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Christopher C. Holmes
Chris C. Holmes – Chris Holmes 0001
Person information
- affiliation: University of Oxford, Department of Statistics, UK
- affiliation: The Alan Turing Institute, London, UK
Other persons with the same name
- Chris Holmes — disambiguation page
- Chris Holmes 0002 — CGI IT, London, UK (and 1 more)
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Journal Articles
- 2024
- [j23]Harry Coppock, George Nicholson, Ivan Kiskin, Vasiliki Koutra, Kieran Baker
, Jobie Budd
Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers. Nat. Mac. Intell. 6(2): 229-242 (2024) - [j22]James Liley
Publisher Correction: Development and assessment of a machine learning tool for predicting emergency admission in Scotland. npj Digit. Medicine 7(1) (2024) - [j21]James Liley
Development and assessment of a machine learning tool for predicting emergency admission in Scotland. npj Digit. Medicine 7(1) (2024) - [j20]Sam Dauncey, Christopher C. Holmes, Christopher Williams, Fabian Falck:
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [j19]Robin Mitra
Learning from data with structured missingness. Nat. Mac. Intell. 5(1): 13-23 (2023) - 2020
- [j18]Bilal A. Mateen
Improving the quality of machine learning in health applications and clinical research. Nat. Mach. Intell. 2(10): 554-556 (2020) - 2018
- [j17]Jack Jewson
Principles of Bayesian Inference Using General Divergence Criteria. Entropy 20(6): 442 (2018) - 2017
- [j16]Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes:
On Markov chain Monte Carlo methods for tall data. J. Mach. Learn. Res. 18: 47:1-47:43 (2017) - 2013
- [j15]Jérémie Becker
NucleoFinder: a statistical approach for the detection of nucleosome positions. Bioinform. 29(6): 711-716 (2013) - [j14]Wenting Wang, Veerabhadran Baladandayuthapani, Chris C. Holmes
Integrative network-based Bayesian analysis of diverse genomics data. BMC Bioinform. 14(S-13): S8 (2013) - 2012
- [j13]Jean-Baptiste Cazier
GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples. Bioinform. 28(22): 2981-2982 (2012) - 2011
- [j12]Ajay Jasra
Stochastic boosting algorithms. Stat. Comput. 21(3): 335-347 (2011) - 2010
- [j11]Taane G. Clark
A Bayesian approach using covariance of single nucleotide polymorphism data to detect differences in linkage disequilibrium patterns between groups of individuals. Bioinform. 26(16): 1999-2003 (2010) - 2009
- [j10]Alex Webb
Phylogenetic inference under recombination using Bayesian stochastic topology selection. Bioinform. 25(2): 197-203 (2009) - [j9]Jochen W. Klingelhoefer, Loukas Moutsianas, Christopher C. Holmes:
Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency. Bioinform. 25(13): 1594-1601 (2009) - [j8]Shahzia Anjum, Arnaud Doucet
A boosting approach to structure learning of graphs with and without prior knowledge. Bioinform. 25(22): 2929-2936 (2009) - 2008
- [j7]Eleni Giannoulatou
GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population. Bioinform. 24(19): 2209-2214 (2008) - [j6]Ajay Jasra, Arnaud Doucet
Interacting sequential Monte Carlo samplers for trans-dimensional simulation. Comput. Stat. Data Anal. 52(4): 1765-1791 (2008) - 2007
- [j5]Ajay Jasra
On population-based simulation for static inference. Stat. Comput. 17(3): 263-279 (2007) - 2003
- [j4]Christopher C. Holmes, Dave Denison:
Classification with Bayesian MARS. Mach. Learn. 50(1-2): 159-173 (2003) - 2001
- [j3]Stephen J. Roberts, Christopher C. Holmes, Dave Denison:
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo. IEEE Trans. Pattern Anal. Mach. Intell. 23(8): 909-914 (2001) - 2000
- [j2]Christopher C. Holmes
Bayesian wavelet networks for nonparametric regression. IEEE Trans. Neural Networks Learn. Syst. 11(1): 27-35 (2000) - 1998
- [j1]Christopher C. Holmes, Bani K. Mallick:
Bayesian Radial Basis Functions of Variable Dimension. Neural Comput. 10(5): 1217-1233 (1998)
Conference and Workshop Papers
- 2024
- [c29]Fabian Falck, Ziyu Wang, Christopher C. Holmes:
Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective. ICML 2024 - [c28]Oscar Clivio, Avi Feller, Chris C. Holmes:
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference. UAI 2024: 856-880 - 2023
- [c27]Lucile Ter-Minassian, Oscar Clivio, Karla DiazOrdaz, Robin J. Evans, Christopher C. Holmes:
PWSHAP: A Path-Wise Explanation Model for Targeted Variables. ICML 2023: 34054-34089 - [c26]Jack Jewson, Sahra Ghalebikesabi, Chris C. Holmes:
Differentially Private Statistical Inference through β-Divergence One Posterior Sampling. NeurIPS 2023 - [c25]Christopher Williams, Fabian Falck, George Deligiannidis, Chris C. Holmes, Arnaud Doucet, Saifuddin Syed:
A Unified Framework for U-Net Design and Analysis. NeurIPS 2023 - [c24]Sahra Ghalebikesabi, Chris C. Holmes, Edwin Fong, Brieuc Lehmann:
Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates. UAI 2023: 658-668 - 2022
- [c23]Oscar Clivio, Fabian Falck, Brieuc Lehmann, George Deligiannidis, Chris C. Holmes:
Neural score matching for high-dimensional causal inference. AISTATS 2022: 7076-7110 - [c22]Fabian Falck, Christopher Williams, Dominic Danks, George Deligiannidis, Christopher Yau, Chris C. Holmes, Arnaud Doucet, Matthew Willetts:
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs. NeurIPS 2022 - [c21]Sahra Ghalebikesabi, Harry Wilde, Jack Jewson, Arnaud Doucet, Sebastian J. Vollmer, Chris C. Holmes:
Mitigating statistical bias within differentially private synthetic data. UAI 2022: 696-705 - 2021
- [c20]Harrison Wilde, Jack Jewson, Sebastian J. Vollmer, Chris C. Holmes:
Foundations of Bayesian Learning from Synthetic Data. AISTATS 2021: 541-549 - [c19]Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth:
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. AISTATS 2021: 3565-3573 - [c18]Alexander Camuto, Matthew Willetts, Chris C. Holmes, Brooks Paige, Stephen J. Roberts:
Learning Bijective Feature Maps for Linear ICA. AISTATS 2021: 3655-3663 - [c17]Sahra Ghalebikesabi, Rob Cornish, Chris C. Holmes, Luke J. Kelly:
Deep Generative Missingness Pattern-Set Mixture Models. AISTATS 2021: 3727-3735 - [c16]Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen J. Roberts, Christopher C. Holmes:
Improving VAEs' Robustness to Adversarial Attack. ICLR 2021 - [c15]Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli:
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. ICML 2021: 1249-1260 - [c14]Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh:
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift. NeurIPS 2021: 7898-7911 - [c13]Fabian Falck, Haoting Zhang, Matthew Willetts, George Nicholson, Christopher Yau, Chris C. Holmes:
Multi-Facet Clustering Variational Autoencoders. NeurIPS 2021: 8676-8690 - [c12]Edwin Fong, Chris C. Holmes:
Conformal Bayesian Computation. NeurIPS 2021: 18268-18279 - [c11]Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla DiazOrdaz, Chris C. Holmes:
On Locality of Local Explanation Models. NeurIPS 2021: 18395-18407 - 2020
- [c10]Matthew Willetts, Stephen J. Roberts, Chris C. Holmes:
Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels. IEEE BigData 2020: 5286-5295 - [c9]Alexander Camuto, Matthew Willetts, Umut Simsekli, Stephen J. Roberts, Chris C. Holmes:
Explicit Regularisation in Gaussian Noise Injections. NeurIPS 2020 - 2019
- [c8]Edwin Fong, Simon Lyddon, Chris C. Holmes:
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap. ICML 2019: 1952-1962 - 2018
- [c7]Tammo Rukat, Christopher C. Holmes, Christopher Yau:
Probabilistic Boolean Tensor Decomposition. ICML 2018: 4410-4419 - [c6]Simon Lyddon, Stephen Walker, Chris C. Holmes:
Nonparametric learning from Bayesian models with randomized objective functions. NeurIPS 2018: 2075-2085 - 2017
- [c5]Pedro M. Esperança, Louis J. M. Aslett, Chris C. Holmes:
Encrypted Accelerated Least Squares Regression. AISTATS 2017: 334-343 - [c4]Tammo Rukat, Christopher C. Holmes, Michalis K. Titsias, Christopher Yau:
Bayesian Boolean Matrix Factorisation. ICML 2017: 2969-2978 - 2016
- [c3]Babak Hodjat, Hormoz Shahrzad, Risto Miikkulainen, Lawrence Murray, Chris C. Holmes:
PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification. GPTP 2016: 139-148 - 2014
- [c2]Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes:
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach. ICML 2014: 405-413 - 2001
- [c1]Stephen J. Roberts, Christopher C. Holmes, Dave Denison:
Minimum-Entropy Data Clustering Using Reversible Jump Markov Chain Monte Carlo. ICANN 2001: 103-110
Informal and Other Publications
- 2024
- [i46]Lucile Ter-Minassian, Liran Szlak, Ehud Karavani, Chris C. Holmes, Yishai Shimoni:
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation. CoRR abs/2401.17737 (2024) - [i45]Lucile Ter-Minassian, Sahra Ghalebikesabi, Karla Diaz-Ordaz, Chris C. Holmes:
Explainable AI for survival analysis: a median-SHAP approach. CoRR abs/2402.00072 (2024) - [i44]Sam Dauncey, Chris C. Holmes, Christopher Williams, Fabian Falck:
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection. CoRR abs/2403.01485 (2024) - [i43]Ziyu Wang, Chris Holmes:
On Uncertainty Quantification for Near-Bayes Optimal Algorithms. CoRR abs/2403.19381 (2024) - [i42]Fabian Falck, Ziyu Wang, Chris Holmes:
Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective. CoRR abs/2406.00793 (2024) - [i41]Ziyu Wang, Chris Holmes:
On Subjective Uncertainty Quantification and Calibration in Natural Language Generation. CoRR abs/2406.05213 (2024) - [i40]Jake Fawkes, Lucile Ter-Minassian, Desi Ivanova, Uri Shalit, Christopher C. Holmes:
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition. CoRR abs/2409.07215 (2024) - [i39]Oscar Clivio, Avi Feller, Christopher C. Holmes:
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference. CoRR abs/2409.16407 (2024) - 2023
- [i38]Rob Cornish, Muhammad Faaiz Taufiq, Arnaud Doucet, Chris C. Holmes:
Causal Falsification of Digital Twins. CoRR abs/2301.07210 (2023) - [i37]Fabian Falck, Christopher Williams, Dominic Danks, George Deligiannidis
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs. CoRR abs/2301.08187 (2023) - [i36]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, Ginestra Bianconi, Rubén J. Sánchez-García, Alisha R. Davies, Maxine Mackintosh, Eleni-Rosalina Andrinopoulou, Anahid Basiri, Chris Harbron, Ben D. MacArthur
Learning from data with structured missingness. CoRR abs/2304.01429 (2023) - [i35]Christopher Williams, Fabian Falck, George Deligiannidis
A Unified Framework for U-Net Design and Analysis. CoRR abs/2305.19638 (2023) - [i34]Lucile Ter-Minassian, Oscar Clivio, Karla Diaz-Ordaz, Robin J. Evans, Chris C. Holmes:
PWSHAP: A Path-Wise Explanation Model for Targeted Variables. CoRR abs/2306.14672 (2023) - [i33]Jack Jewson, Sahra Ghalebikesabi, Chris C. Holmes:
Differentially Private Statistical Inference through β-Divergence One Posterior Sampling. CoRR abs/2307.05194 (2023) - 2022
- [i32]Oscar Clivio, Fabian Falck, Brieuc Lehmann, George Deligiannidis
Neural Score Matching for High-Dimensional Causal Inference. CoRR abs/2203.00554 (2022) - [i31]Sahra Ghalebikesabi, Chris C. Holmes, Edwin Fong
Density Estimation with Autoregressive Bayesian Predictives. CoRR abs/2206.06462 (2022) - [i30]Jobie Budd, Kieran Baker, Emma Karoune, Harry Coppock, Selina Patel
A large-scale and PCR-referenced vocal audio dataset for COVID-19. CoRR abs/2212.07738 (2022) - [i29]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, Lorraine Butler, Radka Jersakova, Jonathon Mellor, Selina Patel
Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers. CoRR abs/2212.08570 (2022) - [i28]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, Jonathon Mellor, George Nicholson, Joe Packham, Selina Patel
Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19. CoRR abs/2212.08571 (2022) - 2021
- [i27]Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli:
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. CoRR abs/2102.07006 (2021) - [i26]Sahra Ghalebikesabi, Rob Cornish, Luke J. Kelly, Chris C. Holmes:
Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness. CoRR abs/2103.03532 (2021) - [i25]Fabian Falck, Haoting Zhang, Matthew Willetts, George Nicholson, Christopher Yau, Christopher C. Holmes:
Multi-Facet Clustering Variational Autoencoders. CoRR abs/2106.05241 (2021) - [i24]Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla Diaz-Ordaz, Chris C. Holmes:
On Locality of Local Explanation Models. CoRR abs/2106.14648 (2021) - [i23]Sahra Ghalebikesabi, Harrison Wilde, Jack Jewson, Arnaud Doucet, Sebastian J. Vollmer, Chris C. Holmes:
Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale. CoRR abs/2108.10934 (2021) - 2020
- [i22]Alexander Camuto, Matthew Willetts, Brooks Paige, Chris C. Holmes, Stephen J. Roberts:
Learning Bijective Feature Maps for Linear ICA. CoRR abs/2002.07766 (2020) - [i21]Mark Briers, Marcos Charalambides, Chris C. Holmes:
Risk scoring calculation for the current NHSx contact tracing app. CoRR abs/2005.11057 (2020) - [i20]Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh:
Neural Ensemble Search for Performant and Calibrated Predictions. CoRR abs/2006.08573 (2020) - [i19]Tom Lovett, Mark Briers, Marcos Charalambides, Radka Jersakova, James Lomax, Chris C. Holmes:
Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers. CoRR abs/2007.05057 (2020) - [i18]Matthew Willetts, Xenia Miscouridou, Stephen J. Roberts, Chris C. Holmes:
Relaxed-Responsibility Hierarchical Discrete VAEs. CoRR abs/2007.07307 (2020) - [i17]Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth:
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. CoRR abs/2007.07365 (2020) - [i16]Alexander Camuto, Matthew Willetts, Umut Simsekli, Stephen J. Roberts, Chris C. Holmes:
Explicit Regularisation in Gaussian Noise Injections. CoRR abs/2007.07368 (2020) - [i15]Harrison Wilde, Jack Jewson, Sebastian J. Vollmer, Chris C. Holmes:
Foundations of Bayesian Learning from Synthetic Data. CoRR abs/2011.08299 (2020) - 2019
- [i14]Matthew Willetts, Stephen J. Roberts, Christopher C. Holmes:
Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels. CoRR abs/1901.08560 (2019) - [i13]Edwin Fong, Simon Lyddon, Christopher C. Holmes:
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap. CoRR abs/1902.03175 (2019) - [i12]Matthew Willetts, Alexander Camuto, Stephen J. Roberts, Chris C. Holmes:
Disentangling Improves VAEs' Robustness to Adversarial Attacks. CoRR abs/1906.00230 (2019) - [i11]Matthew Willetts, Stephen J. Roberts, Chris C. Holmes:
Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders. CoRR abs/1909.11501 (2019) - [i10]Matthew Willetts, Alexander Camuto, Stephen J. Roberts, Chris C. Holmes:
Regularising Deep Networks with DGMs. CoRR abs/1909.11507 (2019) - 2018
- [i9]Tammo Rukat, Christopher C. Holmes, Christopher Yau:
TensOrMachine: Probabilistic Boolean Tensor Decomposition. CoRR abs/1805.04582 (2018) - [i8]Simon Lyddon, Stephen Walker, Christopher C. Holmes:
Nonparametric learning from Bayesian models with randomized objective functions. CoRR abs/1806.11544 (2018) - [i7]Matthew Willetts, Aiden R. Doherty, Stephen J. Roberts, Chris C. Holmes:
Semi-unsupervised Learning of Human Activity using Deep Generative Models. CoRR abs/1810.12176 (2018) - [i6]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, Richard Branson, Karel G. M. Moons, Gary S. Collins, John P. A. Ioannidis, Chris C. Holmes, Harry Hemingway:
Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness. CoRR abs/1812.10404 (2018) - 2017
- [i5]Tammo Rukat, Christopher C. Holmes, Michalis K. Titsias, Christopher Yau:
Bayesian Boolean Matrix Factorisation. CoRR abs/1702.06166 (2017) - [i4]Pedro M. Esperança, Louis J. M. Aslett, Chris C. Holmes:
Encrypted accelerated least squares regression. CoRR abs/1703.00839 (2017) - 2015
- [i3]Louis J. M. Aslett, Pedro M. Esperança
A review of homomorphic encryption and software tools for encrypted statistical machine learning. CoRR abs/1508.06574 (2015) - [i2]Louis J. M. Aslett, Pedro M. Esperança
Encrypted statistical machine learning: new privacy preserving methods. CoRR abs/1508.06845 (2015) - 2013
- [i1]Michalis K. Titsias, Christopher Yau, Christopher C. Holmes:
Statistical Inference in Hidden Markov Models using $k$-segment Constraints. CoRR abs/1311.1189 (2013)
Coauthor Index
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