6 citations · 6 across the 7 of their papers we have counts for
7 papers
DAPLSR: Data Augmentation Partial Least Squares Regression Model via Manifold Optimization
Haoran Chen, Jiapeng Liu, Jiafan Wang +1
Traditional Partial Least Squares Regression (PLSR) models frequently underperform when handling data characterized by uneven categories. To address the issue, this paper proposes…
Integrating Response Time and Attention Duration in Bayesian Preference Learning for Multiple Criteria Decision Aiding
Jiaxuan Jiang, Jiapeng Liu, Miłosz Kadziński +2
We introduce a multiple criteria Bayesian preference learning framework incorporating behavioral cues for decision aiding. The framework integrates pairwise comparisons, response t…
Preference Construction: A Bayesian Interactive Preference Elicitation Framework Based on Monte Carlo Tree Search
Yan Wang, Jiapeng Liu, Milosz Kadziński +1
We present a novel preference learning framework to capture participant preferences efficiently within limited interaction rounds. It involves three main contributions. First, we d…
Efficient Generalized Low-Rank Tensor Contextual Bandits
Qianxin Yi, Yiyang Yang, Shaojie Tang +2
In this paper, we aim to build a novel bandits algorithm that is capable of fully harnessing the power of multi-dimensional data and the inherent non-linearity of reward functions…
Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation
Mengzhuo Guo, Zhongzhi Xu, Qingpeng Zhang +2
Ordinal regression predicts the objects' labels that exhibit a natural ordering, which is important to many managerial problems such as credit scoring and clinical diagnosis. In th…
A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples
Jiapeng Liu, Milosz Kadzinski, Xiuwu Liao +2
We present a preference learning framework for multiple criteria sorting. We consider sorting procedures applying an additive value model with diverse types of marginal value funct…
Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria
Jiapeng Liu, Milosz Kadzinski, Xiuwu Liao +1
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…