16 citations · 73 across the 11 of their papers we have counts for
11 papers
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen +4
Active learning parallelization is widely used, but typically relies on fixing the batch size throughout experimentation. This fixed approach is inefficient because of a dynamic tr…
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Samuel Daulton, Xingchen Wan, David Eriksson +3
Optimizing expensive-to-evaluate black-box functions of discrete (and potentially continuous) design parameters is a ubiquitous problem in scientific and engineering applications.…
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature
Masaki Adachi, Yannick Kuhn, Birger Horstmann +3
A wide variety of battery models are available, and it is not always obvious which model `best' describes a dataset. This paper presents a Bayesian model selection approach using B…
Bayesian Generational Population-Based Training
Xingchen Wan, Cong Lu, Jack Parker-Holder +4
Reinforcement learning (RL) offers the potential for training generally capable agents that can interact autonomously in the real world. However, one key limitation is the brittlen…
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
Cong Lu, Philip J. Ball, Tim G. J. Rudner +3
Offline reinforcement learning has shown great promise in leveraging large pre-collected datasets for policy learning, allowing agents to forgo often-expensive online data collecti…
Robust Multi-Objective Bayesian Optimization Under Input Noise
Samuel Daulton, Sait Cakmak, Maximilian Balandat +3
Bayesian optimization (BO) is a sample-efficient approach for tuning design parameters to optimize expensive-to-evaluate, black-box performance metrics. In many manufacturing proce…
Revisiting Design Choices in Offline Model-Based Reinforcement Learning
Cong Lu, Philip J. Ball, Jack Parker-Holder +2
Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially…
Marginalising over Stationary Kernels with Bayesian Quadrature
Saad Hamid, Sebastian Schulze, Michael A. Osborne +1
Marginalising over families of Gaussian Process kernels produces flexible model classes with well-calibrated uncertainty estimates. Existing approaches require likelihood evaluatio…
Bayesian Optimization for Dynamic Problems
Favour M. Nyikosa, Michael A. Osborne, Stephen J. Roberts
We propose practical extensions to Bayesian optimization for solving dynamic problems. We model dynamic objective functions using spatiotemporal Gaussian process priors which captu…
Practical Bayesian Optimization for Variable Cost Objectives
Mark McLeod, Michael A. Osborne, Stephen J. Roberts
We propose a novel Bayesian Optimization approach for black-box functions with an environmental variable whose value determines the tradeoff between evaluation cost and the fidelit…
A Variational Bayesian State-Space Approach to Online Passive-Aggressive Regression
Arnold Salas, Stephen J. Roberts, Michael A. Osborne
Online Passive-Aggressive (PA) learning is a class of online margin-based algorithms suitable for a wide range of real-time prediction tasks, including classification and regressio…