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The Role of Deconfounding in Meta-learning
Yinjie Jiang · Zhengyu Chen · Luotian Yuan · Ying WEI · Kun Kuang · Xinhai Ye · Zhihua Wang · Fei Wu

Tue Jul 19 03:30 PM -- 05:30 PM (PDT) @ Hall E #636

Meta-learning has emerged as a potent paradigm for quick learning of few-shot tasks, by leveraging the meta-knowledge learned from meta-training tasks. Well-generalized meta-knowledge that facilitates fast adaptation in each task is preferred; however, recent evidence suggests the undesirable memorization effect where the meta-knowledge simply memorizing all meta-training tasks discourages task-specific adaptation and poorly generalizes. There have been several solutions to mitigating the effect, including both regularizer-based and augmentation-based methods, while a systematic understanding of these methods in a single framework is still lacking. In this paper, we offer a novel causal perspective of meta-learning. Through the lens of causality, we conclude the universal label space as a confounder to be the causing factor of memorization and frame the two lines of prevailing methods as different deconfounder approaches. Remarkably, derived from the causal inference principle of front-door adjustment, we propose two frustratingly easy but effective deconfounder algorithms, i.e., sampling multiple versions of the meta-knowledge via Dropout and grouping the meta-knowledge into multiple bins. The proposed causal perspective not only brings in the two deconfounder algorithms that surpass previous works in four benchmark datasets towards combating memorization, but also opens a promising direction for meta-learning.

Author Information

Yinjie Jiang (Zhejiang University)
Zhengyu Chen (Zhejiang University)
Luotian Yuan (Zhejiang University)
Ying WEI (City University of Hong Kong)
Kun Kuang (Zhejiang University)

Kun Kuang, Associate Professor in the College of Computer Science and Technology, Zhejiang University. He received his Ph.D. in the Department of Computer Science and Technology at Tsinghua University in 2019. He was a visiting scholar at Stanford University. His main research interests include causal inference, Artificial Intelligence, and causally regularized machine learning. He has published over 30 papers in major international journals and conferences, including SIGKDD, ICML, ACM MM, AAAI, IJCAI, TKDE, TKDD, Engineering, and ICDM, etc.

Xinhai Ye (Zhejiang University)
Zhihua Wang (Shanghai Institute for Advanced Study of Zhejiang University)
Fei Wu (Zhejiang University, China)

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