Search Results for author:
Found 8 papers, 2 papers with code
MTRec: Multi-Task Learning over BERT for News Recommendation
With the adoption of large pre-trained models like BERT in news recommendation, the above way to incorporate multi-field information may encounter challenges: the shallow feature encoding to compress the category and entity information is not compatible with the deep BERT encoding.
Boosted Histogram Transform for Regression
In this paper, we propose a boosting algorithm for regression problems called \textit{boosted histogram transform for regression} (BHTR) based on histogram transforms composed of random rotations, stretchings, and translations.
Bagged Regularized $k$-Distances for Anomaly Detection
We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled examples.
Optimal Locally Private Nonparametric Classification with Public Data
In this work, we investigate the problem of public data-assisted non-interactive LDP (Local Differential Privacy) learning with a focus on non-parametric classification.
Augmented Abstractive Summarization With Document-LevelSemantic Graph
Previous abstractive methods apply sequence-to-sequence structures to generate summary without a module to assist the system to detect vital mentions and relationships within a document.
Under-bagging Nearest Neighbors for Imbalanced Classification
In this paper, we propose an ensemble learning algorithm called \textit{under-bagging $k$-nearest neighbors} (\textit{under-bagging $k$-NN}) for imbalanced classification problems.
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression
In this paper, we propose a gradient boosting algorithm for large-scale regression problems called \textit{Gradient Boosted Binary Histogram Ensemble} (GBBHE) based on binary histogram partition and ensemble learning.
Density-based Clustering with Best-scored Random Forest
Single-level density-based approach has long been widely acknowledged to be a conceptually and mathematically convincing clustering method.
