrix Factorisation. - Tammo Rukat; Christopher C. Holmes; Michalis K. Titsias; Christopher Yau (ICML, 2017)

On Markov chain Monte Carlo methods for tall data. - Rémi Bardenet; Arnaud Doucet; Christopher C. Holmes (J. Mach. Learn. Res., 2017)

Probabilistic Boolean Tensor Decomposition. - Tammo Rukat; Christopher C. Holmes; Christopher Yau (ICML, 2018)

Nonparametric learning from Bayesian models with randomized objective functions. - Simon Lyddon; Stephen Walker; Chris C. Holmes (NeurIPS, 2018)

TensOrMachine: Probabilistic Boolean Tensor Decomposition. - Tammo Rukat; Christopher C. Holmes; Christopher Yau (CoRR, 2018)

Semi-unsupervised Learning of Human Activity using Deep Generative Models. - Matthew Willetts; Aiden R. Doherty; Stephen J. Roberts; Chris C. Holmes (CoRR, 2018)

Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness. - Sebastian J. Vollmer; Bilal A. Mateen; Gergo Bohner; Franz J. Király; Rayid Ghani; Pall Jonsson; Sarah Cumbers; Adrian Jonas; Katherine S. L. McAllister; Puja Myles; David Granger; Mark Birse; et al. (CoRR, 2018)

Principles of Bayesian Inference Using General Divergence Criteria. - Jack Jewson; Jim Q. Smith; Chris C. Holmes (Entropy, 2018)

Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap. - Edwin Fong; Simon Lyddon; Chris C. Holmes (ICML, 2019)

Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels. - Matthew Willetts; Stephen J. Roberts; Christopher C. Holmes (CoRR, 2019)

Disentangling Improves VAEs' Robustness to Adversarial Attacks. - Matthew Willetts; Alexander Camuto; Stephen J. Roberts; Chris C. Holmes (CoRR, 2019)

Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders. - Matthew Willetts; Stephen J. Roberts; Chris C. Holmes (CoRR, 2019)

Regularising Deep Networks with DGMs. - Matthew Willetts; Alexander Camuto; Stephen J. Roberts; Chris C. Holmes (CoRR, 2019)

Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels. - Matthew Willetts; Stephen J. Roberts; Chris C. Holmes (IEEE BigData, 2020)

Explicit Regularisation in Gaussian Noise Injections. - Alexander Camuto; Matthew Willetts; Umut Simsekli; Stephen J. Roberts; Chris C. Holmes (NeurIPS, 2020)

Risk scoring calculation for the current NHSx contact tracing app. - Mark Briers; Marcos Charalambides; Chris C. Holmes (CoRR, 2020)

Neural Ensemble Search for Performant and Calibrated Predictions. - Sheheryar Zaidi; Arber Zela; Thomas Elsken; Chris C. Holmes; Frank Hutter; Yee Whye Teh (CoRR, 2020)

Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers. - Tom Lovett; Mark Briers; Marcos Charalambides; Radka Jersakova; James Lomax; Chris C. Holmes (CoRR, 2020)

Relaxed-Responsibility Hierarchical Discrete VAEs. - Matthew Willetts; Xenia Miscouridou; Stephen J. Roberts; Chris C. Holmes (CoRR, 2020)

Improving the quality of machine learning in health applications and clinical research. - Bilal A. Mateen; James Liley; Alastair K. Denniston; Chris C. Holmes; Sebastian J. Vollmer (Nat. Mach. Intell., 2020)

Learning Bijective Feature Maps for Linear ICA. - Alexander Camuto; Matthew Willetts; Chris C. Holmes; Brooks Paige; Stephen J. Roberts (AISTATS, 2021)

Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. - Alexander Camuto; Matthew Willetts; Stephen J. Roberts; Chris C. Holmes; Tom Rainforth (AISTATS, 2021)

Deep Generative Missingness Pattern-Set Mixture Models. - Sahra Ghalebikesabi; Rob Cornish; Chris C. Holmes; Luke J. Kelly (AISTATS, 2021)

Foundations of Bayesian Learning from Synthetic Data. - Harrison Wilde; Jack Jewson; Sebastian J. Vollmer; Chris C. Holmes (AISTATS, 2021)

Improving VAEs' Robustness to Adversarial Attack. - Matthew Willetts; Alexander Camuto; Tom Rainforth; Stephen J. Roberts; Christopher C. Holmes (ICLR, 2021)

Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. - Alexander Camuto; Xiaoyu Wang; Lingjiong Zhu; Chris C. Holmes; Mert Gürbüzbalaban; Umut Simsekli (ICML, 2021)

Multi-Facet Clustering Variational Autoencoders. - Fabian Falck; Haoting Zhang; Matthew Willetts; George Nicholson; Christopher Yau; Chris C. Holmes (NeurIPS, 2021)

Conformal Bayesian Computation. - Edwin Fong; Chris C. Holmes (NeurIPS, 2021)

On Locality of Local Explanation Models. - Sahra Ghalebikesabi; Lucile Ter-Minassian; Karla DiazOrdaz; Chris C. Holmes (NeurIPS, 2021)

Neural Ensemble Search for Uncertainty Estimation and Dataset Shift. - Sheheryar Zaidi; Arber Zela; Thomas Elsken; Chris C. Holmes; Frank Hutter; Yee Whye Teh (NeurIPS, 2021)

Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness. - Sahra Ghalebikesabi; Rob Cornish; Luke J. Kelly; Chris C. Holmes (CoRR, 2021)

Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale. - Sahra Ghalebikesabi; Harrison Wilde; Jack Jewson; Arnaud Doucet; Sebastian J. Vollmer; Chris C. Holmes (CoRR, 2021)

Neural score matching for high-dimensional causal inference. - Oscar Clivio; Fabian Falck; Brieuc Lehmann; George Deligiannidis; Chris C. Holmes (AISTATS, 2022)

A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs. - Fabian Falck; Christopher Williams; Dominic Danks; George Deligiannidis; Christopher Yau; Chris C. Holmes; Arnaud Doucet; Matthew Willetts (NeurIPS, 2022)

Mitigating statistical bias within differentially private synthetic data. - Sahra Ghalebikesabi; Harry Wilde; Jack Jewson; Arnaud Doucet; Sebastian J. Vollmer; Chris C. Holmes (UAI, 2022)

Density Estimation with Autoregressive Bayesian Predictives. - Sahra Ghalebikesabi; Chris C. Holmes; Edwin Fong; Brieuc Lehmann (CoRR, 2022)

A large-scale and PCR-referenced vocal audio dataset for COVID-19. - Jobie Budd; Kieran Baker; Emma Karoune; Harry Coppock; Selina Patel; Ana Tendero Cañadas; Alexander Titcomb; Richard Payne; David Hurley; Sabrina Egglestone; Lorraine Butler; Jonathon Mellor; et al. (CoRR, 2022)

Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers. - Harry Coppock; George Nicholson; Ivan Kiskin; Vasiliki Koutra; Kieran Baker; Jobie Budd; Richard Payne; Emma Karoune; David Hurley; Alexander Titcomb; Sabrina Egglestone; Ana Tendero Cañadas; et al. (CoRR, 2022)

Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19. - Davide Pigoli; Kieran Baker; Jobie Budd; Lorraine Butler; Harry Coppock; Sabrina Egglestone; Steven G. Gilmour; Chris C. Holmes; David Hurley; Radka Jersakova; Ivan Kiskin; Vasiliki Koutra; et al. (CoRR, 2022)

PWSHAP: A Path-Wise Explanation Model for Targeted Variables. - Lucile Ter-Minassian; Oscar Clivio; Karla DiazOrdaz; Robin J. Evans 002; Christopher C. Holmes (ICML, 2023)

Differentially Private Statistical Inference through β-Divergence One Posterior Sampling. - Jack Jewson; Sahra Ghalebikesabi; Chris C. Holmes (NeurIPS, 2023)

A Unified Framework for U-Net Design and Analysis. - Christopher Williams; Fabian Falck; George Deligiannidis; Chris C. Holmes; Arnaud Doucet; Saifuddin Syed (NeurIPS, 2023)

Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates. - Sahra Ghalebikesabi; Chris C. Holmes; Edwin Fong; Brieuc Lehmann (UAI, 2023)

Causal Falsification of Digital Twins. - Rob Cornish; Muhammad Faaiz Taufiq; Arnaud Doucet; Chris C. Holmes (CoRR, 2023)

Learning from data with structured missingness. - Robin Mitra; Sarah F. McGough; Tapabrata Chakraborti; Chris C. Holmes; Ryan Copping; Niels Hagenbuch; Stefanie Biedermann; Jack Noonan; Brieuc Lehmann; Aditi Shenvi; Xuan Vinh Doan; David Leslie; et al. (Nat. Mac. Intell., 2023)

Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective. - Fabian Falck; Ziyu Wang; Christopher C. Holmes (ICML, 2024)

Towards Representation Learning for Weighting Problems in Design-Based Causal Inference. - Oscar Clivio; Avi Feller; Chris C. Holmes (UAI, 2024)

Explainable AI for survival analysis: a median-SHAP approach. - Lucile Ter-Minassian; Sahra Ghalebikesabi; Karla Diaz-Ordaz; Chris C. Holmes (CoRR, 2024)

On Uncertainty Quantification for Near-Bayes Optimal Algorithms. - Ziyu Wang; Chris Holmes 001 (CoRR, 2024)

Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers. - Harry Coppock; George Nicholson; Ivan Kiskin; Vasiliki Koutra; Kieran Baker; Jobie Budd; Richard Payne; Emma Karoune; David Hurley; Alexander Titcomb; Sabrina Egglestone; Ana Tendero Cañadas; et al. (Nat. Mac. Intell., 2024)

Publisher Correction: Development and assessment of a machine learning tool for predicting emergency admission in Scotland. - James Liley; Gergo Bohner; Samuel R. Emerson; Bilal A. Mateen; Katie Borland; David Carr; Scott Heald; Samuel D. Oduro; Jill Ireland; Keith Moffat; Rachel Porteous; Stephen Riddell; et al. (npj Digit. Medicine, 2024)

Development and assessment of a machine learning tool for predicting emergency admission in Scotland. - James Liley; Gergo Bohner; Samuel R. Emerson; Bilal A. Mateen; Katie Borland; David Carr; Scott Heald; Samuel D. Oduro; Jill Ireland; Keith Moffat; Rachel Porteous; Stephen Riddell; et al. (npj Digit. Medicine, 2024)

Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection. - Sam Dauncey; Christopher C. Holmes; Christopher Williams; Fabian Falck (Trans. Mach. Learn. Res., 2024)

Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition. - Jake Fawkes; Lucile Ter-Minassian; Desi R. Ivanova; Uri Shalit; Christopher C. Holmes (AISTATS, 2025)

Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation. - Lucile Ter-Minassian; Liran Szlak; Ehud Karavani; Christopher C. Holmes; Yishai Shimoni (AISTATS, 2025)

On Subjective Uncertainty Quantification and Calibration in Natural Language Generation. - Ziyu Wang; Christopher C. Holmes (AISTATS, 2025)