Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose to incorporate few-shot learning frameworks for fast adaptations to graph classes with limited labeled graphs. Specifically, these works propose to accumulate meta-knowledge across diverse meta-training tasks, and then generalize such meta-knowledge to the target task with a disjoint label set. However, existing methods generally ignore task correlations among meta-training tasks while treating them independently. Nevertheless, such task correlations can advance the model generalization to the target task for better classification performance. On the other hand, it remains non-trivial to utilize task correlations due to the complex components in a large number of meta-training tasks. To deal with this, we propose a novel few-shot learning framework FAITH that captures task correlations via constructing a hierarchical task graph at different granularities. Then we further design a loss-based sampling strategy to select tasks with more correlated classes. Moreover, a task-specific classifier is proposed to utilize the learned task correlations for few-shot classification. Extensive experiments on four prevalent few-shot graph classification datasets demonstrate the superiority of FAITH over other state-of-the-art baselines.
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A Primal-Dual Subgradient Approach for Fair Meta Learning
The problem of learning to generalize on unseen
classes during the training step, also known as few-shot classification,
has attracted considerable attention. Initialization
based methods, such as the gradient-based model agnostic meta-learning
(MAML) [1], tackle the few-shot learning problem by
“learning to fine-tune”. The goal of these approaches is to learn
proper model initialization so that the classifiers for new classes
can be learned from a few labeled examples with a small number
of gradient update steps. Few shot meta-learning is well-known
with its fast-adapted capability and accuracy generalization onto
unseen tasks [2]. Learning fairly with unbiased outcomes is
another significant hallmark of human intelligence, which is
rarely touched in few-shot meta-learning. In this work, we
propose a novel Primal-Dual Fair Meta-learning framework,
namely PDFM, which learns to train fair machine learning
models using only a few examples based on data from related
tasks. The key idea is to learn a good initialization of a fair
model’s primal and dual parameters so that it can adapt to a
new fair learning task via a few gradient update steps. Instead
of manually tuning the dual parameters as hyperparameters
via a grid search, PDFM optimizes the initialization of the
primal and dual parameters jointly for fair meta-learning via
a subgradient primal-dual approach. We further instantiate an
example of bias controlling using decision boundary covariance
(DBC) [3] as the fairness constraint for each task, and demonstrate
the versatility of our proposed approach by applying it
to classification on a variety of three real-world datasets. Our
experiments show substantial improvements over the best prior
work for this setting.
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- Award ID(s):
- 1954409
- NSF-PAR ID:
- 10287548
- Date Published:
- Journal Name:
- Proceedings of the IEEE International Conference on Data Mining (ICDM 2020)
- Page Range / eLocation ID:
- 821-830
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The problem of few-shot graph classification targets at assigning class labels for graph samples, where only limited labeled graphs are provided for each class. To solve the problem brought by label scarcity, recent studies have proposed to adopt the prevalent few-shot learning framework to achieve fast adaptations to graph classes with limited labeled graphs. In particular, these studies typically propose to accumulate meta-knowledge across a large number of meta-training tasks, and then generalize such meta-knowledge to meta-test tasks sampled from a disjoint class set. Nevertheless, existing studies generally ignore the crucial task correlations among meta-training tasks and treat them independently. In fact, such task correlations can help promote the model generalization to meta-test tasks and result in better classification performance. On the other hand, it remains challenging to capture and utilize task correlations due to the complex components and interactions in meta-training tasks. To deal with this, we propose a novel few-shot graph classification framework FAITH to capture task correlations via learning a hierarchical task structure at different granularities. We further propose a task-specific classifier to incorporate the learned task correlations into the few-shot graph classification process. Moreover, we derive FAITH+, a variant of FAITH that can improve the sampling process for the hierarchical task structure. The extensive experiments on four prevalent graph datasets further demonstrate the superiority of FAITH and FAITH+ over other state-of-the-art baselines.
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
