Active Learning with Logged Data
This repository contains a python implementation of experiments in paper:
Songbai Yan, Kamalika Chaudhuri, Tara Javidi. "Active Learning with Logged Data." Proceedings of the 35th International Conference on Machine Learning, PMLR 80:5521-5530, 2018.
- algos.py Implementation of active learning algorithms
- data.py Preprocessing of data sets
- experiments.py Implementation of experiments
- logger.py Logging information for plots and reports
- main.py The main file
- model.py Implementation of a linear model
- opt.py Implementation of stochastic gradient descent with weighted samples
- utils.py Auxiliary functions
Data sets should be put in "../data", and results are located in "../results/".