4 papers
Robust and Computation-Aware Gaussian Processes
Marshal Arijona Sinaga, Julien Martinelli, Samuel Kaski
Gaussian processes (GPs) are widely used for regression and optimization tasks such as Bayesian optimization (BO) due to their expressiveness and principled uncertainty estimates.…
Challenges in interpretability of additive models
Xinyu Zhang, Julien Martinelli, ST John
We review generalized additive models as a type of ``transparent'' model that has recently seen renewed interest in the deep learning community as neural additive models. We highli…
PABBO: Preferential Amortized Black-Box Optimization
Xinyu Zhang, Daolang Huang, Samuel Kaski +1
Preferential Bayesian Optimization (PBO) is a sample-efficient method to learn latent user utilities from preferential feedback over a pair of designs. It relies on a statistical s…
Proxy-informed Bayesian transfer learning with unknown sources
Sabina J. Sloman, Julien Martinelli, Samuel Kaski
Generalization outside the scope of one's training data requires leveraging prior knowledge about the effects that transfer, and the effects that don't, between different data sour…