activity
20172024
most citedGoal-Aware RSS for Complex Scenarios via Program Logic

16 citations · 35 across the 16 of their papers we have counts for

collaborators

22 papers

cs.SE2024

StatWhy: Formal Verification Tool for Statistical Hypothesis Testing Programs

Yusuke Kawamoto, Kentaro Kobayashi, Kohei Suenaga

Statistical methods have been widely misused and misinterpreted in various scientific fields, raising significant concerns about the integrity of scientific research. To mitigate t…

cs.SE2023

Probabilistic Black-Box Checking via Active MDP Learning

Junya Shijubo, Masaki Waga, Kohei Suenaga

We introduce a novel methodology for testing stochastic black-box systems, frequently encountered in embedded systems. Our approach enhances the established black-box checking (BBC…

cs.DC2023

Learning nonlinear hybrid automata from input--output time-series data

Amit Gurung, Masaki Waga, Kohei Suenaga

Learning an automaton that approximates the behavior of a black-box system is a long-studied problem. Besides its theoretical significance, its application to search-based testing…

cs.CV2022

BOREx: Bayesian-Optimization--Based Refinement of Saliency Map for Image- and Video-Classification Models

Atsushi Kikuchi, Kotaro Uchida, Masaki Waga +1

Explaining a classification result produced by an image- and video-classification model is one of the important but challenging issues in computer vision. Many methods have been pr…

cs.AI2022★ 3 cited

Formalizing Statistical Causality via Modal Logic

Yusuke Kawamoto, Tetsuya Sato, Kohei Suenaga

We propose a formal language for describing and explaining statistical causality. Concretely, we define Statistical Causality Language (StaCL) for expressing causal effects and spe…

cs.AI2022★ 4 cited

Sound and Relatively Complete Belief Hoare Logic for Statistical Hypothesis Testing Programs

Yusuke Kawamoto, Tetsuya Sato, Kohei Suenaga

We propose a new approach to formally describing the requirement for statistical inference and checking whether a program uses the statistical method appropriately. Specifically, w…