Papers tabular-classification
“tabular-classification” 태그가 달린 논문 48편 · 필터 해제
Tokenize features, enhancing tables: the FT-TABPFN model for tabular classification
Traditional methods for tabular classification usually rely on supervised learning from scratch, which requires extensive training data to determine model parameters. However, a novel approach called Prior-Data Fitted Ne…
Classificationtabular-classificationSqueezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications
Many industry verticals are confronted with small-sized tabular data. In this low-data regime, it is currently unclear whether the best performance can be expected from simple baselines, or more complex machine learning …
AutoMLMeta-Learningregressiontabular-classificationTuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
While tabular classification has traditionally relied on from-scratch training, a recent breakthrough called prior-data fitted networks (PFNs) challenges this approach. Similar to large language models, PFNs make use of …
FairnessIn-Context Learningparameter-efficient fine-tuningtabular-classificationMotherNet: Fast Training and Inference via Hyper-Network Transformers
Foundation models are transforming machine learning across many modalities, with in-context learning replacing classical model training. Recent work on tabular data hints at a similar opportunity to build foundation mode…
ClassificationIn-Context LearningMeta-LearningPrompt Engineering+1Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks
Tabular classification has traditionally relied on supervised algorithms, which estimate the parameters of a prediction model using its training data. Recently, Prior-Data Fitted Networks (PFNs) such as TabPFN have succe…
feature selectiontabular-classificationThe Disagreement Problem in Faithfulness Metrics
The field of explainable artificial intelligence (XAI) aims to explain how black-box machine learning models work. Much of the work centers around the holy grail of providing post-hoc feature attributions to any model ar…
BenchmarkingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)tabular-classificationConfronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful social biases that reflect the stereotypes a…
FairnessIn-Context Learningtabular-classificationRobust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance
Dealing with label noise in tabular classification tasks poses a persistent challenge in machine learning. While robust boosting methods have shown promise in binary classification, their effectiveness in complex, multi-…
Binary ClassificationClassificationComputational EfficiencyMissing Values+2Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule
As a modern ensemble technique, Deep Forest (DF) employs a cascading structure to construct deep models, providing stronger representational power compared to traditional decision forests. However, its greedy multi-layer…
AutoMLData Augmentationtabular-classificationPareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck
In modern deep learning, algorithmic choices (such as width, depth, and learning rate) are known to modulate nuanced resource tradeoffs. This work investigates how these complexities necessarily arise for feature learnin…
tabular-classificationA Suite of Fairness Datasets for Tabular Classification
There have been many papers with algorithms for improving fairness of machine-learning classifiers for tabular data. Unfortunately, most use only very few datasets for their experimental evaluation. We introduce a suite …
ClassificationFairnesstabular-classificationStochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
We present Re-weighted Gradient Descent (RGD), a novel optimization technique that improves the performance of deep neural networks through dynamic sample re-weighting. Leveraging insights from distributionally robust op…
Domain AdaptationDomain GeneralizationMeta-LearningRepresentation Learning+1TabLLM: Few-shot Classification of Tabular Data with Large Language Models
We study the application of large language models to zero-shot and few-shot classification of tabular data. We prompt the large language model with a serialization of the tabular data to a natural-language string, togeth…
ClassificationDeep LearningLanguage ModelingLanguage Modelling+4Is margin all you need? An extensive empirical study of active learning on tabular data
Given a labeled training set and a collection of unlabeled data, the goal of active learning (AL) is to identify the best unlabeled points to label. In this comprehensive study, we analyze the performance of a variety of…
Active LearningAllBenchmarkingtabular-classificationPTab: Using the Pre-trained Language Model for Modeling Tabular Data
Tabular data is the foundation of the information age and has been extensively studied. Recent studies show that neural-based models are effective in learning contextual representation for tabular data. The learning of a…
Language ModelingLanguage ModellingRepresentation Learningtabular-classificationGANDALF: Gated Adaptive Network for Deep Automated Learning of Features
We propose a novel high-performance, interpretable, and parameter \& computationally efficient deep learning architecture for tabular data, Gated Adaptive Network for Deep Automated Learning of Features (GANDALF). GANDAL…
feature selectionregressionRepresentation Learningtabular-classificationTabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification meth…
AutoMLBayesian InferenceGeneral ClassificationGPU+4The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling
There is an increasing interest in the application of deep learning architectures to tabular data. One of the state-of-the-art solutions is TabTransformer which incorporates an attention mechanism to better track relatio…
Binary ClassificationDeep Learningtabular-classificationRepresenter Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models
Explaining the influence of training data on deep neural network predictions is a critical tool for debugging models through data curation. A recent tractable and appealing approach for this task was provided via the co…
image-classificationImage Classificationtabular-classificationtext-classification+1Fieldwise Factorized Networks for Tabular Data Classification
Tabular data is one of the most common data-types in machine learning, however, deep neural networks have not yet convincingly outperformed classical baselines on such datasets. In this paper, we first investigate the th…
Classificationtabular-classification