Search Results for author: Koji Tsuda

Found 20 papers, 6 papers with code

On a linear fused Gromov-Wasserstein distance for graph structured data

no code implementations9 Mar 2022 Dai Hai Nguyen, Koji Tsuda

We present a framework for embedding graph structured data into a vector space, taking into account node features and topology of a graph into the optimal transport (OT) problem.

Probing conformational dynamics of antibodies with geometric simulations

no code implementations29 Sep 2021 Andrejs Tucs, Koji Tsuda, Adnan Sljoka

This chapter describes the application of constrained geometric simulations for prediction of antibody structural dynamics.

A generative model for molecule generation based on chemical reaction trees

no code implementations7 Jun 2021 Dai Hai Nguyen, Koji Tsuda

We propose a generative model to generate molecules via multi-step chemical reaction trees.

Continuous black-box optimization with quantum annealing and random subspace coding

no code implementations30 Apr 2021 Syun Izawa, Koki Kitai, Shu Tanaka, Ryo Tamura, Koji Tsuda

As QA specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution of QA has to be translated back.

Sample Space Truncation on Boltzmann Machines

no code implementations NeurIPS Workshop DL-IG 2020 Mahito Sugiyama, Koji Tsuda, Hiroyuki Nakahara

We present a lightweight variant of Boltzmann machines via sample space truncation, called a truncated Boltzmann machine (TBM), which has not been investigated before while can be naturally introduced from the log-linear model viewpoint.

Efficient Construction Method for Phase Diagrams Using Uncertainty Sampling

1 code implementation6 Dec 2018 Kei Terayama, Ryo Tamura, Yoshitaro Nose, Hidenori Hiramatsu, Hideo Hosono, Yasushi Okuno, Koji Tsuda

Furthermore, we show that using the US approach, undetected new phase can be rapidly found, and smaller number of initial sampling points are sufficient.

Materials Science Computational Physics

Transductive Boltzmann Machines

no code implementations21 May 2018 Mahito Sugiyama, Koji Tsuda, Hiroyuki Nakahara

We present transductive Boltzmann machines (TBMs), which firstly achieve transductive learning of the Gibbs distribution.

Population-based de novo molecule generation, using grammatical evolution

1 code implementation6 Apr 2018 Naruki Yoshikawa, Kei Terayama, Teruki Honma, Kenta Oono, Koji Tsuda

Automatic design with machine learning and molecular simulations has shown a remarkable ability to generate new and promising drug candidates.

Chemical Physics Biomolecules

Legendre Decomposition for Tensors

1 code implementation NeurIPS 2018 Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda

We present a novel nonnegative tensor decomposition method, called Legendre decomposition, which factorizes an input tensor into a multiplicative combination of parameters.

Tensor Decomposition

Bias-Variance Decomposition for Boltzmann Machines

no code implementations ICLR 2018 Mahito Sugiyama, Koji Tsuda, Hiroyuki Nakahara

We achieve bias-variance decomposition for Boltzmann machines using an information geometric formulation.

ChemTS: An Efficient Python Library for de novo Molecular Generation

2 code implementations29 Sep 2017 Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama, Koji Tsuda

Automatic design of organic materials requires black-box optimization in a vast chemical space.

Chemical Physics Computational Engineering, Finance, and Science

Selective Inference for Sparse High-Order Interaction Models

no code implementations 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., $> 10^{17}$).

Tensor Balancing on Statistical Manifold

1 code implementation ICML 2017 Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda

To theoretically prove the correctness of the algorithm, we model tensors as probability distributions in a statistical manifold and realize tensor balancing as projection onto a submanifold.

Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining

no code implementations15 Feb 2016 Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama, Koji Tsuda, Ichiro Takeuchi

The SPP method allows us to efficiently find a superset of all the predictive patterns in the database that are needed for the optimal predictive model.

Graph Mining

Selective Inference Approach for Statistically Sound Predictive Pattern Mining

no code implementations15 Feb 2016 Shinya Suzumura, Kazuya Nakagawa, Mahito Sugiyama, Koji Tsuda, Ichiro Takeuchi

The main obstacle of this problem is in the difficulty of taking into account the selection bias, i. e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases.

Selection bias Two-sample testing

Redesigning pattern mining algorithms for supercomputers

no code implementations27 Oct 2015 Kazuki Yoshizoe, Aika Terada, Koji Tsuda

Upcoming many core processors are expected to employ a distributed memory architecture similar to currently available supercomputers, but parallel pattern mining algorithms amenable to the architecture are not comprehensively studied.

Safe Feature Pruning for Sparse High-Order Interaction Models

no code implementations26 Jun 2015 Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama, Koji Tsuda, Ichiro Takeuchi

An SFS rule has a property that, if a feature satisfies the rule, then the feature is guaranteed to be non-active in the LASSO solution, meaning that it can be safely screened-out prior to the LASSO training process.

Sparse Learning

Submodularity Cuts and Applications

no code implementations NeurIPS 2009 Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, Jeff A. Bilmes

Several key problems in machine learning, such as feature selection and active learning, can be formulated as submodular set function maximization.

Active Learning

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