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Found 18 papers, 4 papers with code
Topological Augmentation for Class-Imbalanced Node Classification
Class imbalance is prevalent in real-world node classification tasks and often biases graph learning models toward majority classes.
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures.
Characterizing Long-Tail Categories on Graphs
However, there is limited literature that provides a theoretical tool to characterize the behaviors of long-tail categories on graphs and understand the generalization performance in real scenarios.
FairGen: Towards Fair Graph Generation
In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability.
Fairness-aware Multi-view Clustering
We also derive insights regarding the relative performance of the proposed regularizers in various scenarios.
STERLING: Synergistic Representation Learning on Bipartite Graphs
Self-Supervised Learning (SSL) is a promising paradigm to address this challenge.
Retrieval Based Time Series Forecasting
In this paper, we show both theoretically and empirically that the uncertainty could be effectively reduced by retrieving relevant time series as references.
ARIEL: Adversarial Graph Contrastive Learning
In this work, by introducing an adversarial graph view for data augmentation, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to extract informative contrastive samples within reasonable constraints.
COIN: Co-Cluster Infomax for Bipartite Graphs
We theoretically prove that COIN is able to effectively increase the mutual information of node embeddings and COIN is upper-bounded by the prior distributions of nodes.
Adversarial Graph Contrastive Learning with Information Regularization
Contrastive learning is an effective unsupervised method in graph representation learning.
Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis
Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in corporate fundamentals.
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation
In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness.
On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation
In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness.
Heterogeneous Contrastive Learning
We first provide a theoretical analysis showing that the vanilla contrastive learning loss easily leads to the sub-optimal solution in the presence of false negative pairs, whereas the proposed weighted loss could automatically adjust the weight based on the similarity of the learned representations to mitigate this issue.
Network of Tensor Time Series
We propose a novel model called Network of Tensor Time Series, which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN).
Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States
Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e. g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
Task-Based Learning via Task-Oriented Prediction Network with Applications in Finance
Real-world applications often involve domain-specific and task-based performance objectives that are not captured by the standard machine learning losses, but are critical for decision making.
PAGAN: Portfolio Analysis with Generative Adversarial Networks
Sound financial theories demonstrate that in an efficient marketplace all information available today, including expectations on future events, are represented in today prices whereas future price trend is driven by the uncertainty.
Computational Finance Statistical Finance
