Search Results for author:
Found 14 papers, 4 papers with code
Extrapolating Away from the Cutoff in Regression Discontinuity Designs
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.
Labeled Data Generation with Inexact Supervision
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.
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals
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.
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks
Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.
An Ontology-Aware Framework for Audio Event Classification
Recent advancements in audio event classification often ignore the structure and relation between the label classes available as prior information.
Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository
Nowadays, Internet is a primary source of attaining health information.
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values.
LMLFM: Longitudinal Multi-Level Factorization Machine
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.
Node Injection Attacks on Graphs via Reinforcement Learning
Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes.
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
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
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MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
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.)
Improving Image Captioning by Leveraging Knowledge Graphs
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.
Multi-View Network Embedding Via Graph Factorization Clustering and Co-Regularized Multi-View Agreement
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.
Compositional Stochastic Average Gradient for Machine Learning and Related Applications
Many machine learning, statistical inference, and portfolio optimization problems require minimization of a composition of expected value functions (CEVF).
