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no code implementations • 12 Apr 2022 • Alex Wang, Haotong Liang, Austin McDannald, Ichiro Takeuchi, A. Gilad Kusne

In these systems, machine learning controls experiment design, execution, and analysis in a closed loop.

no code implementations • 8 Apr 2022 • Logan Saar, Haotong Liang, Alex Wang, Austin McDannald, Efrain Rodriguez, Ichiro Takeuchi, A. Gilad Kusne

We present the next generation in science education, a kit for building a low-cost autonomous scientist.

no code implementations • 14 Feb 2022 • Vo Nguyen Le Duy, Ichiro Takeuchi

In this paper, we study statistical inference on the similarity/distance between two time-series under uncertain environment by considering a statistical hypothesis test on the distance obtained from Dynamic Time Warping (DTW) algorithm.

no code implementations • 31 Jan 2022 • Yu Inatsu, Shion Takeno, Masayuki Karasuyama, Ichiro Takeuchi

In black-box function optimization, we need to consider not only controllable design variables but also uncontrollable stochastic environment variables.

no code implementations • 9 Dec 2021 • Eugene Ndiaye, Ichiro Takeuchi

Path-following algorithms are frequently used in composite optimization problems where a series of subproblems, with varying regularization hyperparameters, are solved sequentially.

no code implementations • 1 Dec 2021 • Valentin Stanev, Erik Skau, Ichiro Takeuchi, Boian S. Alexandrov

We utilize a recently developed topic modeling method called SeNMFk, extending the standard Non-negative Matrix Factorization (NMF) methods by incorporating the semantic structure of the text, and adding a robust system for determining the number of topics.

no code implementations • 16 Nov 2021 • Shunya Kusakawa, Shion Takeno, Yu Inatsu, Kentaro Kutsukake, Shogo Iwazaki, Takashi Nakano, Toru Ujihara, Masayuki Karasuyama, Ichiro Takeuchi

A cascade process is a multi-stage process in which the output of one stage is used as an input for the next stage.

no code implementations • 15 Nov 2021 • A. Gilad Kusne, Austin McDannald, Brian DeCost, Corey Oses, Cormac Toher, Stefano Curtarolo, Apurva Mehta, Ichiro Takeuchi

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades.

no code implementations • 18 Oct 2021 • Ryota Sugiyama, Hiroki Toda, Vo Nguyen Le Duy, Yu Inatsu, Ichiro Takeuchi

In this paper, we study statistical inference of change-points (CPs) in multi-dimensional sequence.

no code implementations • 29 Sep 2021 • Vo Nguyen Le Duy, Ichiro Takeuchi

In this paper, we study statistical inference for the Wasserstein distance, which has attracted much attention and has been applied to various machine learning tasks.

no code implementations • 8 Jul 2021 • Noriaki Hashimoto, Yusuke Takagi, Hiroki Masuda, Hiroaki Miyoshi, Kei Kohno, Miharu Nagaishi, Kensaku Sato, Mai Takeuchi, Takuya Furuta, Keisuke Kawamoto, Kyohei Yamada, Mayuko Moritsubo, Kanako Inoue, Yasumasa Shimasaki, Yusuke Ogura, Teppei Imamoto, Tatsuzo Mishina, Koichi Ohshima, Hidekata Hontani, Ichiro Takeuchi

To address this problem, we employ attention-based multiple instance learning, which enables us to focus on tumor-specific regions when the similarity between cases is computed.

no code implementations • 9 Jun 2021 • Diptesh Das, Vo Nguyen Le Duy, Hiroyuki Hanada, Koji Tsuda, Ichiro Takeuchi

Automated high-stake decision-making such as medical diagnosis requires models with high interpretability and reliability.

no code implementations • 11 May 2021 • Vo Nguyen Le Duy, Ichiro Takeuchi

The basic concept of conditional SI is to make the inference conditional on the selection event, which enables an exact and valid statistical inference to be conducted even when the hypothesis is selected based on the data.

no code implementations • 22 Apr 2021 • Toshiaki Tsukurimichi, Yu Inatsu, Vo Nguyen Le Duy, Ichiro Takeuchi

In practical data analysis under noisy environment, it is common to first use robust methods to identify outliers, and then to conduct further analysis after removing the outliers.

1 code implementation • 14 Apr 2021 • Eugene Ndiaye, Ichiro Takeuchi

Conformal prediction constructs a confidence set for an unobserved response of a feature vector based on previous identically distributed and exchangeable observations of responses and features.

no code implementations • 2 Mar 2021 • Chris Nelson, Anna N. Morozovska, Maxim A. Ziatdinov, Eugene A. Eliseev, Xiaohang Zhang, Ichiro Takeuchi, Sergei V. Kalinin

The evolution of the atomic structures of the combinatorial library of Sm-substituted thin film BiFeO3 along the phase transition boundary from the ferroelectric rhombohedral phase to the non-ferroelectric orthorhombic phase is explored using scanning transmission electron microscopy (STEM).

Data Analysis, Statistics and Probability Materials Science

no code implementations • 8 Feb 2021 • Yu Inatsu, Shogo Iwazaki, Ichiro Takeuchi

A natural measure of robustness is the probability that $f(\bm x, \bm w)$ exceeds a given threshold $h$, which is known as the \emph{probability threshold robustness} (PTR) measure in the literature on robust optimization.

1 code implementation • 25 Dec 2020 • Kazuya Sugiyama, Vo Nguyen Le Duy, Ichiro Takeuchi

Conditional SI has been mainly studied in the context of feature selection such as stepwise feature selection (SFS).

1 code implementation • 29 Oct 2020 • Rory Bunker, Keisuke Fujii, Hiroyuki Hanada, Ichiro Takeuchi

Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence.

no code implementations • 5 Oct 2020 • Vo Nguyen Le Duy, Shogo Iwazaki, Ichiro Takeuchi

To overcome this difficulty, we introduce a conditional selective inference (SI) framework -- a new statistical inference framework for data-driven hypotheses that has recently received considerable attention -- to compute exact (non-asymptotic) valid p-values for the segmentation results.

no code implementations • 17 Sep 2020 • Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi

As an AL problem in such an uncertain environment, we study Mean-Variance Analysis in Bayesian Optimization (MVA-BO) setting.

no code implementations • 22 Jun 2020 • Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi

In many product development problems, the performance of the product is governed by two types of parameters called design parameter and environmental parameter.

no code implementations • 11 Jun 2020 • A. Gilad Kusne, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo, Albert V. Davydov, Ritesh Agarwal, Leonid A. Bendersky, Mo Li, Apurva Mehta, Ichiro Takeuchi

Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1].

2 code implementations • 21 Apr 2020 • Vo Nguyen Le Duy, Ichiro Takeuchi

Unfortunately, the main limitation of the original SI approach for Lasso is that the inference is conducted not only conditional on the selected features but also on their signs -- this leads to loss of power because of over-conditioning.

1 code implementation • 31 Mar 2020 • Haotong Liang, Valentin Stanev, A. Gilad Kusne, Ichiro Takeuchi

Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics.

2 code implementations • NeurIPS 2020 • Vo Nguyen Le Duy, Hiroki Toda, Ryota Sugiyama, Ichiro Takeuchi

In this paper, we introduce a novel method to perform statistical inference on the significance of the CPs, estimated by a Dynamic Programming (DP)-based optimal CP detection algorithm.

no code implementations • 11 Feb 2020 • Maxim Ziatdinov, Chris Nelson, Xiaohang Zhang, Rama Vasudevan, Eugene Eliseev, Anna N. Morozovska, Ichiro Takeuchi, Sergei V. Kalinin

Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy, with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models.

Materials Science

2 code implementations • 3 Feb 2020 • Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama

Hence, we propose a supervised distance metric learning method for the graph classification problem.

1 code implementation • CVPR 2020 • Noriaki Hashimoto, Daisuke Fukushima, Ryoichi Koga, Yusuke Takagi, Kaho Ko, Kei Kohno, Masato Nakaguro, Shigeo Nakamura, Hidekata Hontani, Ichiro Takeuchi

We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI).

no code implementations • 9 Nov 2019 • Kota Matsui, Shunya Kusakawa, Keisuke Ando, Kentaro Kutsukake, Toru Ujihara, Ichiro Takeuchi

In this paper, we propose an active learning method for an inverse problem that aims to find an input that achieves a desired structured-output.

no code implementations • 26 Oct 2019 • Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi

In the manufacturing industry, it is often necessary to repeat expensive operational testing of machine in order to identify the range of input conditions under which the machine operates properly.

1 code implementation • NeurIPS 2019 • Eugene Ndiaye, Ichiro Takeuchi

If you are predicting the label $y$ of a new object with $\hat y$, how confident are you that $y = \hat y$?

no code implementations • 13 Sep 2019 • Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Ichiro Takeuchi

As part of a quality control process in manufacturing it is often necessary to test whether all parts of a product satisfy a required property, with as few inspections as possible.

no code implementations • CVPR 2020 • Kosuke Tanizaki, Noriaki Hashimoto, Yu Inatsu, Hidekata Hontani, Ichiro Takeuchi

To overcome this difficulty, we introduce a statistical approach called selective inference, and develop a framework to compute valid p-values in which the segmentation bias is properly accounted for.

no code implementations • 6 May 2019 • Vo Nguyen Le Duy, Takuto Sakuma, Taiju Ishiyama, Hiroki Toda, Kazuya Nishi, Masayuki Karasuyama, Yuta Okubo, Masayuki Sunaga, Yasuo Tabei, Ichiro Takeuchi

Given two groups of trajectories, the goal of this problem is to extract moving patterns in the form of sub-trajectories which are more similar to sub-trajectories of one group and less similar to those of the other.

no code implementations • 8 Mar 2019 • Yu Inatsu, Daisuke Sugita, Kazuaki Toyoura, Ichiro Takeuchi

We study active learning (AL) based on Gaussian Processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function.

no code implementations • ICML 2020 • Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama

In this paper, we focus on the information-based approach, which is a popular and empirically successful approach in BO.

1 code implementation • 12 Oct 2018 • Eugene Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi

Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task.

no code implementations • 3 Oct 2018 • Hiroki Kato, Hiroyuki Hanada, Ichiro Takeuchi

In this paper, we propose Safe Optimal Rule Fit (SORF) as an approach to resolve this problem, which is formulated as a convex optimization problem with sparse regularization.

no code implementations • 1 Mar 2018 • Hiroyuki Hanada, Toshiyuki Takada, Jun Sakuma, Ichiro Takeuchi

A drawback of this naive approach is that the uncertainty in the missing entries is not properly incorporated in the prediction.

no code implementations • 20 Feb 2018 • Valentin Stanev, Velimir V. Vesselinov, A. Gilad Kusne, Graham Antoszewski, Ichiro Takeuchi, Boian S. Alexandrov

Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.

no code implementations • ICLR 2019 • Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Ichiro Takeuchi, Ruslan Salakhutdinov, Kenji Fukumizu

In the paper, we propose a post selection inference (PSI) framework for divergence measure, which can select a set of statistically significant features that discriminate two distributions.

no code implementations • 15 Feb 2018 • Yao-Hung Hubert Tsai, Makoto Yamada, Denny Wu, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu

"Which Generative Adversarial Networks (GANs) generates the most plausible images?"

1 code implementation • 12 Feb 2018 • Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama

Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances and an instance in a different class.

1 code implementation • 8 Sep 2017 • Valentin Stanev, Corey Oses, A. Gilad Kusne, Efrain Rodriguez, Johnpierre Paglione, Stefano Curtarolo, Ichiro Takeuchi

Separate regression models are developed to predict the values of $T_{\mathrm{c}}$ for cuprate, iron-based, and "low-$T_{\mathrm{c}}$" compounds.

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., $> 10^{17}$).

no code implementations • 1 Jun 2017 • Yuta Umezu, Ichiro Takeuchi

We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence.

no code implementations • 21 Mar 2017 • Atsushi Shibagaki, Ichiro Takeuchi

We study primal-dual type stochastic optimization algorithms with non-uniform sampling.

no code implementations • 12 Oct 2016 • Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi

We propose a novel kernel based post selection inference (PSI) algorithm, which can not only handle non-linearity in data but also structured output such as multi-dimensional and multi-label outputs.

no code implementations • 1 Jun 2016 • Hiroyuki Hanada, Atsushi Shibagaki, Jun Sakuma, Ichiro Takeuchi

We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier.

no code implementations • 15 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.

no code implementations • 15 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.

no code implementations • 15 Feb 2016 • Toshiyuki Takada, Hiroyuki Hanada, Yoshiji Yamada, Jun Sakuma, Ichiro Takeuchi

The key property of SAG method is that, given an arbitrary approximate solution, it can provide a non-probabilistic assumption-free bound on the approximation quality under cryptographically secure computation framework.

no code implementations • 8 Feb 2016 • Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi

A significant advantage of considering them simultaneously rather than individually is that they have a synergy effect in the sense that the results of the previous safe feature screening can be exploited for improving the next safe sample screening performances, and vice-versa.

no code implementations • 12 Jul 2015 • Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Masayuki Karasuyama, Ichiro Takeuchi

An advantage of our homotopy approach is that it can be interpreted as simulated annealing, a common approach for finding a good local optimal solution in non-convex optimization problems.

no code implementations • 26 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.

no code implementations • 11 Apr 2015 • Shota Okumura, Yoshiki Suzuki, Ichiro Takeuchi

This property is quite advantageous in a typical sensitivity analysis task where only a small number of instances are updated.

1 code implementation • NeurIPS 2015 • Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, Ichiro Takeuchi

Careful tuning of a regularization parameter is indispensable in many machine learning tasks because it has a significant impact on generalization performances.

no code implementations • 10 Feb 2014 • Yoshiki Suzuki, Kohei Ogawa, Yuki Shinmura, Ichiro Takeuchi

If a reasonably good suboptimal model is available, our algorithm can compute lower and upper bounds of many useful quantities for making inferences on the unknown target model.

no code implementations • 27 Jan 2014 • Kohei Ogawa, Yoshiki Suzuki, Shinya Suzumura, Ichiro Takeuchi

Sparse classifiers such as the support vector machines (SVM) are efficient in test-phases because the classifier is characterized only by a subset of the samples called support vectors (SVs), and the rest of the samples (non SVs) have no influence on the classification result.

no code implementations • NeurIPS 2013 • Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima

We introduce a novel formulation of multi-task learning (MTL) called parametric task learning (PTL) that can systematically handle infinitely many tasks parameterized by a continuous parameter.

no code implementations • NeurIPS 2013 • Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi

However, Bayesian learning is often obstructed by computational difficulty: the rigorous Bayesian learning is intractable in many models, and its variational Bayesian (VB) approximation is prone to suffer from local minima.

no code implementations • NeurIPS 2012 • Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus D. Plessis, Song Liu, Ichiro Takeuchi

A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference.

no code implementations • NeurIPS 2011 • Ichiro Takeuchi, Masashi Sugiyama

We consider feature selection and weighting for nearest neighbor classifiers.

no code implementations • NeurIPS 2009 • Masayuki Karasuyama, Ichiro Takeuchi

Conventional single cremental decremental SVM can update the trained model efficiently when single data point is added to or removed from the training set.

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