Jihao Andreas Lin

According to our database, Jihao Andreas Lin authored at least 17 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Empirical Gaussian Processes.

Jihao Andreas Lin, Sebastian Ament, Louis C. Tiao, David Eriksson, Maximilian Balandat, Eytan Bakshy

CoRR, February, 2026

2025

Improving Iterative Gaussian Processes via Warm Starting Sequential Posteriors.

Alan Yufei Dong, Jihao Andreas Lin, José Miguel Hernández-Lobato

CoRR, November, 2025

Graph Random Features for Scalable Gaussian Processes.

Matthew Zhang, Jihao Andreas Lin, Krzysztof Choromanski, Adrian Weller, Richard E. Turner, Isaac Reid

CoRR, September, 2025

Successive Halving with Learning Curve Prediction via Latent Kronecker Gaussian Processes.

Jihao Andreas Lin, Nicolas Mayoraz, Steffen Rendle, Dima Kuzmin, Emil Praun, Berivan Isik

CoRR, August, 2025

Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning.

Jihao Andreas Lin

CoRR, July, 2025

Scalable Gaussian Processes with Latent Kronecker Structure.

Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure.

Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, Eytan Bakshy

CoRR, 2024

Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes.

Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, José Miguel Hernández-Lobato

CoRR, 2024

Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes.

Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Stochastic Gradient Descent for Gaussian Processes Done Right.

Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Minimal Random Code Learning with Mean-KL Parameterization.

Jihao Andreas Lin, Gergely Flamich, José Miguel Hernández-Lobato

CoRR, 2023

Online Laplace Model Selection Revisited.

Jihao Andreas Lin, Javier Antorán, José Miguel Hernández-Lobato

CoRR, 2023

Function-Space Regularization for Deep Bayesian Classification.

Jihao Andreas Lin, Joe Watson, Pascal Klink, Jan Peters

CoRR, 2023

Beyond Intuition, a Framework for Applying GPs to Real-World Data.

Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex S. Gardner, S. T. John, Hong Ge, Richard E. Turner

CoRR, 2023

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent.

Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2021

Latent Derivative Bayesian Last Layer Networks.

Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Learning to Predict the 3D Layout of a Scene.

Jihao Andreas Lin, Jakob Brünker, Daniel Fährmann

CoRR, 2020