150 citations · 331 across the 15 of their papers we have counts for
16 papers
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
Katsuhiko Ishiguro, Kenta Oono, Kohei Hayashi
A graph neural network (GNN) is a good choice for predicting the chemical properties of molecules. Compared with other deep networks, however, the current performance of a GNN is l…
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara +1
Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP…
Data Interpolating Prediction: Alternative Interpretation of Mixup
Takuya Shimada, Shoichiro Yamaguchi, Kohei Hayashi +1
Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks. However, this strategy is not always effective due to the gap between a…
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis
Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida
In this paper, we study random subsampling of Gaussian process regression, one of the simplest approximation baselines, from a theoretical perspective. Although subsampling discard…
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words
Danushka Bollegala, Kohei Hayashi, Ken-ichi Kawarabayashi
Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different ta…
Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection
Huda Hakami, Danushka Bollegala, Hayashi Kohei
Representing the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, kn…
On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm
Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi
Tensor train (TT) decomposition provides a space-efficient representation for higher-order tensors. Despite its advantage, we face two crucial limitations when we apply the TT deco…
Minimizing Quadratic Functions in Constant Time
Kohei Hayashi, Yuichi Yoshida
A sampling-based optimization method for quadratic functions is proposed. Our method approximately solves the following -dimensional quadratic minimization problem in constant t…
Making Tree Ensembles Interpretable
Satoshi Hara, Kohei Hayashi
Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited. In this paper, we…
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach
Satoshi Hara, Kohei Hayashi
Tree ensembles, such as random forests and boosted trees, are renowned for their high prediction performance. However, their interpretability is critically limited due to the enorm…
A Tractable Fully Bayesian Method for the Stochastic Block Model
Kohei Hayashi, Takuya Konishi, Tatsuro Kawamoto
The stochastic block model (SBM) is a generative model revealing macroscopic structures in graphs. Bayesian methods are used for (i) cluster assignment inference and (ii) model sel…
Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias
Yohei Kondo, Kohei Hayashi, Shin-ichi Maeda
A common strategy for sparse linear regression is to introduce regularization, which eliminates irrelevant features by letting the corresponding weights be zeros. However, regulari…
Doubly Decomposing Nonparametric Tensor Regression
Masaaki Imaizumi, Kohei Hayashi
Nonparametric extension of tensor regression is proposed. Nonlinearity in a high-dimensional tensor space is broken into simple local functions by incorporating low-rank tensor dec…
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood
Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki
Factorized information criterion (FIC) is a recently developed approximation technique for the marginal log-likelihood, which provides an automatic model selection framework for a…
Factorized Asymptotic Bayesian Hidden Markov Models
Ryohei Fujimaki, Kohei Hayashi
This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed f…
Estimation of low-rank tensors via convex optimization
Ryota Tomioka, Kohei Hayashi, Hisashi Kashima
In this paper, we propose three approaches for the estimation of the Tucker decomposition of multi-way arrays (tensors) from partial observations. All approaches are formulated as…