65 citations · 220 across the 24 of their papers we have counts for
24 papers
Challenges in interpretability of additive models
Xinyu Zhang, Julien Martinelli, ST John
We review generalized additive models as a type of ``transparent'' model that has recently seen renewed interest in the deep learning community as neural additive models. We highli…
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics
Arina Odnoblyudova, Çağlar Hizli, ST John +5
In biomedical applications it is often necessary to estimate a physiological response to a treatment consisting of multiple components, and learn the separate effects of the compon…
Thin and Deep Gaussian Processes
Daniel Augusto de Souza, Alexander Nikitin, ST John +6
Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the…
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Kenza Tazi, Jihao Andreas Lin, Ross Viljoen +4
Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets. However, their deployment is hindered by computational costs and…
Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions
Çağlar Hızlı, ST John, Anne Juuti +3
Deciding on an appropriate intervention requires a causal model of a treatment, the outcome, and potential mediators. Causal mediation analysis lets us distinguish between direct a…
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
Rui Li, ST John, Arno Solin
Approximate inference in Gaussian process (GP) models with non-conjugate likelihoods gets entangled with the learning of the model hyperparameters. We improve hyperparameter learni…