Noisy matrix completion is a fundamental problem in statistical learning and has attracted a substantial amount of interest over the last two decades. In a variety of applications, the missingness pattern is highly informative, yet it has received relatively less attention. Most existing methods either overlook this so...
Xuan-Yu Chen, Xiao-Yue Niu, Gong-Jun Xu et al.· 0 citations
Hypergraphs record multi-way interactions among entities. Extracting information from the combinatorial structure underlying observed multi-way interactions is a central task in many real-world problems. Existing methods face several limitations. First, many deep architectures for hypergraphs do not explicitly exploit...
Zi-Meng Li, Shi-Hao Wu, Gong-Jun Xu et al.· 0 citations
Mixed-type data containing both numerical and categorical variables arise in many scientific and real-world applications. Existing representation learning and generative modeling approaches typically focus either on reconstruction accuracy or unconditional data generation, but often fail to recover the full conditional...
A family of flexible network-assisted models built upon a generalization of random forests (RF+) is proposed, which achieves highly-competitive prediction accuracy and can be understood through intrinsic interpretability measures, derived directly from the model parameters and structure.
Tiffany M. Tang, E. Levina, Ji Zhu· arXiv.org· 1 citation· ⚡1
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