730 citations · 2.8k across the 62 of their papers we have counts for
65 papers
Advances, challenges, and opportunities for legged robots
Jonas Frey, Matías Mattamala, Hae-Won Park +5
Humanoid and quadrupedal robots have the potential to revolutionize the way we work, interact, and coexist with intelligent machines. To understand their effects on society and how…
Bayesian Optimisation of Functions on Graphs
Xingchen Wan, Pierre Osselin, Henry Kenlay +3
The increasing availability of graph-structured data motivates the task of optimising over functions defined on the node set of graphs. Traditional graph search algorithms can be a…
Bézier Gaussian Processes for Tall and Wide Data
Martin Jørgensen, Michael A. Osborne
Modern approximations to Gaussian processes are suitable for "tall data", with a cost that scales well in the number of observations, but under-performs on ``wide data'', scaling p…
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
Michael K. Cohen, Samuel Daulton, Michael A. Osborne
Gaussian processes (GPs) produce good probabilistic models of functions, but most GP kernels require time, where is the number of data points and the number o…
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen +2
Calculation of Bayesian posteriors and model evidences typically requires numerical integration. Bayesian quadrature (BQ), a surrogate-model-based approach to numerical integration…
Bridging the reality gap in quantum devices with physics-aware machine learning
D. L. Craig, H. Moon, F. Fedele +9
The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of mater…
Adversarial Attacks on Graph Classification via Bayesian Optimisation
Xingchen Wan, Henry Kenlay, Binxin Ru +3
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majorit…
Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds
Vu Nguyen, Marc Peter Deisenroth, Michael A. Osborne
Many functions have approximately-known upper and/or lower bounds, potentially aiding the modeling of such functions. In this paper, we introduce Gaussian process models for functi…
Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning
B. Severin, D. T. Lennon, L. C. Camenzind +20
The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a ke…
Universal Approximation of Functions on Sets
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke +2
Modelling functions of sets, or equivalently, permutation-invariant functions, is a long-standing challenge in machine learning. Deep Sets is a popular method which is known to be…
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
Xingchen Wan, Vu Nguyen, Huong Ha +3
High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian optimisation methods on continuous domains, domain…
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Vu Nguyen, Vaden Masrani, Rob Brekelmans +2
Achieving the full promise of the Thermodynamic Variational Objective (TVO), a recently proposed variational lower bound on the log evidence involving a one-dimensional Riemann int…
Deep Reinforcement Learning for Efficient Measurement of Quantum Devices
V. Nguyen, S. B. Orbell, D. T. Lennon +9
Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experie…
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Vu Nguyen, Tam Le, Makoto Yamada +1
Neural architecture search (NAS) automates the design of deep neural networks. One of the main challenges in searching complex and non-continuous architectures is to compare the si…
Machine learning enables completely automatic tuning of a quantum device faster than human experts
H. Moon, D. T. Lennon, J. Kirkpatrick +10
Device variability is a bottleneck for the scalability of semiconductor quantum devices. Increasing device control comes at the cost of a large parameter space that has to be explo…
Bayesian Optimization for Iterative Learning
Vu Nguyen, Sebastian Schulze, Michael A Osborne
The performance of deep (reinforcement) learning systems crucially depends on the choice of hyperparameters. Their tuning is notoriously expensive, typically requiring an iterative…
Adaptive Configuration Oracle for Online Portfolio Selection Methods
Favour M. Nyikosa, Michael A. Osborne, Stephen J. Roberts
Financial markets are complex environments that produce enormous amounts of noisy and non-stationary data. One fundamental problem is online portfolio selection, the goal of which…
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
Sebastian Farquhar, Michael Osborne, Yarin Gal
We propose Radial Bayesian Neural Networks (BNNs): a variational approximate posterior for BNNs which scales well to large models while maintaining a distribution over weight-space…
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
Diego Granziol, Binxin Ru, Stefan Zohren +3
Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing…
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Binxin Ru, Ahsan S. Alvi, Vu Nguyen +2
Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges. We propose a new approach, Continu…
Knowing The What But Not The Where in Bayesian Optimization
Vu Nguyen, Michael A. Osborne
Bayesian optimization has demonstrated impressive success in finding the optimum input x* and output f* = f(x*) = max f(x) of a black-box function f. In some applications, however,…
Automated Model Selection with Bayesian Quadrature
Henry Chai, Jean-Francois Ton, Roman Garnett +1
We present a novel technique for tailoring Bayesian quadrature (BQ) to model selection. The state-of-the-art for comparing the evidence of multiple models relies on Monte Carlo met…
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
Gabriele Abbati, Philippe Wenk, Michael A Osborne +3
Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integr…
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems
Philippe Wenk, Gabriele Abbati, Michael A Osborne +3
Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting. In this work, we in…