5 citations · 8 across the 10 of their papers we have counts for
10 papers
Natural Evolutionary Search meets Probabilistic Numerics
Pierre Osselin, Masaki Adachi, Xiaowen Dong +1
Zeroth-order local optimisation algorithms are essential for solving real-valued black-box optimisation problems. Among these, Natural Evolution Strategies (NES) represent a promin…
Scalable Valuation of Human Feedback through Provably Robust Model Alignment
Masahiro Fujisawa, Masaki Adachi, Michael A. Osborne
Despite the importance of aligning language models with human preferences, crowd-sourced human feedback is often noisy -- for example, preferring less desirable responses -- posing…
Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature
Masaki Adachi, Masahiro Fujisawa, Michael A Osborne
Despite the significance of probabilistic time-series forecasting models, their evaluation metrics often involve intractable integrations. The most widely used metric, the continuo…
Bayesian Optimization for Building Social-Influence-Free Consensus
Masaki Adachi, Siu Lun Chau, Wenjie Xu +3
We introduce Social Bayesian Optimization (SBO), a vote-efficient algorithm for consensus-building in collective decision-making. In contrast to single-agent scenarios, collective…
Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes
Csaba Tóth, Masaki Adachi, Michael A. Osborne +1
The signature kernel is a kernel between time series of arbitrary length and comes with strong theoretical guarantees from stochastic analysis. It has found applications in machine…
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
Juliusz Ziomek, Masaki Adachi, Michael A. Osborne
Bayesian Optimization (BO) is widely used for optimising black-box functions but requires us to specify the length scale hyperparameter, which defines the smoothness of the functio…
Principled Bayesian Optimisation in Collaboration with Human Experts
Wenjie Xu, Masaki Adachi, Colin N. Jones +1
Bayesian optimisation for real-world problems is often performed interactively with human experts, and integrating their domain knowledge is key to accelerate the optimisation proc…
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen +3
Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealin…
Time-Varying Gaussian Process Bandits with Unknown Prior
Juliusz Ziomek, Masaki Adachi, Michael A. Osborne
Bayesian optimisation requires fitting a Gaussian process model, which in turn requires specifying prior on the unknown black-box function -- most of the theoretical literature ass…
Looping in the Human Collaborative and Explainable Bayesian Optimization
Masaki Adachi, Brady Planden, David A. Howey +5
Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typicall…