JoLT uses LLMs to make probabilistic predictions on tabular data.
problem Making probabilistic predictions on tabular data efficiently and without preprocessing.
method JoLT leverages LLMs' in-context learning to define joint distributions over tabular data.
result JoLT outperforms other methods on tabular classification and regression tasks.
This research examines how model explanations change under distribution shifts in tabular data.
problem Detecting distribution shifts in tabular data affecting model performance and explanations.
method Investigates the relationship between model performance and explanation characteristics under distribution shifts.
result Explanation shifts are a better indicator for detecting predictive performance changes than traditional distribution shift techniques.
GOTabPFN improves tabular model performance with compact tokenization for HDLSS data.
problem Making tabular models effective for high-dimensional, low-sample size data without retraining.
method Introducing Graph-guided Ordering with Local Refinement (GO-LR) and Neuro-Inspired Subunit Compression (NSC) to create compact meta-features.
result GOTabPFN improves stability and accuracy in tabular benchmarks with compact tokenization.
Proposes a proportional masking strategy for better tabular data imputation.
problem Heterogeneity of tabular data disrupts uniform random masking in MAEs.
method Computes missingness statistics, generates proportional masks, uses MLP token mixing.
result Proportional masking preserves missingness distribution, improves imputation performance.
GTDL methods fail to accurately model feature interactions in tabular data.
problem Accurate modeling of feature interactions in tabular data.
method Graph-based tabular deep learning methods using attention mechanisms and message-passing schemes.
result Current GTDL methods fail to recover meaningful feature interactions due to poor edge recovery.
SurvFM-RMST converts survival outcomes into pseudo-observation targets for tabular models.
problem Right-censored follow-up prevents direct use of survival labels in tabular patient data.
method SurvFM-RMST framework that converts survival outcomes into jackknife pseudo-observation targets for restricted mean survival time.
result SurvFM-RMST accurately recovered restricted event-free time in simulations and outperformed naive targets in static datasets.
AdapTable adapts tabular models to shifts without source data, improving HELOC performance.
problem Distribution shifts in tabular data threaten model performance.
method Shift-aware uncertainty calibrator and label distribution handler.
result Up to 16% improvement on HELOC dataset.
Tabular in-context learners perform well on biomolecular tasks, but performance depends on the representation used.
problem Predicting biomolecular properties from limited labeled data.
method Evaluating tabular in-context learners on protein fitness regression and small-molecule classification tasks.
result Tabular in-context learners are competitive for protein fitness regression but not for small-molecule classification.
ProtoNAM models tabular data with neural networks, making predictions transparent.
problem Tabular data analysis using neural networks lacks transparency and accuracy compared to tree-based methods.
method ProtoNAM introduces prototypes into neural networks to model tabular data while maintaining explainability.
result ProtoNAM outperforms existing NN-based GAMs and provides insights into learned feature patterns.
TabPFN-3 scales tabular prediction models to large datasets and improves performance and speed.
problem Scaling tabular prediction models to large datasets with high performance and efficiency.
method Pretrained on synthetic data, introduces test-time compute scaling, and uses row-chunking.
result Significantly outperforms other models on TabArena and diverse datasets.
Simple tabular event prediction model outperforms existing methods.
problem Predicting events from tabular data with historic events.
method Standard autoregressive LLM-style transformers with elementary positional embeddings and causal language modeling.
result Simple model outperforms existing approaches across various datasets and use-cases.
New deep learning framework for tabular data clusters with interpretable features.
problem Need for reliable and interpretable clustering models for tabular data.
method Self-supervised feature selection and gate matrix for cluster-level feature selection.
result Model provides interpretable cluster assignments with driving features.
Paper tackles test-time adaptation for tabular data.
problem Performance degradation due to distribution shifts in testing.
method Proposes FTAT for robustly adapting tabular models during testing.
result FTAT outperforms state-of-the-art methods on benchmark datasets.
Measures consistency of tabular LLM predictions under fine-tuning multiplicity.
problem Conflicting predictions from fine-tuned tabular LLMs.
method Local stability measure in embedding space.
result Probabilistic guarantees on prediction consistency under multiplicity.
Proposes a tabular transformer model to maintain feature effect intelligibility.
problem Losing marginal feature effects in deep tabular transformer networks.
method Adapts tabular transformer networks to identify marginal feature effects.
result The model accurately identifies marginal feature effects, matching black-box performance while maintaining intelligibility.
AgEBO-Tabular combines NAS and hyperparameter tuning for fast, high-performing tabular models.
problem Developing high-performing predictive models for large tabular data sets is challenging.
method Combines aging evolution NAS and asynchronous Bayesian optimization for hyperparameter tuning in data-parallel training.
result Automatically discovered neural network models outperform state-of-the-art AutoML ensembles in inference speed by two orders of magnitude.
TabSurv adapts tabular neural networks for survival analysis.
problem Survival analysis on tabular data using deep learning methods.
method Adapts modern tabular architectures to survival analysis using Weibull distribution or non-parametric prediction. Optimizes SurvHL histogram loss function.
result TabSurv consistently outperforms classical and deep learning baselines on 10 real-world survival datasets.
Improved tabular models learn better from real-world data.
problem Tabular models perform poorly on real-world datasets when trained only on synthetic data.
method Continued pre-training on a curated set of real-world datasets.
result Real-TabPFN achieves superior predictive accuracy on 29 datasets.
TabPFN model shows strong robustness to noisy data.
problem TabPFN tackles robustness to noisy and imperfect tabular data.
method Empirical robustness analysis of TabPFN's attention mechanisms under various perturbations.
result TabPFN maintains high predictive performance and coherent internal behavior under noisy and imperfect data.
LLMs outperform strong tabular baselines on industrial car retrofit prediction.
problem Industrial retrofit planning on structured operational data
method Embedding features, direct prompted classification, and ML+LLM stacking
result LLMs outperform strong tabular baselines on industrial car retrofit prediction
UnmaskingTrees improves tabular data imputation and generation using gradient-boosted decision trees.
problem Traditional methods outperform advanced deep learning techniques on tabular data imputation benchmarks.
method UnmaskingTrees employs gradient-boosted decision trees to incrementally unmask features for imputation and generation.
result UnmaskingTrees outperforms state-of-the-art methods on tabular imputation and generation benchmarks.
Treeffuser predicts tabular data distributions using gradient-boosted trees.
problem Probabilistic prediction with flexible, non-parametric models.
method Gradient-boosted trees for score estimation in conditional diffusion model.
result Treeffuser outperforms existing methods in probabilistic prediction tasks.
Tabular FMs struggle with reliable uncertainty quantification.
problem Uncertainty quantification in tabular foundation models.
method Compared TabPFN and Gaussian processes (GPs) across various regression tasks.
result GP outperforms TabPFN in data-scarce settings and when kernels are good priors.
Bayesian GBMs improve predictive uncertainty calibration for tabular data.
problem Lack of well-calibrated predictive uncertainties in gradient boosting machines.
method Variational inference with soft decision trees.
result Variational soft GBMs provide useful uncertainty estimates and maintain good predictive performance.
Locally sparse neural networks improve interpretability for biomedical tabular data.
problem Overfitting and lack of interpretability in neural networks for tabular biomedical data.
method Locally sparse neural network with a gating network to select relevant features.
result The method outperforms state-of-the-art models in synthetic and real-world biomedical datasets.
New benchmark predicts cardiometabolic risk from accelerometer data, with varying accuracy.
problem Lack of accurate tabular benchmarks for cardiometabolic risk from accelerometer data.
method Tabular learning methods (ridge regression, XGBoost, TabPFN v2) applied to NHANES data.
result TabPFN v2 achieves best performance, but triglycerides remain largely unpredictable.
Benchmarking AutoML for tables with text fields, achieving top performance.
problem Evaluating automated learning systems for tables with text fields.
method Publicly available benchmark with 18 datasets varying in size, types, and feature composition.
result Stack ensembling a multimodal Transformer with various tree models achieved top performance.
NON model improves tabular data classification accuracy.
problem Tabular data classification in real-world applications.
method Field-wise network, across field network, operation fusion network.
result NON significantly outperforms state-of-the-art models.
Imputation-free method learns tabular data with missing values using transformer.
problem Machine learning on tabular data with missing values often leads to unreliable outcomes due to synthetic imputation.
method Incremental attention learning (IFIAL) using transformer with attention masks.
result IFIAL outperforms state-of-the-art methods in 17 diverse tabular data sets.
Bayesian neural networks outperform calibrated neural networks for tabular data.
problem Uncertainty in neural network predictions for tabular data.
method Bayesian neural networks vs. post-hoc calibration methods.
result Bayesian neural networks yield competitive performance compared to calibrated neural networks.
xRFM improves tabular data inference with better accuracy and scalability.
problem Inference from tabular data remains challenging and underdeveloped compared to other AI areas.
method Combines feature learning kernel machines with a tree structure.
result xRFM outperforms other methods across 100 regression and 200 classification datasets.
This research improves uncertainty estimation for medical predictions, enhancing model trust and decision support.
problem Improving model uncertainty estimation for rare medical conditions.
method Developed and refined heuristics for selecting uncertainty estimation techniques, distinguishing them by clinical use-case. Also, compared ensembles vs. auto-encoders for detecting out-of-domain examples.
result Auto-encoders outperform ensembles in detecting out-of-domain examples, highlighting their importance for medical tabular data.
Generative Adversarial Network model for class-imbalanced tabular data.
problem Class imbalance in binary classification problems.
method Generative Adversarial Network (GAN) with synthetic minority class samples.
result Improves average precision compared to re-weighting and oversampling techniques.
Efficient synthetic data generation improves model performance on tabular data.
problem Improving model robustness and performance with scarce or low-quality data.
method Hardness characterization to identify high-value training points, generating synthetic data only from these points.
result Synthetic data generated from hardest points outperforms non-targeted methods on tabular datasets.
AI techniques explain synthetic tabular data weaknesses.
problem Challenges in evaluating synthetic tabular data quality.
method Apply explainable AI to a binary detection classifier.
result Reveals inconsistencies, unrealistic dependencies, or missing patterns in synthetic data.
TANGOS improves neural network performance on tabular data by encouraging neuron specialization.
problem Improving neural network performance on tabular data.
method Gradient orthogonality and specialization of latent units.
result TANGOS leads to improved out-of-sample generalization performance.
Interpretable representations improve explainable AI by translating complex data into understandable concepts.
problem Many explainers use interpretable representations but overlook their full potential and assumptions.
method An in-depth analysis of interpretable representations for tabular, image, and text data, identifying strengths, weaknesses, and desiderata.
result Linear model quantifies interpretable concepts' influence on black-box predictions, revealing their explanatory properties and manipulability.
PRRO generates synthetic tabular data that improves SL performance and class distribution.
problem Low SL utility of synthetic data due to class imbalance and overlooked data relationships.
method Data pruning and column reordering to optimize SL utility.
result Synthetic data generated with PRRO enhances predictive performance and class distribution.
ReTabSyn synthesizes realistic tabular data efficiently by focusing on conditional distribution.
problem Synthesizing realistic tabular data in low-data, imbalanced settings.
method ReTabSyn uses reinforcement learning to prioritize feature correlation preservation during training.
result ReTabSyn consistently outperforms state-of-the-art baselines across various benchmarks.
T-JEPA learns tabular data representations without augmentations, outperforming traditional methods.
problem Challenges in self-supervised learning for tabular data due to lack of data augmentations.
method T-JEPA uses a Joint Embedding Predictive Architecture (JEPA) to predict latent representations of different subsets of features within the same sample.
result Significant improvement in classification and regression tasks, outperforming traditional methods.
Deep neural networks for ordinal outcomes combining image and tabular data.
problem Lack of interpretable models for ordinal outcomes in mixed data types.
method Ordinal Neural Network Transformation Models (ONTRAMs) integrating DL and classical ordinal regression.
result ONTRAMs achieve performance equivalent to standard multi-class DL models but are faster and more interpretable.
New insights on Shapley value precision for tabular data predictions.
problem Precision of Shapley value explanations for individual observations.
method Conditional Shapley value estimation methods for tabular data.
result Shapley value explanations are less precise for outer observations.
A new large-scale tabular benchmark for Learning from Label Proportions.
problem Lack of a large-scale open benchmark for tabular Learning from Label Proportions.
method Proposed LLP-Bench, a suite of 70 datasets (62 feature bag and 8 random bag) from real-world tabular data.
result Demonstrated the effectiveness of 9 SOTA and popular tabular LLP techniques on 62 feature bag datasets.
Unified approach for multimodal data prediction using synthetic data generation.
problem Challenges in integrating heterogeneous data types for accurate predictive performance.
method Generative Distribution Prediction (GDP) framework that uses multimodal synthetic data generation.
result Empirical validation across four tasks demonstrates versatility and effectiveness of GDP.
Efficiently estimates uncertainty for LLM-based entity linking in tabular data.
problem Accurate and reliable uncertainty estimates for LLM-based entity linking in tabular data.
method Self-supervised approach using token-level features for single-shot inference.
result Effective uncertainty estimates detected at a fraction of computational cost.
New deep learning model interprets tabular data with variable selection and explainability.
problem Deep learning models lack interpretability and variable selection.
method Proposes a new network architecture that combines deep learning with generalized linear models.
result The model provides superior predictive power and interpretable results.
Transformer architecture improved with credibility mechanism for better model performance.
problem Improving predictive models in tabular data.
method Introducing a credibility mechanism to the Transformer architecture.
result Credibility Transformer leads to superior predictive models compared to state-of-the-art models.
TabPFN model outperforms previous methods on tabular data.
problem Improving performance on tabular data regression and classification.
method Transformer-based deep learning model for approximate Bayesian inference.
result TabPFN outperforms previous methods on datasets with up to 10,000 samples.