Human-Namable visual attributes are promising in leveraging various recognition tasks. Intuitively, the more accurate the attribute prediction is, the more the recognition tasks can benefit. Relative attributes [1] learns a ranking function per attribute which can provide more accurate attribute prediction, thus, show clear advantages over previous binary attribute. In this paper, we inherit th...

When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and improve productivity. This paper introduces an adaptive ran...

When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and improve productivity. This project introduces an adaptive r...

Adaptation techniques based on importance weighting were shown effective for RankSVM and RankNet, viz., each training instance is assigned a target weight denoting its importance to the target domain and incorporated into loss functions. In this work, we extend RankBoost using importance weighting framework for ranking adaptation. We find it non-trivial to incorporate the target weight into the...

Many online or local data sources provide powerful querying mechanisms but limited ranking capabilities. For instance, PubMed allows users to submit highly expressive Boolean keyword queries, but ranks the query results by date only. However, a user would typically prefer a ranking by relevance, measured by an Information Retrieval (IR) ranking function. The naive approach would be to submit a ...

Achieving a better rank online is often costly. Is the effect of ranking different for mobile devices and traditional PC? This study empirically examines the ranking effect across different device types in an ecommerce environment. With over 4 million observations from Tweaker.net, the largest shopbot in Netherlands, we estimated the ranking effect between mobile and PC. Surprisingly, and contr...

Deep web search engines face the formidable challenge of retrieving high quality results from the vast collection of searchable databases. Deep web search is a two step process of selecting the high quality sources and ranking the results from the selected sources. Though there are existing methods for both the steps, they assess the relevance of the sources and the results using the query-resu...