State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes. - William J. Wilkinson; Paul E. Chang; Michael Riis Andersen; Arno Solin (ICML, 2020)

Fast Variational Learning in State-Space Gaussian Process Models. - Paul E. Chang; William J. Wilkinson; Mohammad Emtiyaz Khan; Arno Solin (MLSP, 2020)

Dual Parameterization of Sparse Variational Gaussian Processes. - Vincent Adam; Paul E. Chang; Mohammad Emtiyaz Khan; Arno Solin (NeurIPS, 2021)

Fantasizing with Dual GPs in Bayesian Optimization and Active Learning. - Paul E. Chang; Prakhar Verma; S. T. John; Victor Picheny; Henry B. Moss; Arno Solin (CoRR, 2022)

Memory-Based Dual Gaussian Processes for Sequential Learning. - Paul Edmund Chang; Prakhar Verma; S. T. John; Arno Solin; Mohammad Emtiyaz Khan (ICML, 2023)

Sparse Function-space Representation of Neural Networks. - Aidan Scannell; Riccardo Mereu; Paul E. Chang; Ella Tamir; Joni Pajarinen; Arno Solin (CoRR, 2023)

Function-space Parameterization of Neural Networks for Sequential Learning. - Aidan Scannell; Riccardo Mereu; Paul Edmund Chang; Ella Tamir; Joni Pajarinen; Arno Solin (ICLR, 2024)

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference. - Paul E. Chang; Nasrulloh Loka; Daolang Huang; Ulpu Remes; Samuel Kaski; Luigi Acerbi (CoRR, 2024)

Rethinking Inference in Gaussian Processes: A Dual Parameterization Approach. - Paul E. Chang (2024)

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference. - Paul Edmund Chang; Nasrulloh Ratu Bagus Satrio Loka; Daolang Huang; Ulpu Remes; Samuel Kaski; Luigi Acerbi (AISTATS, 2025)