Room: Engineering Building 2B025

8:30-9:00 Arrivals

9:00-9:10 Welcome, 

9:10-10:10 TBC Pablo M Olmos, Universidad Carlos III Madrid

10:10-10:40 Coffee Break

10:40-11:40 TBC Manuel Wendl, ETH Zurich

11:40-12:40 TBC Xinxing Shin, University of Manchester

12:40-14:00 Lunch

14:00-15:00 TBC Mads Greisen Højlund, Aarhus University

15:00-15:30 Tea Break

15:30-16:30 Transformed Latent Variable MultiOutput Gaussian Processes Xiaoyu Jiang, University of Manchester

Abstracts

TBC

Transformed Latent Variable MultiOutput Gaussian Processes

MultiOutput Gaussian Processes (MOGPs) provide a principled probabilistic framework for modelling correlated outputs but face scalability bottlenecks when applied to datasets with high dimensional output spaces. To maintain tractability, existing methods typically resort to restrictive assumptions, such as employing low rank or sum of separable kernels, which can limit expressiveness. We propose the Transformed Latent Variable MOGP (TLVMOGP), a novel framework that scales MOGPs to a massive number of outputs while preserving the capacity to capture meaningful interoutput dependencies. TLVMOGP constructs a flexible multioutput deep kernel by mapping inputs and output specific latent variables into an embedding space using a Lipschitz regularised neural network. Combined with stochastic variational inference, our model effectively scales to high dimensional output settings. Across diverse benchmarks, including climate modelling with over 10000 outputs and zero inflated spatial transcriptomics data, TLVMOGP outperforms baselines in both predictive accuracy and computational efficiency.