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20212026
most citedAHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses

1 citations · 1 across the 17 of their papers we have counts for

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cs.LG2025

Robust Anomaly Detection Under Normality Distribution Shift in Dynamic Graphs

Xiaoyang Xu, Xiaofeng Lin, Koh Takeuchi +2

Anomaly detection in dynamic graphs is a critical task with broad real-world applications, including social networks, e-commerce, and cybersecurity. Most existing methods assume th…

cs.LG2025

Unpacking the Implicit Norm Dynamics of Sharpness-Aware Minimization in Tensorized Models

Tianxiao Cao, Kyohei Atarashi, Hisashi Kashima

Sharpness-Aware Minimization (SAM) has been proven to be an effective optimization technique for improving generalization in overparameterized models. While prior works have explor…

cs.CR2025

Counterfactual Evaluation for Blind Attack Detection in LLM-based Evaluation Systems

Lijia Liu, Takumi Kondo, Kyohei Atarashi +4

This paper investigates defenses for LLM-based evaluation systems against prompt injection. We formalize a class of threats called blind attacks, where a candidate answer is crafte…

stat.ML2025

Probability Bounding: Post-Hoc Calibration via Box-Constrained Softmax

Kyohei Atarashi, Satoshi Oyama, Hiromi Arai +1

Many studies have observed that modern neural networks achieve high accuracy while producing poorly calibrated probabilities, making calibration a critical practical issue. In this…

cs.LG2025

Dynamic Feature Selection from Variable Feature Sets Using Features of Features

Katsumi Takahashi, Koh Takeuchi, Hisashi Kashima

Machine learning models usually assume that a set of feature values used to obtain an output is fixed in advance. However, in many real-world problems, a cost is associated with me…

cs.CV2025

Exploring Causes and Mitigation of Hallucinations in Large Vision Language Models

Yaqi Sun, Kyohei Atarashi, Koh Takeuchi +1

Large Vision-Language Models (LVLMs) integrate image encoders with Large Language Models (LLMs) to process multi-modal inputs and perform complex visual tasks. However, they often…