SAINT improves neural networks for tabular data with row attention and contrastive pre-training.
problem Tabular data challenges in machine learning applications.
method SAINT combines row and column attention with contrastive self-supervised pre-training.
result SAINT outperforms previous deep learning methods and even gradient boosting methods on benchmark tasks.
AutoDiff combines auto-encoder and diffusion model for realistic tabular data synthesis.
problem Generating realistic synthetic tabular data with heterogeneous features.
method Employing auto-encoder architecture to handle tabular data's complexity.
result Synthetic tables from AutoDiff show good statistical fidelity and perform well in machine learning tasks.
GAMformer bridges tabular models and interpretability, offering a single-pass approach.
problem Lack of interpretability in tabular foundation models like TabPFN.
method In-context learning for GAM shape functions, training on synthetic data.
result GAMformer performs comparably to other leading GAMs across various classification benchmarks.
Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is …
HyperFast instantly classifies tabular data in one pass.
problem Computational burden and time-consuming hyperparameter tuning for deep learning models.
method Meta-trained hypernetwork for instant classification.
result Significantly faster than competing methods with competitive performance.
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.
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.
New method visualizes tabular feature semantics for better model understanding.
problem Lack of feature interaction interpretation in tabular ML models.
method Feature Vectors method for global tabular dataset interpretability.
result Visualizes semantic relationships among tabular features.
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.
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.
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.
Picket guards against corrupted data in machine learning models.
problem Data corruption biases models and invalidates predictions.
method PicketNet detects corrupted data using self-supervised deep learning; flags corrupted queries online.
result Picket consistently protects models from corrupted data during training and deployment.
Generative model improves tabular data density estimation.
problem Challenges in estimating tabular data distribution.
method Tensor contraction layers and transformers in VAEs.
result Embedding representations improve density estimation metrics.
Framework optimizes model performance and interpretability for tabular data.
problem Balancing model performance and interpretability in machine learning models.
method Model-agnostic multi-objective optimization framework with evolutionary algorithm.
result Framework generates diverse models that trade off performance and interpretability efficiently.
Orion-Bix combines biaxial attention and meta-learning for tabular few-shot learning.
problem Scaling and generalizing tabular models with mixed numeric and categorical fields, weak feature structure, and limited labeled data.
method Orion-Bix uses biaxial attention and meta-learned in-context reasoning to efficiently capture local and global dependencies.
result Orion-Bix outperforms gradient-boosting baselines and state-of-the-art tabular models on public benchmarks.
Synthetic tabular data improves privacy while maintaining model performance.
problem Protecting privacy in synthetic data generation for machine learning.
method Deep generative models for tabular data, emphasizing privacy and model performance.
result Deep generative models enhance synthetic data generation for tabular datasets.
Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions. While research on the topic has mainly been focusing on the image domain, numerous industrial applications, in particular in finance, rely on standard tabular data. In this paper, we discuss the…
TabNAS improves neural architecture search for tabular datasets by rejecting suboptimal architectures.
problem Finding optimal neural architectures for tabular datasets with resource constraints.
method Develops a reinforcement learning controller motivated by rejection sampling to handle resource constraints.
result TabNAS finds better models that obey resource constraints compared to previous methods.
Tab-TRM uses recursive model for insurance pricing on tabular data.
problem Insurance pricing on tabular data.
method Adapts recursive latent reasoning to insurance modeling using a compact, parameter-efficient network.
result Improves insurance pricing accuracy using iterative refinement of latent tokens.
DeepIFSAC uses attention mechanisms and contrastive learning to impute missing values in tabular data.
problem Missing values in tabular data, especially when high and not random.
method Row and column attention in a contrastive learning framework with CutMix data augmentation.
result Proposed method outperforms state-of-the-art methods for missing rates between 10% and 90% and various missing value types.
The paper analyzes LIME for tabular data and proves its behavior in large samples.
problem Understanding the behavior of LIME in tabular data settings.
method Theoretical analysis of LIME's behavior in tabular data, proving its properties in the large sample limit.
result LIME provides explanations proportional to the coefficients of the function in linear cases, but can produce misleading explanations for partition-based models.
Study robustness of global feature effect explanations in machine learning models.
problem Vulnerability of global feature effect explanations to data and model perturbations.
method Theoretical bounds and experimental evaluation of partial dependence plots and accumulated local effects.
result Quantifies the gap between best and worst-case scenarios of misinterpreting machine learning predictions globally.
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
Deep networks and forests perform differently with small samples.
problem Comparing deep networks and decision forests for small sample sizes.
method Unified view of both methods as partition and vote schemes, empirical comparison on various datasets.
result Forests excel with small tabular and structured data, deep nets better with larger samples.
Paper compares two local explanation methods for machine learning models.
problem Comparing two local explanation methods for machine learning models.
method Integrated Gradients and Baseline Shapley methods.
result Additional insights on comparative behavior for tabular data and neural networks.
RTVAE uses β-divergence to detect anomalies in tabular data robustly.
problem Outliers in tabular data affect VAE training and anomaly detection.
method Robust VAE with β-divergence for mixed categorical and continuous features.
result Demonstrates effectiveness on network traffic anomaly detection.
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.
New method learns interpretable concepts from user feedback for high-dimensional data.
problem Lack of interpretable concepts in machine learning models trained on high-dimensional tabular data.
method Proposes a method for learning transparent concept definitions from user labeling of concept features, not instances.
result Demonstrates more efficient learning of aligned concept definitions from user feedback compared to alternative transparent approaches.
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.
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.
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.
WISCA generates consensus explanations from conflicting model-agnostic interpretability methods.
problem Conflicting explanations from diverse interpretability algorithms.
method WISCA integrates class probability and normalized attributions to generate consistent explanations.
result WISCA consistently aligns with the most reliable individual method, improving explanation reliability.
Tab2vox converts tabular data into 3D images for improved demand forecasting.
problem Forecasting demand influenced by multi-level causes and large volatility.
method Tab2vox neural architecture search (NAS) model to convert tabular data into 3D voxel images for 3D CNN forecasting.
result 3D CNN forecasting model outperforms existing tabular data techniques.
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all…
The paper tackles interpreting DCM with image data by addressing data isomorphism.
problem Interpreting DCM with image data due to isomorphic information.
method Proposes and benchmarks two methodologies: architectural adjustments and data source mitigation.
result Direct data source mitigation is more effective for maintaining DCM's interpretability.
Multi-party machine learning leaks global dataset properties even with black-box access.
problem Leakage of global dataset properties in multi-party machine learning.
method Demonstrated leakage of sensitive attribute distributions in pooled data.
result A curious party can infer sensitive attribute distributions in other parties' data with high accuracy.
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.
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.
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.
Paper identifies how neural networks learn features.
problem Understanding how neural networks automatically select features.
method Deep Neural Feature Ansatz, implementing average gradient outer product.
result Recursive Feature Machines achieve state-of-the-art performance.
Tabular data analysis shows GBDTs and NNs perform similarly on most datasets.
problem Determining when NNs outperform GBDTs on tabular data.
method Large-scale comparison of 19 algorithms across 176 datasets.
result For many datasets, GBDTs and NNs perform similarly, with hyperparameter tuning often more important than algorithm choice.
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.
TabPFN doesn't outperform GLM and XGBoost for motor insurance pricing.
problem Improving insurance pricing models using Tabular Foundation Models (TFMs).
method Pre-training on synthetic datasets and in-context learning for inference.
result TabPFN does not consistently outperform established baselines, has longer inference times, and is sensitive to training set size.
BiSHop tackles tabular data challenges with sparse Hopfield layers.
problem Non-rotationally invariant data structure and feature sparsity in tabular data.
method Sequential column-wise and row-wise processing through interconnected directional learning modules with generalized sparse modern Hopfield layers.
result BiSHop surpasses current SOTA methods with significantly less hyperparameter tuning.
In 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev. In terms of data science, his achievement can be viewed as a successful example of feature embedding based on human cognition: chemical properties of all known elements at that time were compressed onto the two-dimensional…
Improves tabular data augmentation for contrastive learning.
problem Ineffective augmentation techniques for tabular data.
method Class-conditioned and feature-correlation based augmentation.
result Consistently outperforms conventional corruption methods.
Method improves model performance on segments with local distribution shifts.
problem Improving model generalization across multiple data segments with local distribution differences.
method Two-stage multiply robust estimation method for tabular data analysis.
result Significantly improves prediction accuracy and robustness on regression and classification tasks.
New research finds uncertainty estimation techniques fail to reliably detect abnormal medical cases.
problem Uncertainty estimation does not reliably detect out-of-distribution patients in medical tabular data.
method A series of tests on various uncertainty estimation techniques on real-world medical data.
result Almost all techniques fail to identify out-of-distribution patients, contradicting earlier findings.