10 papers
Generalised Robust Bayes for Joint Inference of Model and Contamination
Masahiro Fujisawa, Masaki Adachi, Takuo Matsubara
Robust generalised Bayesian methods protect parameter inference from contamination but do not generally infer its proportion or identify individual outliers. We propose Hölder--Bay…
LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms
Masaya Hagai, Yuta Suzuki, Tomoya Murata +2
Quantum chemistry simulations underpin modern materials discovery, yet their impact is limited by steep computational cost and dependence on fixed approximation schemes. Foundation…
LapidaryEngine: Fully Conversational Crystal Generation
Yusei Ito, Yuta Suzuki, Tomoya Murata +1
The emergence of Large Language Models (LLMs) has inspired the vision of generating bespoke crystal materials directly from natural-language instructions, enabling users to design…
Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputs
Masaki Adachi, Anita Yang, Yakun Wang +1
Guided-diffusion black-box optimization (BO) has shown strong empirical performance on structured design problems such as molecules and crystals, but its regret behavior remains po…
Open-Ended Task Discovery via Bayesian Optimization
Masaki Adachi, Yuta Suzuki, Juliusz Ziomek
When applying Bayesian optimization (BO) to scientific workflow, a major yet often overlooked source of uncertainty is the task itself -- namely, what to optimize and how to evalua…
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…
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…
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…