2024/05/24 by Vinod Raman, Raman, Vinod, Unique Subedi +3
Medicine · #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Influenza Virus Research Studies #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2405.15424
openalex publication_date 2024/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study online classification under smoothed adversaries. In this setting, at each time point, the adversary draws an example from a distribution that has a bounded density with respect to a fixed base measure, which is known apriori to the learner. For binary classification and scalar-valued regression, previous works \citephaghtalab2020smoothed, block2022smoothed have shown that smoothed online learning is as easy as learning in the iid batch setting under PAC model. However, we show that smoothed online classification can be harder than the iid batch classification when the label space is unbounded. In particular, we construct a hypothesis class that is learnable in the iid batch setting under the PAC model but is not learnable under the smoothed online model. Finally, we identify a condition that ensures that the PAC learnability of a hypothesis class is sufficient for its smoothed online learnability.