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Poster
Integrated Latent Heterogeneity and Invariance Learning in Kernel Space
Jiashuo Liu · Zheyuan Hu · Peng Cui · Bo Li · Zheyan Shen

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @
in Poster Session 5 »
The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i.i.d$ testing data. Recently, invariant learning methods for out-of-distribution (OOD) generalization propose to find causally invariant relationships with multi-environments. However, modern datasets are frequently multi-sourced without explicit source labels, rendering many invariant learning methods inapplicable. In this paper, we propose Kernelized Heterogeneous Risk Minimization (KerHRM) algorithm, which achieves both the latent heterogeneity exploration and invariant learning in kernel space, and then gives feedback to the original neural network by appointing invariant gradient direction. We theoretically justify our algorithm and empirically validate the effectiveness of our algorithm with extensive experiments.
Keywords: Deep Learning · Reinforcement Learning and Planning · Machine Learning

Author Information

Jiashuo Liu (Tsinghua University)
Zheyuan Hu (Tsinghua University, Tsinghua University)
Peng Cui (Tsinghua University)
Bo Li (Tsinghua University)
Zheyan Shen (Tsinghua University)

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