Advancements in federated learning: Models, methods, and privacy
Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …
concerns. Its main ingredient is to cooperatively learn the model among the distributed …
A review on instance ranking problems in statistical learning
T Werner - Machine Learning, 2022 - Springer
Ranking problems, also known as preference learning problems, define a widely spread
class of statistical learning problems with many applications, including fraud detection …
class of statistical learning problems with many applications, including fraud detection …
Learning urban community structures: A collective embedding perspective with periodic spatial-temporal mobility graphs
Learning urban community structures refers to the efforts of quantifying, summarizing, and
representing an urban community's (i) static structures, eg, Point-Of-Interests (POIs) …
representing an urban community's (i) static structures, eg, Point-Of-Interests (POIs) …
A cross-benchmark comparison of 87 learning to rank methods
Learning to rank is an increasingly important scientific field that comprises the use of
machine learning for the ranking task. New learning to rank methods are generally …
machine learning for the ranking task. New learning to rank methods are generally …
Sparse real estate ranking with online user reviews and offline moving behaviors
Ranking residential real estates based on investment values can provide decision making
support for home buyers and thus plays an important role in estate marketplace. In this …
support for home buyers and thus plays an important role in estate marketplace. In this …
Nonconvex regularizations for feature selection in ranking with sparse SVM
Feature selection in learning to rank has recently emerged as a crucial issue. Whereas
several preprocessing approaches have been proposed, only a few have focused on …
several preprocessing approaches have been proposed, only a few have focused on …
Deep neural network regularization for feature selection in learning-to-rank
Learning-to-rank is an emerging area of research for a wide range of applications. Many
algorithms are devised to tackle the problem of learning-to-rank. However, very few existing …
algorithms are devised to tackle the problem of learning-to-rank. However, very few existing …
Measuring urban vibrancy of residential communities using big crowdsourced geotagged data
The pervasiveness of mobile and sensing technologies today has facilitated the creation of
Big Crowdsourced Geotagged Data (BCGD) from individual users in real time and at …
Big Crowdsourced Geotagged Data (BCGD) from individual users in real time and at …
Fast total-variation based image restoration based on derivative alternated direction optimization methods
The total variation (TV) model is one of the most successful methods for image restoration,
as well as an ideal bed to develop optimization algorithms for solving sparse representation …
as well as an ideal bed to develop optimization algorithms for solving sparse representation …
A decomposition-based multi-objective immune algorithm for feature selection in learning to rank
W Li, Z Chai, Z Tang - Knowledge-Based Systems, 2021 - Elsevier
Abstract Learning-to-rank (L2R) based on feature selection has been proved effectively.
However, feature selection problem is more challenging due to two conflicting objectives …
However, feature selection problem is more challenging due to two conflicting objectives …