Bipartite b-matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas such as healthcare, advertising, education, and general resource allocation. Traditionally, the primary goal of such models is to maximize a linear function of the constituent matches (e.g., linear social welfare maximization) subject to some constraints. Recent work has studied a new goal of balancing whole-match diversity and economic efficiency, where the objective is instead a monotone submodular function over the matching. Basic versions of this problem are solvable in polynomial time. In this work, we prove that the problem of simultaneously maximizing diversity along several features (e.g., country of citizenship, gender, skills) is NP-hard. To address this problem, we develop the first combinatorial algorithm that constructs provably-optimal diverse b-matchings in pseudo-polynomial time. We also provide a Mixed-Integer Quadratic formulation for the same problem and show that our method guarantees optimal solutions and takes less computation time for a reviewer assignment application.
Matched sample selection with GANs for mitigating attribute confounding
University of California at Berkeley（加州大学伯克利分校）；Massachusetts Institute of Technology（麻省理工学院）；California Institute of Technology（加州理工学院）；Amazon Web Services（亚马逊网络服务）
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
University of Hong Kong（香港大学）；MIT（麻省理工学院）；Amazon Web Services（亚马逊云科技）；ASAPP Inc.（ASAPP公司）
文本属性转移的目的是自动重写句子，使它们具有某些语言属性，同时保持其语义内容。由于缺乏监督并行数据，这项任务仍然具有挑战性。现有的方法试图明确地解开内容和属性信息，但这是困难的，往往会导致不良的内容保存和不合语法。相比之下，我们提出了一种更简单的方法，迭代匹配和翻译（Iterative Matching and Translation，IMAT），它：（1）通过对齐源语料库和目标语料库中语义相似的句子的子集来构建伪平行语料库;（2）应用标准的序列到序列模型来学习属性转移;（3）通过改进对齐中的缺陷来迭代地改进学习的转移函数。在情感修改和形式转换任务中，我们的方法大大优于复杂的最先进的系统。作为一个辅助的贡献，我们产生了一个公开的测试集与人类生成的传输参考。
Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model
The Laboratory for AI-Powered Financial Technologies（人工智能赋能金融科技实验室）；School of Data Science, City University of Hong Kong（数据科学学院）；College of Intelligence and Computing, Tianjin University（智能与计算学院）
Beyond Mode Collapse: Distribution Matching for Diverse Reasoning
Tongji University（同济大学）；Independent（独立）；Shanghai AI Laboratory（上海人工智能实验室）；Zhejiang University（浙江大学）；Fudan University（复旦大学）；The Chinese University of Hong Kong（香港中文大学）
TMPO: Trajectory Matching Policy Optimization for Diverse and Efficient Diffusion Alignment
Huazhong University of Science and Technology（华中科技大学）；Kuaishou Technology（快手科技）；Nanyang Technological University（南洋理工大学）；Wuhan University（武汉大学）；Tsinghua University（清华大学）
Evaluating the Interpretability of Sparse Autoencoders with Concept Annotations
The Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所）；Technische Universität Berlin（柏林工业大学）；INRAE（法国国家农业、食品与环境研究院）；Inria, EVERGREEN（法国国家信息与自动化研究所，EVERGREEN）；UMR TETIS, Univ Montpellier（UMR TETIS，蒙彼利埃大学）
Letting Trajectories Spread: Quality-Preserving Control for Diverse Flow Matching
The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Nanyang Technological University（南洋理工大学）；CFAR, Agency for Science, Technology and Research, Singapore（新加坡科技研究局CFAR）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；National University of Singapore（新加坡国立大学）
