33 citations · 62 across the 8 of their papers we have counts for
8 papers
Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch
Paul Brunzema, Louis Tiao, Nhat Le +3
Bayesian optimization (BO) has become the standard tool for sample-efficient optimization and owes its efficiency to uncertainty-aware search driven by generic statistical priors.…
Empirical Gaussian Processes
Jihao Andreas Lin, Sebastian Ament, Louis C. Tiao +3
Gaussian processes (GPs) are powerful and widely used probabilistic regression models, but their effectiveness in practice is often limited by the choice of kernel function. This k…
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Louis C. Tiao, Vincent Dutordoir, Victor Picheny
Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for lacking the ability to learn representations. Rec…
Batch Bayesian optimisation via density-ratio estimation with guarantees
Rafael Oliveira, Louis Tiao, Fabio Ramos
Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions. Traditionally BO has been set as a sequential decision-…
BORE: Bayesian Optimization by Density-Ratio Estimation
Louis C. Tiao, Aaron Klein, Matthias Seeger +3
Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion enco…
Model-based Asynchronous Hyperparameter and Neural Architecture Search
Aaron Klein, Louis C. Tiao, Thibaut Lienart +2
We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian p…
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Pantelis Elinas, Edwin V. Bonilla, Louis Tiao
We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial a…
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
Louis C. Tiao, Edwin V. Bonilla, Fabio Ramos
We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlyin…