Active learning strategies for atomic cluster expansion models

Atomic Energy Models for Machine Learning: Atomic Restricted Boltzmann Machines

Monitoring atomic cluster expansion by high-harmonic generation.

High-harmonic generation is shown to be capable of providing time-resolved information about the particle density of a complex system. As an example, we study numerically high-harmonic generation from expanding xenon clusters in a pump-probe laser scheme, where the pump laser pulse induces the cluster explosion and the probe pulse generates harmonics in the expanding cluster. We show that the h...

PLAL: Cluster-based active learning

We investigate the label complexity of active learning under some smoothness assumptions on the data-generating process. We propose a procedure, PLAL, for “activising” passive, sample-based learners. The procedure takes an unlabeled sample, queries the labels of some of its members, and outputs a full labeling of that sample. Assuming the data satisfies “Probabilistic Lipschitzness”, a notion o...

Active Learning for Pipeline Models

For many machine learning solutions to complex applications, there are significant performance advantages to decomposing the overall task into several simpler sequential stages, commonly referred to as a pipeline model. Typically, such scenarios are also characterized by high sample complexity, motivating the study of active learning for these situations. While most active learning research exa...

Active Learning for Misspecified Models

Active learning is the problem in supervised learning to design the locations of training input points so that the generalization error is minimized. Existing active learning methods often assume that the model used for learning is correctly specified, i.e., the learning target function can be expressed by the model at hand. In many practical situations, however, this assumption may not be fulf...