no code implementations • 19 Feb 2024 • Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy, Ichiro Takeuchi
In this study, we consider a medical diagnostic task using generated images by diffusion models, and propose a statistical test to quantify its reliability.
no code implementations • 6 Feb 2024 • Daiki Miwa, Tomohiro Shiraishi, Vo Nguyen Le Duy, Teruyuki Katsuoka, Ichiro Takeuchi
In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE).
1 code implementation • 16 Jan 2024 • Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka, Vo Nguyen Le Duy, Kouichi Taji, Ichiro Takeuchi
In this study, we propose a statistical test for ViT's attentions, enabling us to use the attentions as reliable quantitative evidence indicators for ViT's decision-making with a rigorously controlled error rate.
1 code implementation • 25 Nov 2023 • Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy, Ichiro Takeuchi
In this study, we investigate the quantification of the statistical reliability of detected change points (CPs) in time series using a Recurrent Neural Network (RNN).
no code implementations • 23 Oct 2023 • Vo Nguyen Le Duy, Hsuan-Tien Lin, Ichiro Takeuchi
We propose a novel statistical method for testing the results of anomaly detection (AD) under domain adaptation (DA), which we call CAD-DA -- controllable AD under DA.
1 code implementation • 21 Jul 2023 • Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy, Ichiro Takeuchi
This additional conditions often causes the loss of power, and this issue is referred to as over-conditioning in [Fithian et al., 2014].
no code implementations • 6 Jan 2023 • Daiki Miwa, Vo Nguyen Le Duy, Ichiro Takeuchi
Various saliency map methods have been proposed to interpret and explain predictions of deep learning models.
no code implementations • 14 Feb 2022 • Vo Nguyen Le Duy, Ichiro Takeuchi
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 • 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 • 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 • 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).
2 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.
3 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.
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 • 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.