1 citations · 2 across the 8 of their papers we have counts for
8 papers
Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing
Anh Vu Nguyen, Dino Sejdinovic, Tat-Jun Chin
Edge learning refers to training machine learning models deployed on edge platforms, typically using new data accumulated onboard. The computational limitations on edge devices aff…
Squared families: Searching beyond regular probability models
Russell Tsuchida, Jiawei Liu, Cheng Soon Ong +1
We introduce squared families, which are families of probability densities obtained by squaring a linear transformation of a statistic. Squared families are singular, however their…
Near-Optimal Approximations for Bayesian Inference in Function Space
Veit Wild, James Wu, Dino Sejdinovic +1
We propose a scalable inference algorithm for Bayes posteriors defined on a reproducing kernel Hilbert space (RKHS). Given a likelihood function and a Gaussian random element repre…
Indirect Query Bayesian Optimization with Integrated Feedback
Mengyan Zhang, Shahine Bouabid, Cheng Soon Ong +2
We develop the framework of Indirect Query Bayesian Optimization (IQBO), a new class of Bayesian optimization problems where the integrated feedback is given via a conditional expe…
Label Distribution Learning using the Squared Neural Family on the Probability Simplex
Daokun Zhang, Russell Tsuchida, Dino Sejdinovic
Label distribution learning (LDL) provides a framework wherein a distribution over categories rather than a single category is predicted, with the aim of addressing ambiguity in la…
Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks
Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo +6
Computational complexity of Bayesian learning is impeding its adoption in practical, large-scale tasks. Despite demonstrations of significant merits such as improved robustness and…
Bayesian Adaptive Calibration and Optimal Design
Rafael Oliveira, Dino Sejdinovic, David Howard +1
The process of calibrating computer models of natural phenomena is essential for applications in the physical sciences, where plenty of domain knowledge can be embedded into simula…
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families
Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
We introduce squared neural Poisson point processes (SNEPPPs) by parameterising the intensity function by the squared norm of a two layer neural network. When the hidden layer is f…