Browse by UCL people
Group by: Type | Date
Number of items: 25.
Article
Cabrera, CP;
Manson, J;
Shepherd, JM;
Torrance, HD;
Watson, D;
Longhi, MP;
Hoti, M;
... Brohi, K; + view all
(2017)
Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study.
PLoS Med
, 14
(7)
, Article e1002352. 10.1371/journal.pmed.1002352.
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Cooles, Faye AH;
Tarn, Jessica;
Lendrem, Dennis W;
Naamane, Najib;
Lin, Chung Ma;
Millar, Ben;
Maney, Nicola J;
... Isaacs, John D; + view all
(2022)
Interferon-α-mediated therapeutic resistance in early rheumatoid arthritis implicates epigenetic reprogramming.
Annals of the Rheumatic Diseases
10.1136/annrheumdis-2022-222370.
(In press).
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Cope, AP;
Barnes, MR;
Belson, A;
Binks, M;
Brockbank, S;
Bonachela-Capdevila, F;
Carini, C;
... RA-MAP Consortium; + view all
(2018)
The RA-MAP Consortium: a working model for academia-industry collaboration.
Nature Reviews Rheumatology
, 14
(1)
pp. 53-60.
10.1038/nrrheum.2017.200.
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John, CR;
Watson, D;
Barnes, MR;
Pitzalis, C;
Lewis, MJ;
(2020)
Spectrum: fast density-aware spectral clustering for single and multi-omic data.
Bioinformatics
, 36
(4)
pp. 1159-1166.
10.1093/bioinformatics/btz704.
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John, CR;
Watson, D;
Russ, D;
Goldmann, K;
Ehrenstein, M;
Pitzalis, C;
Lewis, M;
(2020)
M3C: Monte Carlo reference-based consensus clustering.
Scientific Reports
, 10
, Article 1816. 10.1038/s41598-020-58766-1.
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Marchal, N;
Watson, DS;
(2021)
The paradox of poor representation: How voter–party incongruence curbs affective polarisation.
The British Journal of Politics and International Relations
10.1177/13691481211048502.
(In press).
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Mökander, Jakob;
Juneja, Prathm;
Watson, David S;
Floridi, Luciano;
(2022)
The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?
Minds and Machines
10.1007/s11023-022-09612-y.
(In press).
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Nicholls, HL;
John, CR;
Watson, DS;
Munroe, PB;
Barnes, MR;
Cabrera, CP;
(2020)
Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.
Frontiers in Genetics
, 11
, Article 350. 10.3389/fgene.2020.00350.
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O'Toole, SM;
Watson, DS;
Novoselova, TV;
Romano, LEL;
King, PJ;
Bradshaw, TY;
Thompson, CL;
... Chapple, JP; + view all
(2019)
Oncometabolite induced primary cilia loss in pheochromocytoma.
Endocrine-Related Cancer
, 26
(1)
pp. 165-180.
10.1530/ERC-18-0134.
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Öhman, CJ;
Watson, D;
(2019)
Are the dead taking over Facebook? A Big Data approach to the future of death online.
Big Data & Society
, 6
(1)
10.1177/2053951719842540.
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Watson, David S;
(2022)
Conceptual challenges for interpretable machine learning.
Synthese
, 200
, Article 65. 10.1007/s11229-022-03485-5.
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Watson, David S;
Gultchin, Limor;
Taly, Ankur;
Floridi, Luciano;
(2022)
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice.
Minds and Machines
, 32
(1)
pp. 185-218.
10.1007/s11023-022-09598-7.
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Watson, DS;
(2021)
Interpretable machine learning for genomics.
Human Genetics
10.1007/s00439-021-02387-9.
(In press).
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Watson, D;
(2019)
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.
Minds & Machines
, 29
pp. 417-440.
10.1007/s11023-019-09506-6.
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Watson, D;
Floridi, L;
(2018)
Crowdsourced science: sociotechnical epistemology in the e-research paradigm.
Synthese
, 195
(2)
pp. 741-764.
10.1007/s11229-016-1238-2.
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Watson, DS;
Floridi, L;
(2020)
The explanation game: a formal framework for interpretable machine learning.
Synthese
10.1007/s11229-020-02629-9.
(In press).
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Watson, DS;
Krutzinna, J;
Bruce, IN;
Griffiths, CE;
McInnes, IB;
Barnes, MR;
Floridi, L;
(2019)
Clinical applications of machine learning algorithms: beyond the black box.
BMJ
, 364
, Article l886. 10.1136/bmj.l886.
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Watson, DS;
Wright, MN;
(2021)
Testing Conditional Independence in Supervised Learning Algorithms.
Machine Learning
, 110
pp. 2107-2129.
10.1007/s10994-021-06030-6.
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Proceedings paper
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Gultchin, L;
Watson, DS;
Kusner, MJ;
Silva, R;
(2021)
Operationalizing Complex Causes: A Pragmatic View of Mediation.
In:
Proceedings of the 38th International Conference on Machine Learning.
(pp. pp. 3875-3885).
MLResearchPress
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Watson, David;
(2022)
Rational Shapley Values.
In:
FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency.
(pp. pp. 1083-1094).
ACM
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Watson, David S;
Blesch, Kristin;
Kapar, Jan;
Wright, Marvin N;
(2023)
Adversarial Random Forests for Density Estimation and Generative Modeling.
In: Ruiz, Francisco and Dy, Jennifer and Van de Meent, Jan-Willem, (eds.)
Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023.
PMLR (The Proceedings of Machine Learning Research): Palau de Congressos, Valencia, Spain.
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Watson, DS;
Gultchin, L;
Taly, A;
Floridi, L;
(2021)
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice.
In: de Campos, C and Maathuis, MH and Quaeghebeur, E, (eds.)
Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021.
(pp. pp. 1382-1392).
Association for Uncertainty in Artificial Intelligence (AUAI)
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Watson, DS;
Silva, R;
(2022)
Causal Discovery Under a Confounder Blanket.
In:
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022).
(pp. pp. 2096-2106).
PMLR 180
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Watson, DS;
(2021)
No explanation without inference.
In:
AISB 2021 Symposium Proceedings: Overcoming Opacity in Machine Learning.
(pp. pp. 9-11).
Society for the Study of Artificial Intelligence & Simulation of Behaviour
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Working / discussion paper
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John, C;
Watson, D;
Russ, D;
Goldmann, K;
Ehrenstein, M;
Pitzalis, C;
Lewis, M;
(2019)
M3C: Monte Carlo reference-based consensus clustering.
BioRxiv: Cold Spring Harbor, NY, USA.
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