5 papers
Multivariate Uncertainty Quantification with Tomographic Quantile Forests
Takuya Kanazawa
Quantifying predictive uncertainty is essential for safe and trustworthy real-world AI deployment. Yet, fully nonparametric estimation of conditional distributions remains challeng…
Analysis of the QCD Kondo phase using random matrices
Takuya Kanazawa
We propose a novel random matrix model that describes the QCD Kondo phase. The model correctly implements both the chiral symmetry of light quarks and the SU(2) spin symmetry of he…
Relativistic Cooper pairing in the microscopic limit of chiral random matrix theory
Takuya Kanazawa
Random matrix theory (RMT) provides a powerful framework for analyzing universal features of strongly coupled physical systems. In quantum chromodynamics (QCD), cold quark matter a…
Using Distance Correlation for Efficient Bayesian Optimization
Takuya Kanazawa
The need to collect data via expensive measurements of black-box functions is prevalent across science, engineering and medicine. As an example, hyperparameter tuning of a large AI…
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning
Takuya Kanazawa, Chetan Gupta
Sequential decision making in the real world often requires finding a good balance of conflicting objectives. In general, there exist a plethora of Pareto-optimal policies that emb…