Jihao Andreas Lin

21 papers A* 4A 1Journal 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Empirical Gaussian Processes. CoRR Jihao Andreas Lin, Sebastian Ament, Louis C. Tiao, David Eriksson, Maximilian Balandat, Eytan Bakshy
2025	J	`jnl`	Graph Random Features for Scalable Gaussian Processes. CoRR Matthew Zhang, Jihao Andreas Lin, Krzysztof Choromanski, Adrian Weller, Richard E. Turner, Isaac Reid
2025	J	`jnl`	Improving Iterative Gaussian Processes via Warm Starting Sequential Posteriors. CoRR Alan Yufei Dong, Jihao Andreas Lin, José Miguel Hernández-Lobato
2025	A*	`conf`	Scalable Gaussian Processes with Latent Kronecker Structure. ICML Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy
2025	J	`jnl`	Scalable Gaussian Processes with Latent Kronecker Structure. CoRR Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy
2025	J	`jnl`	Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning. CoRR Jihao Andreas Lin
2025	J	`jnl`	Successive Halving with Learning Curve Prediction via Latent Kronecker Gaussian Processes. CoRR Jihao Andreas Lin, Nicolas Mayoraz, Steffen Rendle, Dima Kuzmin, Emil Praun, Berivan Isik
2024	A*	`conf`	Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. NeurIPS Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato
2024	J	`jnl`	Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. CoRR Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato
2024	J	`jnl`	Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure. CoRR Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, Eytan Bakshy
2024	A*	`conf`	Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
2024	J	`jnl`	Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes. CoRR Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, José Miguel Hernández-Lobato
2023	J	`jnl`	Beyond Intuition, a Framework for Applying GPs to Real-World Data. CoRR Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex S. Gardner, S. T. John, Hong Ge, Richard E. Turner
2023	J	`jnl`	Function-Space Regularization for Deep Bayesian Classification. CoRR Jihao Andreas Lin, Joe Watson, Pascal Klink, Jan Peters
2023	J	`jnl`	Minimal Random Code Learning with Mean-KL Parameterization. CoRR Jihao Andreas Lin, Gergely Flamich, José Miguel Hernández-Lobato
2023	J	`jnl`	Online Laplace Model Selection Revisited. CoRR Jihao Andreas Lin, Javier Antorán, José Miguel Hernández-Lobato
2023	A*	`conf`	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
2023	J	`jnl`	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
2023	J	`jnl`	Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
2021	A	`conf`	Latent Derivative Bayesian Last Layer Networks. AISTATS Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters
2020	J	`jnl`	Learning to Predict the 3D Layout of a Scene. CoRR Jihao Andreas Lin, Jakob Brünker, Daniel Fährmann