91 citations · 992 across the 56 of their papers we have counts for
7 papers · 1 filter
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,…
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…
Efficient Bayesian Optimization using Multiscale Graph Correlation
Takuya Kanazawa
Bayesian optimization is a powerful tool to optimize a black-box function, the evaluation of which is time-consuming or costly. In this paper, we propose a new approach to Bayesian…