10 citations · 13 across the 9 of their papers we have counts for
8 papers · 1 filter
Predictive Modeling through Hyper-Bayesian Optimization
Manisha Senadeera, Santu Rana, Sunil Gupta +1
Model selection is an integral problem of model based optimization techniques such as Bayesian optimization (BO). Current approaches often treat model selection as an estimation pr…
BO-Muse: A human expert and AI teaming framework for accelerated experimental design
Sunil Gupta, Alistair Shilton, Arun Kumar A +7
In this paper we introduce BO-Muse, a new approach to human-AI teaming for the optimization of expensive black-box functions. Inspired by the intrinsic difficulty of extracting exp…
Continual Learning with Dependency Preserving Hypernetworks
Dupati Srikar Chandra, Sakshi Varshney, P. K. Srijith +1
Humans learn continually throughout their lifespan by accumulating diverse knowledge and fine-tuning it for future tasks. When presented with a similar goal, neural networks suffer…
Sparse Spectrum Gaussian Process for Bayesian Optimization
Ang Yang, Cheng Li, Santu Rana +2
We propose a novel sparse spectrum approximation of Gaussian process (GP) tailored for Bayesian optimization. Whilst the current sparse spectrum methods provide desired approximati…
Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives
Tinu Theckel Joy, Santu Rana, Sunil Gupta +1
In this paper we develop a Bayesian optimization based hyperparameter tuning framework inspired by statistical learning theory for classifiers. We utilize two key facts from PAC le…
Multi-objective Bayesian optimisation with preferences over objectives
Majid Abdolshah, Alistair Shilton, Santu Rana +2
We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more importa…