research
∙
03/15/2023
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning
Sequential decision making in the real world often requires finding a go...
share
research
∙
09/17/2022
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network
Development of an accurate, flexible, and numerically efficient uncertai...
research
∙
07/27/2022
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control
Uncertainty quantification is one of the central challenges for machine ...
research
∙
04/26/2021
One-parameter family of acquisition functions for efficient global optimization
Bayesian optimization (BO) with Gaussian processes is a powerful methodo...
research
∙
03/17/2021
Efficient Bayesian Optimization using Multiscale Graph Correlation
Bayesian optimization is a powerful tool to optimize a black-box functio...
research
∙
02/17/2021
Using Distance Correlation for Efficient Bayesian Optimization
We propose a novel approach for Bayesian optimization, called GP-DC, whi...
research
∙
08/24/2019