no code implementations • 4 Sep 2018 • Tsung-Yu Hsieh, Yasser EL-Manzalawy, Yiwei Sun, Vasant Honavar
Many machine learning, statistical inference, and portfolio optimization problems require minimization of a composition of expected value functions (CEVF).
no code implementations • 6 Nov 2018 • Yiwei Sun, Ngot Bui, Tsung-Yu Hsieh, Vasant Honavar
Our experiments with several benchmark real-world single view networks show that GFC-based SVNE yields network embeddings that are competitive with or superior to those produced by the state-of-the-art single view network embedding methods when the embeddings are used for labeling unlabeled nodes in the networks.
no code implementations • 25 Jan 2019 • Yimin Zhou, Yiwei Sun, Vasant Honavar
We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning.
1 code implementation • 20 Aug 2019 • Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang
To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph.
Ranked #25 on Node Classification on Pubmed
no code implementations • 20 Aug 2019 • Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, Vasant Honavar
Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e. g., friendship, shared interests in music, etc.)
no code implementations • 14 Sep 2019 • Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, Vasant Honavar
Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes.
1 code implementation • 11 Nov 2019 • Junjie Liang, Dongkuan Xu, Yiwei Sun, Vasant Honavar
However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables.
no code implementations • 22 Nov 2019 • Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Charu Aggarwal, Prasenjit Mitra, Suhang Wang
Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values.
no code implementations • 27 Jan 2020 • Yiwei Sun, Shabnam Ghaffarzadegan
Recent advancements in audio event classification often ignore the structure and relation between the label classes available as prior information.
1 code implementation • 27 Jan 2020 • Enyan Dai, Yiwei Sun, Suhang Wang
Nowadays, Internet is a primary source of attaining health information.
no code implementations • 28 Jun 2020 • Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, Suhang Wang
Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.
1 code implementation • 23 Nov 2020 • Tsung-Yu Hsieh, Suhang Wang, Yiwei Sun, Vasant Honavar
Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions.
no code implementations • 8 Jun 2021 • Enyan Dai, Kai Shu, Yiwei Sun, Suhang Wang
We propose a novel generative framework named as ADDES which can synthesize high-quality labeled data for target classification tasks by learning from data with inexact supervision and the relations between inexact supervision and target classes.
no code implementations • 29 Nov 2023 • Yiwei Sun
This paper aims to provide a methodological blueprint to identify treatment effects away from the cutoff in various empirical settings by offering a non-exhaustive list of assumptions on the counterfactual outcome.