Search Results for author:
Found 6 papers, 5 papers with code
A Multi-Scale Deep Learning Framework for Projecting Weather Extremes
Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year.
Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs
We present a new type of acquisition functions for online decision making in multi-armed and contextual bandit problems with extreme payoffs.
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
We introduce a class of acquisition functions for sample selection that leads to faster convergence in applications related to Bayesian experimental design and uncertainty quantification.
Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoring
An unmanned autonomous vehicle (UAV) is sent on a mission to explore and reconstruct an unknown environment from a series of measurements collected by Bayesian optimization.
Bayesian Optimization with Output-Weighted Optimal Sampling
In Bayesian optimization, accounting for the importance of the output relative to the input is a crucial yet challenging exercise, as it can considerably improve the final result but often involves inaccurate and cumbersome entropy estimations.
Learning the Tangent Space of Dynamical Instabilities from Data
For a large class of dynamical systems, the optimally time-dependent (OTD) modes, a set of deformable orthonormal tangent vectors that track directions of instabilities along any trajectory, are known to depend "pointwise" on the state of the system on the attractor, and not on the history of the trajectory.
