Let Me At Least Learn What You Really Like: Dealing With Noisy Humans When Learning Preferences

消费者究竟如何选择：利用专家混合模型揭示隐藏偏好

How Do Consumers Really Choose: Exposing Hidden Preferences with the Mixture of Experts Model

本文引入专家混合（MoE）框架建模消费者异质性偏好，利用概率门控和专家网络动态细分消费者，并在大规模零售数据上验证其预测准确性优于传统计量模型，为需求预测和精准营销提供新方法。

LLM评估者真的是自恋者吗？对自我偏好评估的健全性检查

Are LLM Evaluators Really Narcissists? Sanity Checking Self-Preference Evaluations

通过比较评估者自我评价与评价其他模型时的投票分布，发现仅51%的先前结果具有统计显著性，表明自我偏好主要由评估者质量而非自恋驱动。

什么数据才是真正必要的？推荐系统推理数据最小化的可行性研究

What Data is Really Necessary? A Feasibility Study of Inference Data Minimization for Recommender Systems

本文研究推荐系统隐式反馈推理数据最小化问题，提出新问题形式化并分析多种技术，证明大幅减数据可行但受技术设置与用户特征制约。

社会选择应引导AI对齐以处理多样化的人类反馈

Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback

本文论证社会选择理论能有效解决AI对齐中多样化人类反馈的聚合问题，并基于相关研讨会探讨了推进该研究议程的途径。

Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria

The learning of predictive models for data-driven decision support has been a prevalent topic in many fields. However, construction of models that would capture interactions among input variables is a challenging task. In this paper, we present a new preference learning approach for multiple criteria sorting with potentially interacting criteria. It employs an additive piecewise-linear value function as the basic preference model, which is augmented with components for handling the interactions. To construct such a model from a given set of assignment examples concerning reference alternatives…

选择前先量化：用于鲁棒强化学习的主动动力学偏好

Quantification before Selection: Active Dynamics Preference for Robust Reinforcement Learning

针对 Domain Randomization 训练的策略过于保守的问题，提出 Active Dynamics Preference (ADP) 方法，通过量化并主动选择高信息量、低密度的系统参数来稳定训练，显著提升了策略在四个机器人运动任务中对系统不一致性的鲁棒性。