65 citations · 74 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Identifying latent state transition in non-linear dynamical systems
Çağlar Hızlı, Çağatay Yıldız, Matthias Bethge +2
This work aims to improve generalization and interpretability of dynamical systems by recovering the underlying lower-dimensional latent states and their time evolutions. Previous…
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…
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…
Memory-Based Dual Gaussian Processes for Sequential Learning
Paul E. Chang, Prakhar Verma, S. T. John +2
Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning. In such cases, errors can accum…