Several papers are accepted or just written on representation learning for graph embedding and similarity learning. These are great works of students in our group as well as collaborators.
- Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities (arXiv 2019)
- Robust Graph Embedding with Noisy Link Weights (arXiv)(AISTATS 2019 accepted papers)
- Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability (arXiv) (AISTATS 2019 accepted papers)
- A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks (arXiv) (ICML 2018)
As a related topic, papers on word embedding and image embedding are also accepted or published.
- Segmentation-free compositional n-gram embedding (arXiv)(NAACL-HLT 2019 accepted papers)
- Word-like character n-gram embedding (W-NUT 2018)
- Segmentation-Free Word Embedding for Unsegmented Languages (EMNLP 2017)
- Spectral Graph-Based Method of Multimodal Word Embedding (TextGraphs-11 2017)
- Image and tag retrieval by leveraging image-group links with multi-domain graph embedding (ICIP 2016)
- Cross-Lingual Word Representations via Spectral Graph Embeddings (ACL 2016) (CL-Eigenwords Website)
There are also several papers on theory and applications of statistics.
- An information criterion for auxiliary variable selection in incomplete data analysis (Entropy 2019)
- Selective Inference for Testing Trees and Edges in Phylogenetics (arXiv 2019)
- Transitivity vs Preferential Attachment: Determining the Driving Force Behind the Evolution of Scientific Co-Authorship Networks (ICCS 2018)
- Selective inference for the problem of regions via multiscale bootstrap (arXiv 2018)
- PAFit: an R Package for Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks (arXiv 2017)
- Joint estimation of preferential attachment and node fitness in growing complex networks (Scientific Reports 2016)
- Cross-validation of matching correlation analysis by resampling matching weights (Neural Networks 2016) (arXiv)