Search Results for author: Vo Nguyen Le Duy

Found 11 papers, 3 papers with code

Exact Statistical Inference for Time Series Similarity using Dynamic Time Warping by Selective Inference

no code implementations14 Feb 2022 Vo Nguyen Le Duy, Ichiro Takeuchi

In this paper, 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.

Decision Making Dynamic Time Warping +1

Exact Statistical Inference for the Wasserstein Distance by Selective Inference

no code implementations29 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.

More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming

no code implementations11 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.

Model Selection

Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation

no code implementations22 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.

Outlier Detection

Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference

no code implementations5 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.

Semantic Segmentation Two-sample testing

Parametric Programming Approach for More Powerful and General Lasso Selective Inference

2 code implementations21 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.

feature selection

Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming

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.

Statistically Discriminative Sub-trajectory Mining

no code implementations6 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.

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