2024

• Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. T. Papamarkou et al. ICML.

• Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. W Lin, F Dangel, R Eschenhagen, K Neklyudov, A Kristiadi, R E Turner and A Makhzani. ICML

• Translation Equivariant Transformer Neural Processes. M Ashman, C Diaconu, J Kim, L Sivaraya, S Markou, J Requeima, W P Bruinsma and R E Turner. ICML

• Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds. D Dodd, L Sharrock. and C Nemeth. ICML.

• Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. W Lin, F Dangel, R Eschenhagen, J Bae, R E Turner and A Makhzani. ICML

• Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. I Chien, W Bruinsma, J Gonzalez, and R E Turner. ICML

• Scalable Monte Carlo for Bayesian Learning. P Fearnhead, C Nemeth, CJ Oates and C Sherlock arXiv:2407.12751.

• Gaussian processes for Bayesian inverse problems associated with linear partial differential equations. T Bai, A L Teckentrup and K C Zygalakis. Statistics and Computing

• Optimising Distributions with Natural Gradient Surrogates. J So and R E Turner. AISTATS

• Tuning-free maximum likelihood training of latent variable models via coin betting. Sharrock, D Dodd and C Nemeth. AISTATS

• Identifiable Feature Learning for Spatial Data with Nonlinear ICA. H Hälvä, J So, R E Turner and A Hyvärinen. AISTATS

• Mixed type multimorbidity variational autoencoder: a deep generative model for multimorbidity analysis. W Kim, P A Jenkins, C Yau. Machine Learning for Healthcare

• Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous, Time-Varying Treatment Effects. I Chien, C Wong, Z Gero, J Bagga, R Ueno, R E Turner, R K Weerasinghe, B Piening, T Naumann, C Bifulco, H Poon and J G Hernandez. Machine Learning for Healthcare

• Implicitly Bayesian Prediction Rules in Deep Learning. B Mlodozeniec, D Krueger and R E Turner. Symposium on Advances in Approximate Bayesian Inference

• In-Context In-Context Learning with Transformer Neural Processes. M Ashman, C Diaconu, A Weller and R E Turner. Symposium on Advances in Approximate Bayesian Inference

• Galerkin meets Laplace: Fast uncertainty estimation in neural PDEs. C J Beltran, A Vergari, A L Teckentrup and K C Zygalakis. ICLR 2024 Workshop on AI4DifferentialEquations

• Explicit convergence bounds for Metropolis Markov chains: isoperimetry, spectral gaps and profiles. C Andrieu, A Lee, S Power and A Q Wang. Annals of Applied Probability

• SECRET: Statistical Emulation for Computational Reverse Engineering and Translation with applications in healthcare. L M Paun, M J Colebank, A Taylor-LaPole, M S Olufsen, W Ryan, I Murray, J M Salter, V Applebaum, M Dunne, J Hollins, L Kimpton, V Volodina, X Xiong, D Husmeier. Computer Methods in Applied Mechanics and Engineering

  Self-Organized State-Space Models with Artificial Dynamics. Y Chen, M Gerber, C Andrieu and R Douc. arXiv:2409.08928

• Gradient-free optimization via integration. C Andrieu, N Chopin, E Fincato and M Gerber. arXiv:2408.00888

• Diffusion Generative Modelling for Divide-and-Conquer MCMC. C Trojan, P Fearnhead and C Nemeth. arXiv:2406.11664

• Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows. A Cabezas, L Sharrock and C Nemeth. arXiv:2405.14392

• Monte Carlo sampling with integrator snippets. C Andrieu, M C Escudero, C Zhang. arXiv:2404.13302

• Manifold learning in Wasserstein space. K Hamm, C Moosmüller, B Schmitzer and M Thorpe. arXiv:2311.08549