Invited Talk: BORE — Bayesian Optimization by Density-Ratio Estimation
ELLIS AutoML Seminars (virtual).
BORE: Bayesian Optimization by Density-Ratio Estimation
We reformulate the computation of the acquisition function in Bayesian optimization (BO) as a probabilistic classification problem, providing advantages in scalability, …
A Primer on Pólya-gamma Random Variables - Part II: Bayesian Logistic Regression
We use one weird trick — Pólya-Gamma augmentation — to make exact inference in Bayesian logistic regression tractable.
Contributed Talk: BORE — Bayesian Optimization by Density-Ratio Estimation
NeurIPS 2020 4th Workshop on Meta-Learning (virtual).
Bayesian Optimization by Density Ratio Estimation
📄 One paper accepted to NeurIPS 2020
Our paper "Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings" was accepted to NeurIPS 2020 as a Spotlight Presentation …
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
We propose a joint probabilistic model with stochastic variational inference to improve the performance and robustness of graph convolutional networks (GCNs) in scenarios without …
Pantelis Elinas
Variational Graph Convolutional Networks
A Handbook for Sparse Variational Gaussian Processes
We summarize the notation, identities, and derivations underlying the sparse variational Gaussian process (SVGP) framework.
Tech Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
Amazon Machine Learning Community Tech Talk, Berlin.