Real estate valuation is a structured regression problem in which prices are governed by heterogeneous feature types, sparse regional effects, nonlinear interactions, and the practical logic of comparable properties. Standard multilayer perceptrons treat each row as an isolated vector and must learn locality, scale sensitivity, and categorical matching from supervision alone. Gradient-boosted decision trees provide strong tabular baselines, but their feature-centric splitting mechanism does not explicitly model the retrieval of similar historical observations.
This paper presents RowNet, a retrieval-based neural architecture for real estate price-per-square-meter prediction. RowNet represents a query property through pairwise similarity features against a memory bank of labeled properties. A first retrieval layer estimates a coarse target from feature-only similarities. A second layer augments the memory comparison with target-consistency features and uses multiple learned attention heads to retrieve complementary comparable sets. A final mixture-of-experts module combines learned gating, residual correction, entropy regularization, and head-diversity regularization to produce the prediction.
Stanford University（斯坦福大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；University of California, Berkeley（加州大学伯克利分校）；École Polytechnique（巴黎高等理工学院）
Muddling Label Regularization: Deep Learning for Tabular Datasets
Ecole Polytechnique（巴黎综合理工学院）；Université Paris Dauphine-PSL（巴黎第九大学-巴黎文理研究大学）
深度学习（DL）被认为是计算机视觉、语音识别和自然语言处理的最新技术。直到最近，人们还普遍认为DL与表格数据的学习任务无关，特别是在集成方法被公认为黄金标准的小样本区域。我们提出了一种新的端到端可微的方法来训练一个标准的FFNN。我们的方法，\textbf{Muddling labels for Regularization}（\texttt{MLR}），通过生成无信息标签和在训练过程中在最后一个隐藏层上应用可微的闭合形式正则化方案来惩罚记忆。\texttt{MLR}在来自UCI数据库和Kaggle的几个数据集上的回归和分类任务中优于经典NN和黄金标准（GBDT，RF），涵盖了大范围的样本量和特征样本比。研究人员和从业者可以将\texttt{MLR}作为现成的\DL{}解决方案单独使用，也可以将其集成到最先进的机器学习管道中。
From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning
BASF（巴斯夫）；Clausthal University of Technology（克劳斯塔尔工业大学）；Amazon Music（亚马逊音乐）；Bielefeld School of Business, Hochschule Bielefeld (HSBI)（比勒费尔德商学院，比勒费尔德应用科学大学）
SelectArena: A Modern Benchmark for Feature Selection on Real-world Tabular Data
Université du Littoral-Côte-d’Opale（滨海-加来海峡大学）；University of Freiburg（弗莱堡大学）；Prior Labs（Prior实验室）；HTW Berlin（柏林应用科学大学）；ELLIS Institute Tübingen（ELLIS蒂宾根研究所）
Can Tabular Foundation Models Amortize Statistical Inference?
London School of Economics and Political Science（伦敦政治经济学院）；University of Science and Technology of China（中国科学技术大学）；Shanghai University of Finance and Economics（上海财经大学）；University of Milano-Bicocca（米兰比可卡大学）
A Closer Look at Deep Learning Methods on Tabular Datasets
School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）；National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家实验室，南京大学）
DANets: Deep Abstract Networks for Tabular Data Classification and Regression
Zhejiang University（浙江大学）；Huazhong University of Science and Technology（华中科技大学）；University of Notre Dame（圣母大学）；Zhejiang University School of Medicine（浙江大学医学院）
Highly Imbalanced Regression with Tabular Data in SEP and Other Applications
Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Aerospace, Physics and Space Sciences（航空航天、物理与空间科学系）；Florida Institute of Technology（佛罗里达理工学院）
TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression
Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim（信息系统与机器学习实验室（ISMLL）、希尔德斯海姆大学）；VWFS Data Analytics Research Center（VWFS数据分析研究中心）
FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院机械工程系）；Center for Computational Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院计算工程中心）
Benchmarking Tabular Foundation Models for Conditional Density Estimation in Regression
Department of Statistics, Federal University of São Carlos (UFSCar)（圣卡洛斯联邦大学统计系）；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔国立理工学院、让·库尔坦数学实验室）
SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data
Nanjing University（南京大学）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）
LAIR-Net: Leaky Alignment-Impulse Residual Networks for Tabular Regression
Indian Institute of Technology Guwahati（印度技术学院加瓦哈蒂分校）；SAFIR, Sorbonne University Abu Dhabi（阿布扎比索邦大学 SAFIR）；Indian Institute of Technology Bombay（印度技术学院孟买分校）
From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy
Osnabrück University（奥斯纳布吕克大学）；Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB)（莱布尼茨农业工程与生物经济研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
TabReliBench: Evaluating Tabular Foundation Models Beyond Predictive Accuracy
Renmin University of China（中国人民大学）；Cerebras Systems, Inc.（赛博拉斯系统公司）；National University of Singapore（新加坡国立大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models
School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳校区智能科学与工程学院）；Shenzhen Key Lab for Advanced Motion Control and Modern Automation Equipments, Shenzhen（深圳先进运动控制及现代自动化装备重点实验室）；Department of Electrical Engineering, City University of Hong Kong, Kowloon（香港城市大学电子工程系）
On the Uncertainty Quantification Ability of Tabular Foundation Models
Department of Mechanical and Aerospace Engineering, University of California, Irvine（加州大学欧文分校机械与航空航天工程系）；Department of Civil and Environmental Engineering, University of California, Irvine（加州大学欧文分校土木与环境工程系）
