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.
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.
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 • 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.
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 • 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.
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 • 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 • 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 • 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.
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 • 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).