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06/09/2023
Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature
Real-world optimisation problems often feature complex combinations of (...
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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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05/03/2023
SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning
We propose SimSC, a remarkably simple framework, to address the problem ...
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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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01/28/2023
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
Large pretrained language models have been widely used in downstream NLP...
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10/18/2022
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Optimizing expensive-to-evaluate black-box functions of discrete (and po...
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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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11/11/2021
BOiLS: Bayesian Optimisation for Logic Synthesis
Optimising the quality-of-results (QoR) of circuits during logic synthes...
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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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11/04/2021
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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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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11/15/2020
Explaining the Adaptive Generalisation Gap
We conjecture that the reason for the difference in generalisation betwe...
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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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03/02/2020
Iterate Averaging Helps: An Alternative Perspective in Deep Learning
Iterate averaging has a rich history in optimisation, but has only very ...
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12/20/2019