leo-p
commented
7 years ago Summary:
- Dataset:
- Objective: Automatically learn which Active Learning strategy to use.
- Code: here
Inner-workings:
They use the multi-armed bandit framework where each arm is an Active Learning strategy.
The core RL algorithm used is EXP4.P which is itself based on EXP4 (Exponential weighting for Exploration and Explotation with Experts). They make only slight adjustments to the reward function.
Algorithm:
Results:
Beats all other techniques most of the time and make sure that in the long run we use the best strategy.
https://www.csie.ntu.edu.tw/~htlin/paper/doc/aaai15albl.pdf
Pool-based active learning is an important technique that helps reduce labeling efforts within a pool of unlabeled instances. Currently, most pool-based active learning strategies are constructed based on some human-designed philosophy; that is, they reflect what human beings assume to be “good labeling questions.” However, while such human-designed philosophies can be useful on specific data sets, it is often difficult to establish the theoretical connection of those philosophies to the true learning performance of interest. In addition, given that a single human-designed philosophy is un- likely to work on all scenarios, choosing and blending those strategies under different scenarios is an important but challenging practical task. This paper tackles this task by letting the machines adaptively “learn” from the performance of a set of given strategies on a particular data set. More specifically, we design a learning algorithm that connects active learning with the well-known multi-armed bandit problem. Further, we postulate that, given an appropriate choice for the multi-armed bandit learner, it is possible to estimate the performance of different strategies on the fly. Extensive empirical studies of the resulting ALBL algorithm confirm that it performs better than state-of-the-art strategies and a lead- ing blending algorithm for active learning, all of which are based on human-designed philosophy.