使用Wallenius分布对偏好数据建模

基于贝叶斯非参数分层模型的披露风险评估

Disclosure risk assessment with Bayesian non-parametric hierarchical modelling

本文提出基于分层狄利克雷过程的贝叶斯非参数模型评估数据集披露风险，无需调参即可准确估计样本与总体唯一值，并引入数据增强方案处理结构零值，在纽约人口普查数据上验证有效。

通过序贯逻辑斯谛回归对印度避孕行为的贝叶斯半参数模型

Bayesian semiparametric modelling of contraceptive behavior in India via sequential logistic regressions

在印度，计划生育的特点是战略方案差异很大，包括针对具体方法的避孕目标、强制绝育和最近的无目标方法。随着时间的推移，计划生育政策的这些重大变化激发了人们对评估不同方案的有效性的极大兴趣，同时了解哪些人口亚群没有得到适当解决。目前的研究考虑了上述政策的具体方面，例如，包括与使用避孕药具的妇女选择绝育以外的其他避孕方法有关的因素。虽然这些分析产生了相关的见解，但它们未能提供对不同计划生育政策以及避孕选择背后的决定因素的全球概览。出于这种考虑，我们提出了一个贝叶斯半参数模型依赖于通过一组条件伯努利选择的多项式概率质量函数的重新参数化。连续的二元结构被定义为与印度目前的计划生育政策相一致，并与避孕选择的合理过程相一致。这种灵活的表述和仔细的重新参数化相结合，使得在一个单一的模型内对印度的不同政策和避孕偏好有一个更广泛和可解释的概览。

The Bradly-Terry Regression Trunk approach for modelling preference data with small trees

This paper introduces the Bradley-Terry Regression Trunk model, a novel probabilistic approach for the analysis of preference data expressed through paired comparison rankings. In some cases, it may be reasonable to assume that the preferences expressed by individuals depend on their characteristics. Within the framework of tree-based partitioning, we specify a tree-based model estimating the joint effects of subject-specific covariates over and above their main effects. We combine a tree-based model and the log-linear Bradley-Terry model using the outcome of the comparisons as response variab…

Fast Game Content Adaptation Through Bayesian-based Player Modelling

In games, as well as many user-facing systems, adapting content to users' preferences and experience is an important challenge. This paper explores a novel method to realize this goal in the context of dynamic difficulty adjustment (DDA). Here the aim is to constantly adapt the content of a game to the skill level of the player, keeping them engaged by avoiding states that are either too difficult or too easy. Current systems for DDA rely on expensive data mining, or on hand-crafted rules designed for particular domains, and usually adapts to keep players in the flow, leaving no room for the d…

Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems

Recommender systems are often designed based on a collaborative filtering approach, where user preferences are predicted by modelling interactions between users and items. Many common approaches to solve the collaborative filtering task are based on learning representations of users and items, including simple matrix factorization, Gaussian process latent variable models, and neural-network based embeddings. While matrix factorization approaches fail to model nonlinear relations, neural networks can potentially capture such complex relations with unprecedented predictive power and are highly s…

Explaining Preferences with Shapley Values

While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propose \textsc{Pref-SHAP}, a Shapley value-based model explanation framework for pairwise comparison data. We derive the appropriate value functions for preference models and further extend the framework to model and explain \emph{context specific} information, such as the surface type in a tennis game. To demonstrate the utility of \textsc{Pref-SHAP}, we apply our method to a variety of synthetic and real-world datasets …

函数效应模型：利用机器学习在面板数据中考虑偏好异质性

Functional effects models: Accounting for preference heterogeneity in panel data with machine learning

本文提出函数效应模型，利用梯度提升决策树和深度神经网络从社会人口特征中学习个体特定偏好参数，以解决面板选择数据中的个体异质性问题，并在合成实验和真实面板数据上验证其优于传统固定/随机效应模型及现有机器学习选择模型。

兰格vin扩散在动物运动建模中的无偏似然估计

Unbiased likelihood estimation of the Langevin diffusion for animal movement modelling

本文提出利用布朗桥进行重要性采样，改进兰格vin扩散模型的似然估计，以解决 telemetry 数据中自相关和时间不规则性的问题，提升生态栖息地选择研究的准确性。

从异构排名数据中的联合学习

Joint Learning from Heterogeneous Rank Data

本文提出了Sparse Fused Plackett-Luce模型，通过跨群体共享系数信息并施加稀疏性，实现了对先验已知异构排名数据的联合学习，提升了模型的估计、预测和可解释性。