730 citations · 2.8k across the 62 of their papers we have counts for
11 papers · 1 filter
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…
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Ahsan S. Alvi, Binxin Ru, Jan Calliess +2
Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead…
On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke +2
Recent work on the representation of functions on sets has considered the use of summation in a latent space to enforce permutation invariance. In particular, it has been conjectur…