期刊： MACHINE LEARNING, 2021; 110 (4)
Research showed that deep learning models are vulnerable to membership inference attacks, which aim to determine if an example is in the training set ......
期刊： MACHINE LEARNING, 2021; 110 (5)
Recently, some statistical topic modeling approaches have been widely applied in the field of supervised document classification. However, there are f......
期刊： MACHINE LEARNING, 2021; 110 (6)
In this paper, we study the asymptotic properties of regularized least squares with indefinite kernels in reproducing kernel Krein spaces (RKKS). By i......
期刊： MACHINE LEARNING, 2021; 110 (8)
Similar unlabeled (SU) classification is pervasive in many real-world applications, where only similar data pairs (two data points have the same label......
期刊： MACHINE LEARNING, 2021; 110 (6)
In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space. A......
期刊： MACHINE LEARNING, 2021; 110 (6)
Graph-based semi-supervised learning (GSSL) is an important paradigm among semi-supervised learning approaches and includes the two processes of graph......
期刊： MACHINE LEARNING, 2021; 110 (7)
Recently, the Tensor Nuclear Norm (TNN) regularization based on t-SVD has been widely used in various low tubal-rank tensor recovery tasks. However, t......
期刊： MACHINE LEARNING, 2021; 110 (6)
Traditional clustering algorithms focus on a single clustering result; as such, they cannot explore potential diverse patterns of complex real world d......
期刊： MACHINE LEARNING, 2021; 110 (8)
Principal component analysis (PCA) has been widely used as an effective technique for feature extraction and dimension reduction. In the High Dimensio......
期刊： MACHINE LEARNING, 2021; 110 (8)
Climate models (CM) are used to evaluate the impact of climate change on the risk of floods and heavy precipitation events. However, these numerical s......
期刊： MACHINE LEARNING, 2021; 110 (5)
With the emerging of massive short texts, e.g., social media posts and question titles from Q&A systems, discovering valuable information from the......
期刊： MACHINE LEARNING, 2021; 110 (6)
The predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is o......
期刊： MACHINE LEARNING, 2021; 110 (6)
A Bayesian inference framework for supervised Gaussian process latent variable models is introduced. The framework overcomes the high correlations bet......
期刊： MACHINE LEARNING, 2021; 110 (8)
Long-term forecasting involves predicting a horizon that is far ahead of the last observation. It is a problem of high practical relevance, for instan......
期刊： MACHINE LEARNING, 2021; 110 (9)
Reinforcement learning (RL) aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. T......
