58 citations · 216 across the 28 of their papers we have counts for
5 papers · 1 filter
Policy Gradient with Kernel Quadrature
Satoshi Hayakawa, Tetsuro Morimura
Reward evaluation of episodes becomes a bottleneck in a broad range of reinforcement learning tasks. Our aim in this paper is to select a small but representative subset of a large…
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…
Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation
Hayata Yamasaki, Sathyawageeswar Subramanian, Satoshi Hayakawa +1
A significant challenge in the field of quantum machine learning (QML) is to establish applications of quantum computation to accelerate common tasks in machine learning such as th…
SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces
Masaki Adachi, Satoshi Hayakawa, Saad Hamid +3
Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective f…
Sampling-based Nyström Approximation and Kernel Quadrature
Satoshi Hayakawa, Harald Oberhauser, Terry Lyons
We analyze the Nyström approximation of a positive definite kernel associated with a probability measure. We first prove an improved error bound for the conventional Nyström approx…