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.
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.
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.
TraDE uses self-attention for better density estimation of tabular and image data.
problem Improving density estimation for tabular and image data.
method Self-attention-based architecture trained with a penalized maximum likelihood objective.
result TraDE produces significantly better density estimates than existing methods.
Generative model for tabular data density regression.
problem Estimating conditional distribution of outcomes given covariates.
method Tree-based flow model for efficient sampling and likelihood evaluation.
result Our method achieves comparable or superior performance with reduced training and sampling costs.
DiffGBM improves tree-based models for tabular regression by making their design defaults explicit and tunable.
problem Improving tree-based models for tabular regression without neural density estimators.
method Explicitly exposes and makes tunable the design defaults of tree-based models, using a Gaussian-path flow-matching trainer and a score-flex space.
result Selected configurations of DiffGBM outperform the published baseline on all eleven tabular benchmarks, with the best aggregate CRPS skill.
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.
A new GNM model outperforms MLP for tabular data.
problem Learning with tabular data.
method Proposes Graph Neural Machine (GNM) replacing MLP's graph representation with a nearly complete graph and using synchronous message passing.
result GNM outperforms MLP in classification and regression tasks.
Paper introduces a new method to improve learning on imbalanced regression problems.
problem Imbalanced distribution learning in predictive modeling reduces standard algorithms' performance.
method The paper proposes a novel method using disentangled VAEs and Smoothed Bootstrap in the latent space.
result The method improves learning on tabular data within the Imbalanced Regression framework.
AutoGluon-Tabular automates tabular data ML with single line Python.
problem Training accurate ML models on unprocessed tabular data.
method Ensembling multiple models in multiple layers.
result Outperforms competitors in accuracy and robustness.
Bayesian Additive Distribution Regression (DistBART) predicts distributions from grouped data.
problem Predicting distributions from grouped data with varying characteristics.
method Bayesian nonparametric approach using BART for modeling the regression function.
result Empirical and theoretical evidence supports DistBART's effectiveness in learning from low-dimensional marginals.
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.
Unified diffusion model tackles missing data in tabular datasets.
problem Training diffusion models on tabular data with missing values.
method Proposes a masked regression loss for Denoising Score Matching to learn from incomplete data.
result Demonstrates superior performance on tabular datasets with missing values compared to state-of-the-art methods.
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.
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.
BFTS uses Bayesian Additive Regression Trees for improved personalized mobile health interventions.
problem Adapting to complex, non-linear user behaviors in personalized mobile health interventions.
method Bayesian Forest Thompson Sampling (BFTS) integrates Bayesian Additive Regression Trees (BART) into the exploration loop of contextual bandits.
result BFTS achieves state-of-the-art regret on tabular benchmarks and improves engagement rates by over 30% in a behavioral intervention study.
Proposes a method for explaining tabular data using copulas.
problem Lack of ground truth for explainability in complex datasets.
method Uses copulas to specify statistical properties and build intuition.
result Demonstrates improved explainability on logistic regression and correlation use cases.
Tabular foundation models outperform other methods in conditional density estimation across various datasets.
problem Estimating the full conditional distribution of a response given tabular covariates, especially in settings with heteroscedasticity, multimodality, or asymmetric uncertainty.
method Benchmarked three tabular foundation model variants (TabPFN and TabICL) against six CDE baselines on 39 real-world datasets.
result Tabular foundation models achieve the best CDE loss, log-likelihood, and CRPS across all sample sizes, outperforming other methods.
DNN2LR bridges DNN power and LR interpretability.
problem Combining DNN power and LR interpretability for real-world tabular data.
method Automatic feature crossing method based on DNN interpretation inconsistencies.
result DNN2LR outperforms complex DNN models and feature crossing methods.
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.
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.
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.
A minimalist approach generates synthetic tabular data with sparse PCA and XGBoost.
problem Generating robust synthetic tabular data for model testing.
method Minimalistic unsupervised SparsePCA encoder with XGBoost decoder.
result The method provides an alternative to raw and quantile perturbation for model robustness testing.
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.
TabDPT combines ICL and self-supervised learning to train tabular models on real data.
problem Slow generalization of tabular foundation models to unseen datasets.
method In-Context Learning (ICL) combined with self-supervised learning, using real data for pre-training.
result TabDPT achieves strong performance on regression and classification benchmarks.
TabPFN-2.5 boosts tabular AI performance, especially for large datasets.
problem Improving tabular AI models for large datasets and complex feature sets.
method Advanced tabular foundation model with data augmentation and distillation techniques.
result TabPFN-2.5 outperforms tuned tree-based models and AutoGluon 1.4 on benchmark 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.
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
Study compares various calibration methods for binary classification tasks.
problem Improving probabilistic predictions in binary classification models.
method Benchmarked 21 classifiers using 5 calibration methods on real data.
result Venn-Abers predictors and Beta calibration show the largest log-loss reductions.
HAR regression improves performance on small datasets.
problem Small datasets with complex functions.
method Data-adaptive kernel ridge regression using tensor-product spline basis.
result Achieves n−1/3 convergence rate for right-continuous functions. FAST-DAD distills complex ensemble models into faster, more accurate individual models.
problem Deploying complex AutoML ensemble predictors on tabular data is slow, large, and opaque.
method Data augmentation strategy based on Gibbs sampling from a self-attention pseudolikelihood estimator.
result FAST-DAD distillation produces significantly better individual models than standard training.
Improving in-context learning for latent space Bayesian optimization by adapting pretraining on molecular latent space.
problem Improving in-context learning for latent space Bayesian optimization.
method Adapting pretraining on molecular latent space.
result Achieving strong performance on held-out molecular optimization benchmarks.
Two new methods reduce random forest latency and improve accuracy.
problem High latency and memory demands in deep random forest models.
method DiNo and RanBu convert shallow random forests into efficient predictors.
result RanBu matches or exceeds full-depth random forest accuracy with up to 95% reduction in time.
TabCF simplifies causal inference for distributional effects.
problem Estimating causal effects with unmeasured confounding.
method Control function regression using tabular foundation models.
result Accurate, fast, and transparent causal estimation of distributional quantities.
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.
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.
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.
Markov boundary improves tabular prediction but not as expected.
problem Improving tabular prediction using the Markov boundary.
method Evaluation on a synthetic SCM benchmark with feature counts from 40 to 1000.
result Restricting a regressor to the Markov boundary often improves prediction, but existing discovery and training pipelines do not fully exploit this.
Shifts dataset evaluates uncertainty in real-world tasks across modalities.
problem Lack of standard datasets for evaluating uncertainty estimation and robustness to distributional shift.
method Proposes Shifts Dataset for evaluation of uncertainty estimates and robustness to distributional shift across tabular, audio, text, and sensor data.
result Baseline results for tabular weather prediction, machine translation, and SDC vehicle motion prediction.
PyTorch Frame simplifies multi-modal tabular learning with modular data and model handling.
problem Handling complex multi-modal tabular data in deep learning.
method A PyTorch-based framework that provides a data structure, model abstraction, and integration with external models.
result Demonstrated the effectiveness of PyTorch Frame in implementing and applying diverse tabular models to complex multi-modal tabular data.
MET learns tabular data representations without data augmentations.
problem Lack of effective self-supervised learning methods for tabular data.
method Reconstruction-based approach using masked encoding, with separate representations for each coordinate and adversarial reconstruction loss.
result MET achieves state-of-the-art performance on five diverse tabular datasets, improving up to 9% over current methods.
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.
Deep tabular models outperform GBDT in medical diagnosis tasks.
problem Transfer learning for tabular data in medical diagnosis.
method Proposes a pseudo-feature method for transfer learning between different feature sets.
result Tabular neural networks outperform GBDT in medical diagnosis tasks.
DAG-FM discovers causal relationships from heterogeneous data.
problem Challenges in causal discovery from heterogeneous causal mechanisms.
method DAG-FM uses two specialized Transformer-based sub-modules and a robust tabular interaction block to model complex row-column interactions.
result DAG-FM achieves state-of-the-art performance on synthetic and real-world datasets.
Generative models explain machine learning predictions with counterfactual instances.
problem Generating human-interpretable insights into machine learning model predictions.
method Sparse counterfactual explanations using conditional generative models.
result Single forward pass generates batches of counterfactual instances.
Robust Bayes-Assisted Conformal Prediction improves prediction set sizes.
problem Misspecification of Bayesian working model and prior misalignment.
method RoBAS (Robust Bayes-Assisted Shrinkage) framework with two instantiations.
result Improves prediction set sizes in shifted settings.
Robust Bayes-Assisted Conformal Prediction improves prediction set sizes.
problem Misspecification of Bayesian working model and prior misalignment.
method RoBAS (Robust Bayes-Assisted Shrinkage) framework with two instantiations.
result Proposed scores adapt to prior quality, reducing interval widths in shifted settings.
GACTGAN synthesizes tabular data better with less computational overhead.
problem Synthesizing mixed tabular data while balancing risk and utility.
method Integrates Bayesian posterior approximation with Stochastic Weight Averaging-Gaussian (SWAG) in CTGAN.
result GACTGAN produces better synthetic data with reduced privacy risk.