Search Results for author:
Found 18 papers, 7 papers with code
Statistical Test for Generated Hypotheses by Diffusion Models
In this study, we consider a medical diagnostic task using generated images by diffusion models, and propose a statistical test to quantify its reliability.
Statistical Test for Anomaly Detections by Variational Auto-Encoders
In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE).
Statistical Test for Attention Map in Vision Transformer
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.
Selective Inference for Changepoint detection by Recurrent Neural Network
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).
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference
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.
Bounded P-values in Parametric Programming-based Selective Inference
This additional conditions often causes the loss of power, and this issue is referred to as over-conditioning in [Fithian et al., 2014].
Valid P-Value for Deep Learning-Driven Salient Region
Various saliency map methods have been proposed to interpret and explain predictions of deep learning models.
Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detection
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.
Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference
In this paper, we study statistical inference of change-points (CPs) in multi-dimensional sequence.
Exact Statistical Inference for the Wasserstein Distance by Selective Inference
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.
Fast and More Powerful Selective Inference for Sparse High-order Interaction Model
Automated high-stake decision-making such as medical diagnosis requires models with high interpretability and reliability.
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming
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.
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation
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.
More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approach
Conditional SI has been mainly studied in the context of feature selection such as stepwise feature selection (SFS).
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
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.
Parametric Programming Approach for More Powerful and General Lasso Selective Inference
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.
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
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.
Statistically Discriminative Sub-trajectory Mining
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.
