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Found 7 papers, 0 papers with code
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning
Sequential decision making in the real world often requires finding a good balance of conflicting objectives.
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network
While this method has shown promising performance on a hand pose estimation task in computer vision, it remained unexplored whether this method works as nicely for regression on tabular data, and how it competes with more recent advanced UQ methods such as NGBoost.
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control
Uncertainty quantification is one of the central challenges for machine learning in real-world applications.
One-parameter family of acquisition functions for efficient global optimization
Bayesian optimization (BO) with Gaussian processes is a powerful methodology to optimize an expensive black-box function with as few function evaluations as possible.
Efficient Bayesian Optimization using Multiscale Graph Correlation
Bayesian optimization is a powerful tool to optimize a black-box function, the evaluation of which is time-consuming or costly.
Using Distance Correlation for Efficient Bayesian Optimization
We propose a novel approach for Bayesian optimization, called $\textsf{GP-DC}$, which combines Gaussian processes with distance correlation.
Accelerating small-angle scattering experiments with simulation-based machine learning
Making material experiments more efficient is a high priority for materials scientists who seek to discover new materials with desirable properties.
