Yuta Umezu

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2ranked-venue papers

0as first author

0since 2021 · last 2018

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 1 paper		Probabilistic and Bayesian machine learning · 50% Learning theory · 50%
Databases, data mining, and information retrieval 1 paper		Data mining · 50% Recommender systems · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Learning theory › hypothesis testing selective inference	0.3	1	2017	Selective Inference for Sparse High-Order Interaction Models · ICML 2017
Machine learning › Probabilistic and Bayesian machine learning statistical inference	0.3	1	2017	Selective Inference for Sparse High-Order Interaction Models · ICML 2017
Data mining › dimensionality reduction feature selection	0.3	1	2017	Selective Inference for Sparse High-Order Interaction Models · ICML 2017
Recommender systems › click-through rate prediction › feature interaction learning high-order feature interaction	0.3	1	2017	Selective Inference for Sparse High-Order Interaction Models · ICML 2017

Methods — techniques the papers use, named apart from their topics

selective inference framework · 0.6

Year	Publication	Venue	Position
2018	Post Selection Inference with Kernels abstract Finding a set of statistically significant features from complex data (e.g., nonlinear and/or multi-dimensional output data) is important for scientific discovery and has a number of practical applications including biomarker discovery. In this paper, we propose a kernel-based post-selection inference (PSI) algorithm that can find a set of statistically significant features from non-linearly related data. Specifically, our PSI algorithm is based on independence measures, and we call it the Hilbert-Schmidt Independence Criterion (HSIC)-based PSI algorithm (hsicInf). The novelty of hsicInf is that it can handle non-linearity and/or multi-variate/multi-class outputs through kernels. Through synthetic experiments, we show that hsicInf can find a set of statistically significant features for both regression and classification problems. We applied hsicInf to real-world datasets and show that it can successfully identify important features. Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi	AISTATS	2
2017	Selective Inference for Sparse High-Order Interaction Models abstract 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. Our main contribution is to extend recently developed selective inference framework for linear models to high-order interaction models by developing a novel algorithm for efficiently characterizing the selection event for the selective inference of high-order interactions. We demonstrate the effectiveness of the proposed algorithm by applying it to an HIV drug response prediction problem. Shinya Suzumura, Kazuya Nakagawa, Yuta Umezu, Koji Tsuda, Ichiro Takeuchi	ICML	3