1 citations · 1 across the 5 of their papers we have counts for
6 papers
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
Maksym Tretiakov, Sarah Filippi, Sarah Lucie Filipp +4
Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives. A desirable feature of such methods is that they should capture the epistem…
How Many Initial Points Does Bayesian Optimization Need?
Mujin Cheon, James Odgers, Dong-Yeun Koh +1
Bayesian Optimization (BO) generally begins with an initialization phase: a batch of uninformed evaluations. The choice of remains largely heuristic, and we empirically…
Gaussian Mean Field Variational Inference can Overestimate Predictive Variance
James Odgers, Ben Riegler, Siddharth Swaroop +1
Mean Field Variational Inference (MFVI) is widely understood to underestimate posterior variance. By analysing conjugate Bayesian Linear Regression (BLR), we show that this charact…
Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization
Ben Riegler, James Odgers, Vincent Fortuin
Asynchronous Bayesian optimization is widely used for gradient-free optimization in domains with independent parallel experiments and varying evaluation times. Existing methods pos…
Weighted-Sum of Gaussian Process Latent Variable Models
James Odgers, Ruby Sedgwick, Chrysoula Kappatou +2
This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Pro…
Practical Path-based Bayesian Optimization
Jose Pablo Folch, James Odgers, Shiqiang Zhang +6
There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing. Bayesian optimization (BO) has proven to be…