The Log-Exponential Smoothing Technique and Nesterov's Accelerated Gradient Method for Generalized Sylvester Problems. - Nguyen Thai An; Daniel Giles; Nguyen Mau Nam; R. Blake Rector (J. Optim. Theory Appl., 2016)

Minimizing Differences of Convex Functions with Applications to Facility Location and Clustering. - Nguyen Mau Nam; R. Blake Rector; Daniel Giles (J. Optim. Theory Appl., 2017)

Performance analysis of Volna-OP2 - massively parallel code for tsunami modelling. - Daniel Giles; Eugene Kashdan; Dimitra M. Salmanidou; Serge Guillas; Frédéric Dias (CoRR, 2020)

Multilevel Bayesian Quadrature. - Kaiyu Li; Daniel Giles; Toni Karvonen; Serge Guillas; François-Xavier Briol (AISTATS, 2023)

Scalable Data Assimilation with Message Passing. - Oscar Key; So Takao; Daniel Giles; Marc Peter Deisenroth (CoRR, 2024)

Embedding machine-learnt sub-grid variability improves climate model biases. - Daniel Giles; James Briant; Cyril Morcrette; Serge Guillas (CoRR, 2024)

Valid Error Bars for Neural Weather Models using Conformal Prediction. - Vignesh Gopakumar; Joel Oskarrson; Ander Gray; Lorenzo Zanisi; Stanislas Pamela; Daniel Giles; Matt J. Kusner; Marc Peter Deisenroth (CoRR, 2024)

Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction. - Vignesh Gopakumar; Ander Gray; Joel Oskarsson; Lorenzo Zanisi; Stanislas Pamela; Daniel Giles; Matt J. Kusner; Marc Peter Deisenroth (CoRR, 2024)

Learning Chaos In A Linear Way. - Xiaoyuan Cheng; Yi He; Yiming Yang; Xiao Xue; Sibo Cheng; Daniel Giles; Xiaohang Tang; Yukun Hu (ICLR, 2025)

Tensor-Var: Variational Data Assimilation in Tensor Product Feature Space. - Yiming Yang; Xiaoyuan Cheng; Daniel Giles; Sibo Cheng; Yi He; Xiao Xue; Boli Chen; Yukun Hu (CoRR, 2025)

Calibrated Physics-Informed Uncertainty Quantification. - Vignesh Gopakumar; Ander Gray; Lorenzo Zanisi; Timothy Nunn; Stanislas Pamela; Daniel Giles; Matt J. Kusner; Marc Peter Deisenroth (CoRR, 2025)