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Selective Inference for Sparse High-Order Interaction Models

ICML 2017 • Shinya Suzumura • Kazuya Nakagawa • Yuta Umezu • Koji Tsuda • Ichiro Takeuchi

Finding statistically significant high-order interactions in predictive modeling is important but challenging task because the possible number of high-order interactions is extremely large (e.g., $> 10^{17}$). In this paper we study feature selection and statistical inference for sparse high-order interaction models... (read more)

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