{'fraction_non_alphanumeric': 0.018309859154929577, 'fraction_numerical': 0.0, 'mean_word_length': 6.329896907216495, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Regression with an Ensemble of Noisy Base Functions

Ensemble methods achieve state-of-the-art performance in many real-world regression problems while enjoying structural compatibility for modern decentralized computing architectures. However, the implementation of ensemble regression on distributed systems may compromise its cutting-edge performance due to computing and communication reliability issues. This paper introduces robust ensemble combining techniques designed to integrate multiple noisy predictions into a single reliable prediction. Experiments conducted with synthetic and real-world datasets in various noise regimes illustrate our robust methods' superiority over non-robust counterparts.