We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
Data races are a major contributor to parallel software unreliability. A type of race that is both common and typically harmful is the Asymmetric data race. It occurs when at leas...
Point samples with different spectral noise properties (often defined using color names such as white, blue, green, and red) are important for many science and engineering discip...
Abstract. Linearizability is a commonly accepted notion of correctness for libraries of concurrent algorithms. Unfortunately, it assumes a complete isolation between a library and ...
Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise re...