9 papers
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 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…