Learning relevant contextual variables within Bayesian optimization.
Proceedings of the Uncertainty in Artificial Intelligence, 2024
Beyond Intuition, a Framework for Applying GPs to Real-World Data.
CoRR, 2023
Practical Equivariances via Relational Conditional Neural Processes.
CoRR, 2023
Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions.
CoRR, 2023
Cost-aware learning of relevant contextual variables within Bayesian optimization.
CoRR, 2023
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models.
Proceedings of the International Conference on Machine Learning, 2023
Causal Modeling of Policy Interventions From Treatment-Outcome Sequences.
Proceedings of the International Conference on Machine Learning, 2023
Memory-Based Dual Gaussian Processes for Sequential Learning.
Proceedings of the International Conference on Machine Learning, 2023
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds.
CoRR, 2022
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning.
CoRR, 2022
Joint Non-parametric Point Process model for Treatments and Outcomes: Counterfactual Time-series Prediction Under Policy Interventions.
CoRR, 2022
Non-separable Spatio-temporal Graph Kernels via SPDEs.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
GPflux: A Library for Deep Gaussian Processes.
CoRR, 2021
Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.
Bioinform., 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference.
CoRR, 2020
Amortized variance reduction for doubly stochastic objectives.
CoRR, 2020
A Framework for Interdomain and Multioutput Gaussian Processes.
CoRR, 2020
Amortized variance reduction for doubly stochastic objective.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019
Variational Gaussian Process Models without Matrix Inverses.
Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019
Scalable GAM using sparse variational Gaussian processes.
CoRR, 2018
Learning Invariances using the Marginal Likelihood.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Large-Scale Cox Process Inference using Variational Fourier Features.
Proceedings of the 35th International Conference on Machine Learning, 2018