91 citations · 992 across the 56 of their papers we have counts for
56 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…
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…
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network
Takuya Kanazawa, Chetan Gupta
Development of an accurate, flexible, and numerically efficient uncertainty quantification (UQ) method is one of fundamental challenges in machine learning. Previously, a UQ method…
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control
Takuya Kanazawa, Haiyan Wang, Chetan Gupta
Uncertainty quantification is one of the central challenges for machine learning in real-world applications. In reinforcement learning, an agent confronts two kinds of uncertainty,…
Complex spacing ratios of the non-Hermitian Dirac operator in universality classes AI and AII
Takuya Kanazawa, Tilo Wettig
We consider non-Hermitian Dirac operators in QCD-like theories coupled to a chiral U(1) potential or an imaginary chiral chemical potential. We show that in the continuum they fall…
One-parameter family of acquisition functions for efficient global optimization
Takuya Kanazawa
Bayesian optimization (BO) with Gaussian processes is a powerful methodology to optimize an expensive black-box function with as few function evaluations as possible. The expected…