6 citations · 7 across the 15 of their papers we have counts for
17 papers
Selective Inference for Deep Clustering in Latent Spaces
Eina Mizui, Tomohiro Shiraishi, Shunichi Nishino +1
Deep clustering is a powerful approach for discovering meaningful structures in high-dimensional data by learning a low-dimensional latent representation prior to clustering. Despi…
Statistical Testing for Multiple Instance Learning via Selective Inference with Applications to Computational Pathology
Noriaki Hashimoto, Shuichi Nishino, Teruyuki Katsuoka +7
Multiple instance learning (MIL) is widely used in computational pathology because it enables weakly supervised analysis of whole-slide images (WSIs) without requiring patch-level…
Automatic Statistical Test for Rationally Expressible Algorithms by Selective Inference, with Applications to Feature Selection
Teruyuki Katsuoka, Tomohiro Shiraishi, Shuichi Nishino +1
Selective inference (SI) provides statistically valid -values for hypotheses selected by applying an algorithm to the data, correcting for the bias that arises when the same dat…
Post-ADC Inference: Valid Inference After Active Data Collection
Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka +1
The validity of statistical inference depends critically on how data are collected. When data gathered through active data collection (ADC) are reused for a post-hoc inferential ta…
Statistical Testing Framework for Clustering Pipelines by Selective Inference
Yugo Miyata, Tomohiro Shiraishi, Shuichi Nishino +1
A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating multiple analysis algorithms. In many practical applicat…
Statistical Test for Saliency Maps of Graph Neural Networks via Selective Inference
Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka +1
Graph Neural Networks (GNNs) have gained prominence for their ability to process graph-structured data across various domains. However, interpreting GNN decisions remains a signifi…
Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inference
Mizuki Niihori, Shuichi Nishino, Teruyuki Katsuoka +3
In real-world applications, anomaly detection (AD) often operates without access to anomalous data, necessitating semi-supervised methods that rely solely on normal data. Among the…
Change Point Detection in the Frequency Domain with Statistical Reliability
Akifumi Yamada, Tomohiro Shiraishi, Shuichi Nishino +3
Effective condition monitoring in complex systems requires identifying change points (CPs) in the frequency domain, as the structural changes often arise across multiple frequencie…
si4onnx: A Python package for Selective Inference in Deep Learning Models
Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa +2
In this paper, we introduce si4onnx, a package for performing selective inference on deep learning models. Techniques such as CAM in XAI and reconstruction-based anomaly detection…
Statistical Test for Auto Feature Engineering by Selective Inference
Tatsuya Matsukawa, Tomohiro Shiraishi, Shuichi Nishino +2
Auto Feature Engineering (AFE) plays a crucial role in developing practical machine learning pipelines by automating the transformation of raw data into meaningful features that en…
Statistical Test for Feature Selection Pipelines by Selective Inference
Tomohiro Shiraishi, Tatsuya Matsukawa, Shuichi Nishino +1
A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating various analysis algorithms. In this paper, we propose a…
Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference
Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa +2
Anomaly localization in images -- identifying regions that deviate from normal patterns -- is vital in applications such as medical diagnosis and industrial inspection. A recent tr…
Statistical Test for Anomaly Detections by Variational Auto-Encoders
Daiki Miwa, Tomohiro Shiraishi, Vo Nguyen Le Duy +2
In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE). Over the last decade, VAE-based AD has been actively studied in…
Statistical Test for Attention Map in Vision Transformer
Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka +3
The Vision Transformer (ViT) demonstrates exceptional performance in various computer vision tasks. Attention is crucial for ViT to capture complex wide-ranging relationships among…
Multi-agent statistical discriminative sub-trajectory mining and an application to NBA basketball
Rory Bunker, Vo Nguyen Le Duy, Yasuo Tabei +2
Improvements in tracking technology through optical and computer vision systems have enabled a greater understanding of the movement-based behaviour of multiple agents, including i…
Selective Inference for Changepoint detection by Recurrent Neural Network
Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy +1
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). Thanks to its…
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference
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. The distinct…