65 citations · 85 across the 12 of their papers we have counts for
10 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…
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…
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…
Learning Relevant Contextual Variables Within Bayesian Optimization
Julien Martinelli, Ayush Bharti, Armi Tiihonen +5
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding…