Results 1 to 10 of about 1,101,099 (365)
Learning to rank Higgs boson candidates [PDF]
Scientific Reports, 2022 In the extensive search for new physics, the precise measurement of the Higgs boson continues to play an important role. To this end, machine learning techniques have been recently applied to processes like the Higgs production via vector-boson fusion ...
Marius Köppel +6 more
doaj +2 more sources
Controlling Fairness and Bias in Dynamic Learning-to-Rank [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020 Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.
Abdollahpouri Himan +5 more
core +2 more sources
Software defect prediction using learning to rank approach [PDF]
Scientific Reports, 2023 Software defect prediction (SDP) plays a significant role in detecting the most likely defective software modules and optimizing the allocation of testing resources. In practice, though, project managers must not only identify defective modules, but also
Ali Bou Nassif +6 more
doaj +2 more sources
Deep metric learning to rank [PDF]
Computer Vision and Pattern Recognition, 2019 We propose a novel deep metric learning method by revisiting the learning to rank approach. Our method, named FastAP, optimizes the rank-based Average Precision measure, using an approximation derived from distance quantization.
Cakir, Fatih +4 more
core +2 more sources
Unbiased Learning to Rank with Unbiased Propensity Estimation [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018 Learning to rank with biased click data is a well-known challenge. A variety of methods has been explored to debias click data for learning to rank such as click models, result interleaving and, more recently, the unbiased learning-to-rank framework ...
Ai, Qingyao +4 more
core +2 more sources
Distributionally robust learning-to-rank under the Wasserstein metric [PDF]
PLoS ONE, 2023 Despite their satisfactory performance, most existing listwise Learning-To-Rank (LTR) models do not consider the crucial issue of robustness. A data set can be contaminated in various ways, including human error in labeling or annotation, distributional ...
Shahabeddin Sotudian +2 more
doaj +3 more sources
Learning-to-rank vs ranking-to-learn
Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020 In Continuous Integration (CI), regression testing is constrained by the time between commits. This demands for careful selection and/or prioritization of test cases within test suites too large to be run entirely. To this aim, some Machine Learning (ML) techniques have been proposed, as an alternative to deterministic approaches.
Antonia Bertolino +4 more
openaire +4 more sources
iPiDA-LTR: Identifying piwi-interacting RNA-disease associations based on Learning to Rank. [PDF]
PLoS Computational Biology, 2022 Piwi-interacting RNAs (piRNAs) are regarded as drug targets and biomarkers for the diagnosis and therapy of diseases. However, biological experiments cost substantial time and resources, and the existing computational methods only focus on identifying ...
Wenxiang Zhang, Jialu Hou, Bin Liu
doaj +2 more sources
NeuRank: learning to rank with neural networks for drug–target interaction prediction [PDF]
BMC Bioinformatics, 2021 Background Experimental verification of a drug discovery process is expensive and time-consuming. Therefore, recently, the demand to more efficiently and effectively identify drug–target interactions (DTIs) has intensified.
Xiujin Wu +3 more
doaj +2 more sources
Differentiable Unbiased Online Learning to Rank [PDF]
, 2018 Online Learning to Rank (OLTR) methods optimize rankers based on user interactions. State-of-the-art OLTR methods are built specifically for linear models. Their approaches do not extend well to non-linear models such as neural networks.
Harrie Oosterhuis, Maarten de Rijke
openalex +3 more sources