Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vecto...
Recent results in complexity theory suggest that various economic theories require agents to solve computationally intractable problems. However, such results assume the agents ar...
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...
A surprising query result is often an indication of errors in the query or the underlying data. Recent work suggests using causal reasoning to find explanations for the surprisin...
Alexandra Meliou, Wolfgang Gatterbauer, Suman Nath...
We study the problem of computing approximate quantiles in large-scale sensor networks communication-efficiently, a problem previously studied by Greenwald and Khana [12] and Shri...