75 citations · 459 across the 65 of their papers we have counts for
65 papers
Bounded P-values in Parametric Programming-based Selective Inference
Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy +1
Selective inference (SI) has been actively studied as a promising framework for statistical hypothesis testing for data-driven hypotheses. The basic idea of SI is to make inference…
Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping
Felix Adams, Austin McDannald, Ichiro Takeuchi +1
Autonomous experimentation (AE) combines machine learning and research hardware automation in a closed loop, guiding subsequent experiments toward user goals. As applied to materia…
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Exploration
Onur Boyar, Ichiro Takeuchi
Latent Space Bayesian Optimization (LSBO) combines generative models, typically Variational Autoencoders (VAE), with Bayesian Optimization (BO) to generate de-novo objects of inter…
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty
Yu Inatsu, Shion Takeno, Hiroyuki Hanada +2
In this study, we propose a novel multi-objective Bayesian optimization (MOBO) method to efficiently identify the Pareto front (PF) defined by risk measures for black-box functions…
A Confidence Machine for Sparse High-Order Interaction Model
Diptesh Das, Eugene Ndiaye, Ichiro Takeuchi
In predictive modeling for high-stake decision-making, predictors must be not only accurate but also reliable. Conformal prediction (CP) is a promising approach for obtaining the c…