Impala: A Flexible Adaptive Sampling Algorithm for Active Learning

Abstract

The Implicit Potential Active Learning Algorithm, or impala, is a novel approach to adaptive sampling that allows for the blending of multiple independent query strategies into a common batch active learning framework. This methodology is agnostic to both the underlying learning model and the employed query strategies. In essence, impala treats query strategies as potential functions, which are combined into a probability of candidate selection. The end result is a flexible algorithm that can dynamically balance exploration and exploitation whose inherent variability guards against over-zealous greedy searching.