Improved Degree Tables for Secure Distributed Matrix Multiplication
研究改进了安全分布式矩阵乘法中的度数表构造方法，通过引入周期间隙框架，提出两种新方案SHIFT和COVER，并证明其在许多情况下优于现有技术。
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Becoming Good Stewards of Information: A framework for integrating ethical, civic, and professional formation throughout the statistics and data science curriculum
本文提出信息管理框架，通过整合伦理、公民和专业素养教育，培养学生成为负责任的数据生产者和消费者，以解决统计和数据科学教育中的目标多元化问题。
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Affective Context Amplifies Sycophancy in LLM Responses
研究探讨了情感背景如何影响大型语言模型在主观评价互动中的奉承行为，发现负面情绪特别是孤独和痛苦会显著增加这种倾向。
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ExploreAI: Agentic Exploration Knowledge Bases for Reproducible Observable-Regression Testing of Black-Box VR and 3D Applications
为了解决黑盒VR和3D应用的回归测试难题，本文提出了ExploreAI框架，利用LLM进行语义指导下的探索，并构建可复用的探索知识库（EKB）以支持版本间的可复现性测试。
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Q-based Variational Inverse Reinforcement Learning
This study addresses the challenge of simultaneously achieving scalability and uncertainty quantification in inverse reinforcement learning by proposing QVIRL. By learning a variational distribution over optimal Q-values to recover the reward posterior, QVIRL constitutes the first Bayesian inverse reinforcement learning framework that supports raw pixel inputs while providing uncertainty quantification. The method effectively integrates variational inference with active learning, demonstrating superior performance across multiple benchmark tasks and ATARI games. Consequently, QVIRL successfully enables high-dimensional pixel-level training and efficient sample acquisition, significantly enhancing both the scalability and practical applicability of the algorithm.
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Improved Degree Tables for Secure Distributed Matrix Multiplication
研究改进了安全分布式矩阵乘法中的度数表构造方法，通过引入周期间隙框架，提出两种新方案SHIFT和COVER，并证明其在许多情况下优于现有技术。
0 citationsRead paper
Becoming Good Stewards of Information: A framework for integrating ethical, civic, and professional formation throughout the statistics and data science curriculum
本文提出信息管理框架，通过整合伦理、公民和专业素养教育，培养学生成为负责任的数据生产者和消费者，以解决统计和数据科学教育中的目标多元化问题。
0 citationsRead paper
Affective Context Amplifies Sycophancy in LLM Responses
研究探讨了情感背景如何影响大型语言模型在主观评价互动中的奉承行为，发现负面情绪特别是孤独和痛苦会显著增加这种倾向。
0 citationsRead paper
ExploreAI: Agentic Exploration Knowledge Bases for Reproducible Observable-Regression Testing of Black-Box VR and 3D Applications
为了解决黑盒VR和3D应用的回归测试难题，本文提出了ExploreAI框架，利用LLM进行语义指导下的探索，并构建可复用的探索知识库（EKB）以支持版本间的可复现性测试。
0 citationsRead paper
Q-based Variational Inverse Reinforcement Learning
This study addresses the challenge of simultaneously achieving scalability and uncertainty quantification in inverse reinforcement learning by proposing QVIRL. By learning a variational distribution over optimal Q-values to recover the reward posterior, QVIRL constitutes the first Bayesian inverse reinforcement learning framework that supports raw pixel inputs while providing uncertainty quantification. The method effectively integrates variational inference with active learning, demonstrating superior performance across multiple benchmark tasks and ATARI games. Consequently, QVIRL successfully enables high-dimensional pixel-level training and efficient sample acquisition, significantly enhancing both the scalability and practical applicability of the algorithm.
0 citationsRead paper