Robust Learning from Untrusted Sources

By Nikola Konstantinov, Christoph H. Lampert. 2019

77 citations recorded by the source corpus.

Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from external sources, e.g. via crowdsourcing. Unfortunately, the quality of these sources is not always guaranteed. As additional complications, the data might be stored in a distributed way, or might even have to remain private. In this work, we address the question of how to learn robustly in such scenarios. Studying the problem through the lens of statistical learning theory, we derive a procedure that allows for learning from all available sources, yet automatically suppresses irrelevant or corrupted data. We show by extensive experiments that our method provides significant improvements over alternative approaches from robust statistics and distributed optimization.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Multiple-Source Adaptation with Domain Classifiers (2020, neutral): “…al., 2008, 2009a, Hoffman et al., 2018, Pan and Yang, 2010, Muandet et al., 2013, Xu et al., 2014, Hoffman et al., 2012, Gong et al., 2013a,b, Zhang et al., 2015, Ganin et al., 2016, Tzeng et al., 2015, Motiian et al., 2017b,a, Wang et al., 2019, Konstantinov and Lampert, 2019, Liu et al., 2015].”
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data (2020, methodology): “We note that LMSA-Min-max performed better using all 1280 target samples as D̂0, whereas Konstantinov and Lampert (2019), LMSA, and LMSA-Boost performed better using 1024 target samples as a separate new source domain.”
On the Sample Complexity of Adversarial Multi-Source PAC Learning (2020, methodology): “In Konstantinov & Lampert (2019), a learning system has access to multiple datasets, some of which are manipulated, and the authors prove a generalization bound and propose an algorithm based on learning with a weighted combination of all datasets.”