1 citations · 1 across the 13 of their papers we have counts for
14 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…
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…