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06/08/2023
Bayesian Optimisation of Functions on Graphs
The increasing availability of graph-structured data motivates the task ...
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03/15/2023
Bayesian Quadrature for Neural Ensemble Search
Ensembling can improve the performance of Neural Networks, but existing ...
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01/20/2023
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthro...
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11/03/2022
Towards Discovering Neural Architectures from Scratch
The discovery of neural architectures from scratch is the long-standing ...
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07/19/2022
Bayesian Generational Population-Based Training
Reinforcement learning (RL) offers the potential for training generally ...
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03/16/2022
On Redundancy and Diversity in Cell-based Neural Architecture Search
Searching for the architecture cells is a dominant paradigm in NAS. Howe...
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01/12/2022
Learning to Identify Top Elo Ratings: A Dueling Bandits Approach
The Elo rating system is widely adopted to evaluate the skills of (chess...
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12/24/2021
DARTS without a Validation Set: Optimizing the Marginal Likelihood
The success of neural architecture search (NAS) has historically been li...
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11/08/2021
Approximate Neural Architecture Search via Operation Distribution Learning
The standard paradigm in Neural Architecture Search (NAS) is to search f...
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11/05/2021
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family
State-of-the-art results in deep learning have been improving steadily, ...
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11/04/2021
Adversarial Attacks on Graph Classification via Bayesian Optimisation
Graph neural networks, a popular class of models effective in a wide ran...
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09/13/2021
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
Automated machine learning (AutoML) usually involves several crucial com...
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02/14/2021
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
High-dimensional black-box optimisation remains an important yet notorio...
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10/27/2020
A Bayesian Perspective on Training Speed and Model Selection
We take a Bayesian perspective to illustrate a connection between traini...
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06/13/2020
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel
Bayesian optimisation (BO) has been widely used for hyperparameter optim...
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06/08/2020
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
Reliable yet efficient evaluation of generalisation performance of a pro...
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04/03/2020
Neural Architecture Generator Optimization
Neural Architecture Search (NAS) was first proposed to achieve state-of-...
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06/20/2019
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Efficient optimisation of black-box problems that comprise both continuo...
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06/03/2019
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
Efficient approximation lies at the heart of large-scale machine learnin...
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01/29/2019
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Batch Bayesian optimisation (BO) has been successfully applied to hyperp...
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04/18/2018
Entropic Spectral Learning in Large Scale Networks
We present a novel algorithm for learning the spectral density of large ...
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11/02/2017