The enormous number of questions needed to acquire a full preference model when the size of the outcome space is large forces us to work with partial models that approximate the u...
We propose a novel self-training method for a parser which uses a lexicalised grammar and supertagger, focusing on increasing the speed of the parser rather than its accuracy. The...
Jonathan K. Kummerfeld, Jessika Roesner, Tim Dawbo...
We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen t...
Omer Tamuz, Ce Liu, Serge Belongie, Ohad Shamir, A...
In this work, we propose adaptive frequency-domain biased estimation algorithms with mechanisms to automatically adjust the shrinkage factors. The proposed estimation algorithms i...
This paper presents a novel Mod-4 steganographic method in discrete cosine transform (DCT) domain. A group of 2?2 quantized DCT coefficients (GQC) is selected as the valid embeddi...