Papers tabular-regression
“tabular-regression” 태그가 달린 논문 19편 · 필터 해제
Learning Interpretable Differentiable Logic Networks for Tabular Regression
Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be computationally expensive in resource-constrained environments. We rece…
Computational Efficiencyregressiontabular-classificationtabular-regressionSynthetic-Powered Predictive Inference
Conformal prediction is a framework for predictive inference with a distribution-free, finite-sample guarantee. However, it tends to provide uninformative prediction sets when calibration data are scarce. This paper intr…
Conformal Predictionimage-classificationImage ClassificationPrediction+1Decoding-based Regression
Language models have recently been shown capable of performing regression tasks wherein numeric predictions are represented as decoded strings. In this work, we provide theoretical grounds for this capability and further…
Density Estimationregressiontabular-regressionAPAR: Modeling Irregular Target Functions in Tabular Regression via Arithmetic-Aware Pre-Training and Adaptive-Regularized Fine-Tuning
Tabular data are fundamental in common machine learning applications, ranging from finance to genomics and healthcare. This paper focuses on tabular regression tasks, a field where deep learning (DL) methods are not cons…
Data Augmentationtabular-regressionShap-Select: Lightweight Feature Selection Using SHAP Values and Regression
Feature selection is an essential process in machine learning, especially when dealing with high-dimensional datasets. It helps reduce the complexity of machine learning models, improve performance, mitigate overfitting,…
Computational Efficiencyfeature selectionregressiontabular-regressionConstructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study
When assessing the quality of prediction models in machine learning, confidence intervals (CIs) for the generalization error, which measures predictive performance, are a crucial tool. Luckily, there exist many methods f…
Benchmarkingtabular-regressionTransformers with Stochastic Competition for Tabular Data Modelling
Despite the prevalence and significance of tabular data across numerous industries and fields, it has been relatively underexplored in the realm of deep learning. Even today, neural networks are often overshadowed by tec…
Deep Learningtabular-classificationtabular-regressionMSBoost: Using Model Selection with Multiple Base Estimators for Gradient Boosting
Gradient boosting is a widely used machine learning algorithm for tabular regression, classification and ranking. Although, most of the open source implementations of gradient boosting such as XGBoost, LightGBM and other…
ClassificationModel Selectionregressiontabular-classification+1Rényi Neural Processes
Neural Processes (NPs) are deep probabilistic models that represent stochastic processes by conditioning their prior distributions on a set of context points. Despite their obvious advantages in uncertainty estimation fo…
Image Inpaintingregressiontabular-regressionVariational InferenceProbabilistic Calibration by Design for Neural Network Regression
Generating calibrated and sharp neural network predictive distributions for regression problems is essential for optimal decision-making in many real-world applications. To address the miscalibration issue of neural netw…
Decision Makingregressiontabular-regressionProbSAINT: Probabilistic Tabular Regression for Used Car Pricing
Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing…
regressiontabular-regressionUncertainty QuantificationWild-Tab: A Benchmark For Out-Of-Distribution Generalization In Tabular Regression
Out-of-Distribution (OOD) generalization, a cornerstone for building robust machine learning models capable of handling data diverging from the training set's distribution, is an ongoing challenge in deep learning. While…
Out-of-Distribution Generalizationregressiontabular-regressionImproving Neural Additive Models with Bayesian Principles
Neural additive models (NAMs) enhance the transparency of deep neural networks by handling input features in separate additive sub-networks. However, they lack inherent mechanisms that provide calibrated uncertainties an…
Additive modelsBayesian Inferencetabular-regressionVariational Boosted Soft Trees
Gradient boosting machines (GBMs) based on decision trees consistently demonstrate state-of-the-art results on regression and classification tasks with tabular data, often outperforming deep neural networks. However, the…
Decision MakingOut-of-Distribution Detectionregressiontabular-regression+1SRSD: Rethinking Datasets of Symbolic Regression for Scientific Discovery
Symbolic Regression (SR) is a task of recovering mathematical expressions from given data and has been attracting attention from the research community to discuss its potential for scientific discovery. However, the comm…
regressionscientific discoverySymbolic Regressiontabular-regressionRethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
This paper revisits datasets and evaluation criteria for Symbolic Regression (SR), specifically focused on its potential for scientific discovery. Focused on a set of formulas used in the existing datasets based on Feynm…
regressionscientific discoverySymbolic Regressiontabular-regressionInstance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees
Gradient-boosted regression trees (GBRTs) are hugely popular for solving tabular regression problems, but provide no estimate of uncertainty. We propose Instance-Based Uncertainty estimation for Gradient-boosted regressi…
regressiontabular-regressionA Framework and Benchmark for Deep Batch Active Learning for Regression
The acquisition of labels for supervised learning can be expensive. To improve the sample efficiency of neural network regression, we study active learning methods that adaptively select batches of unlabeled data for lab…
Active Learningregressiontabular-regressionRevisiting Deep Learning Models for Tabular Data
The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets. However, the proposed models are usually not properly compared t…
Deep Learningtabular-classificationtabular-regression