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Found 17 papers, 6 papers with code
Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning
Through extensive experiments, we uncover the mode connectivity phenomenon in the LLMs continual learning scenario and find that it can strike a balance between plasticity and stability.
Disambiguated Node Classification with Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data across various domains.
Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels
Self-training has emerged as a widely popular framework to leverage the abundance of unlabeled data, which expands the training set by assigning pseudo-labels to selected unlabeled nodes.
Interpretable Imitation Learning with Dynamic Causal Relations
After the model is learned, we can obtain causal relations among states and action variables behind its decisions, exposing policies learned by it.
T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data
Discovering the shifted behaviors and the evolving patterns underlying the streaming data are important to understand the dynamic system.
Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations
Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations.
Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment
Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions.
TopoImb: Toward Topology-level Imbalance in Learning from Graphs
To address this problem, we propose a new framework {\method} and design (1 a topology extractor, which automatically identifies the topology group for each instance with explicit memory cells, (2 a training modulator, which modulates the learning process of the target GNN model to prevent the case of topology-group-wise under-representation.
Explanation Guided Contrastive Learning for Sequential Recommendation
Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user adoptions.
Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks
In many real-world scenarios, node classes are imbalanced, with some majority classes making up most parts of the graph.
Towards Faithful and Consistent Explanations for Graph Neural Networks
Two typical reasons of spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input.
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society.
Exploring Edge Disentanglement for Node Classification
Concretely, these self-supervision tasks are enforced on a designed edge disentanglement module to be trained jointly with the downstream node classification task to encourage automatic edge disentanglement.
Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
After discussion, it is found that data-driven models integrated engineering or physical knowledge can significantly improve the urban building energy simulation.
Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features
Though the sensitive attribute of each data sample is unknown, we observe that there are usually some non-sensitive features in the training data that are highly correlated with sensitive attributes, which can be used to alleviate the bias.
Semi-Supervised Graph-to-Graph Translation
For example, we can easily build graphs representing peoples' shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain.
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks
This task is non-trivial, as previous synthetic minority over-sampling algorithms fail to provide relation information for newly synthesized samples, which is vital for learning on graphs.
