FinDiff generates synthetic financial data for regulatory tasks.
problem Sharing microdata for research due to privacy regulations.
method Diffusion model using embedding encodings for mixed modality financial data.
result FinDiff excels in generating high-fidelity, privacy-preserving synthetic financial data.
This paper improves credit scoring models using a novel dataset distillation technique.
problem Limited scalability of pretrained models for tabular credit scoring datasets.
method Integrates class imbalance-aware dataset distillation with pretrained models.
result Improved AUC by 2.5% in financial datasets.
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.
Predict stock price movements using financial data and news articles with LLMs.
problem Predicting stock price movements using financial data and news articles.
method Combining financial data and news articles, employing pre-trained LLMs, and using retrieval augmentation techniques.
result Predicted stock price movements with a weighted F1-score of 58.5% and 59.1%.
Framework improves financial predictions with deep learning models.
problem Adverse financial conditions like regime changes and low signal-to-noise ratios.
method Incremental use of decision trees and XGBoost models for robust performance.
result Two-layer deep ensemble of XGBoost models outperforms single models under different market regimes.
Synthetic data improves financial models without real data.
problem Lack of real financial data due to privacy and regulation.
method Application of synthetic data across various financial data types.
result Synthetic data enhances financial model accuracy and fairness.
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.
FinBloom enhances LLMs for real-time financial queries.
problem Limited access to real-time financial data by LLMs.
method Developed a custom 7B parameter LLM, Financial Context Dataset, and a Financial Agent.
result Significantly improved LLMs' capability to handle dynamic financial tasks.
New framework explains ML credit scoring models using counterfactual examples.
problem Explaining complex ML models in finance for credit scoring.
method Adversarial counterfactual examples for tabular data.
result Proposes a method to generate realistic counterfactual examples for 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.
Enhanced CNN for financial data improves predictive accuracy and stability.
problem Complexity and variability in financial data.
method Normalization and Gradient Reduction Architecture.
result Improvement in model accuracy and stability.
In the general framework of a semimartingale financial model and a utility function U defined on the positive real line, we compute the first-order expansion of marginal utility-based prices with respect to a ``small'' number of random endowments. We show that this linear approximation has some important qualitative …
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.
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.
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.
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.
Offline RL algorithms protect privacy while learning from sensitive data.
problem Learning sensitive data in financial, legal, and healthcare applications.
method Differential privacy guarantees for offline RL algorithms.
result Provable prevention of privacy risks with strong learning bounds.
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.
Fed-TDA augments federated tabular data to improve performance and privacy.
problem Non-IID data challenges in federated learning.
method Synthesizes tabular data using simple statistics for augmentation.
result Fed-TDA improves test performance and communication efficiency.
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 framework evaluates synthetic tabular data quality objectively.
problem Lack of an objective interpretation of tabular data metrics.
method Proposes a single mathematical objective for synthetic tabular data distribution, structurally decomposes it, and unifies existing metrics.
result Synthesizers that represent tabular structure outperform other methods, especially on smaller datasets.
Study detects synthetic tabular data across different tables.
problem Detecting synthetic tabular data in varied tables.
method Four table-agnostic detectors combined with preprocessing schemes.
result Cross-table learning possible with naive preprocessing, but cross-table transfer challenging.
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.
DNF-Net tackles tabular data challenges with neural architecture.
problem Handling tabular data efficiently using neural networks.
method DNF-Net uses a neural architecture with inductive bias corresponding to logical Boolean formulas in disjunctive normal form over affine soft-threshold decision terms.
result DNF-Net significantly outperforms fully connected networks on tabular data.
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.
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
New algorithms for clustering and synthetic data generation of heterogeneous tabular datasets.
problem Clustering and generating synthetic data from heterogeneous tabular datasets with hidden cluster structure.
method Developed MMM and MMMsynth algorithms for clustering and synthetic data generation.
result MMMsynth algorithm outperforms other literature tabular-data generators and approaches real data performance.
FinTMMBench benchmarks RAG systems for finance tasks across multiple data types and time periods.
problem Evaluating temporal-aware multi-modal retrieval augmented generation in finance.
method TMMHybridRAG method that converts and integrates data from various modalities and temporal information.
result Demonstrated effectiveness of TMMHybridRAG in diverse financial analysis tasks.
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.
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.
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.
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.
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.
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.
Open-FinLLMs tackle financial tasks with multimodal capabilities.
problem Financial LLMs lack multimodal capabilities and real-world applicability.
method Developed Open-FinLLMs, an open-source multimodal financial LLM suite.
result Open-FinLLMs outperform advanced financial and general LLMs in diverse tasks.
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.
GEM-T generates synthetic tabular data by fitting moments, outperforming neural networks.
problem Generating synthetic tabular data from limited or sensitive real-world data.
method Generative Entropy Maximization (MaxEnt) for tables, capturing nth-order interactions.
result GEM-T matches or exceeds deep neural network approaches in 23 out of 34 datasets.
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.
SubTab turns tabular data into a multi-view problem for better representation learning.
problem Lack of structure in tabular data makes it hard to apply effective self-supervised learning methods.
method Divides tabular features into subsets and uses autoencoder-like reconstruction for collaborative inference.
result SubTab achieves state-of-the-art performance on tabular datasets, matching or surpassing CNN-based methods.
FinPT uses large pretrained models to predict financial risks.
problem Outdated algorithms and lack of open financial benchmarks.
method Profile Tuning on large pretrained foundation models.
result Demonstrated effectiveness on FinBench datasets.
AugMask trains diffusion models on incomplete tabular data by augmenting missing values and applying denoising supervision.
problem Training diffusion models on incomplete tabular data with missing values.
method AugMask uses stochastic augmentation and denoising supervision to adapt diffusion models to incomplete data.
result AugMask enables diffusion-based tabular generators to outperform specialized missing-aware baselines across various datasets and missingness regimes.
This paper introduces DPI, a new metric to assess data-copying risk in tabular data.
problem Measuring privacy risk of data-copying in tabular generative models.
method Proposes Data Plagiarism Index (DPI) for evaluating data-copying risk.
result DPI identifies data-copying threats to tabular data models, highlighting privacy and fairness issues.
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.
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.
Tree-based models outperform deep learning on tabular data, especially for medium-sized datasets.
problem Understanding why tree-based models outperform deep learning on tabular data.
method Extensive benchmarks of tree-based and deep learning models on 45 datasets, accounting for hyperparameters.
result Tree-based models remain state-of-the-art on medium-sized tabular data, even without hyperparameter optimization.
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.
This study benchmarks tabular data generation models, optimizing hyperparameters and feature encodings.
problem Generating realistic tabular data is challenging due to heterogeneity, non-smooth distributions, and complex dependencies.
method Comprehensive evaluation of five model families on 16 datasets, considering hyperparameters, feature encodings, and architectures.
result Large-scale dataset-specific tuning significantly improves model performance, especially for diffusion-based models.
DP-FedTabDiff generates private synthetic tabular data using diffusion models and differential privacy.
problem Privacy-preserving synthetic data generation for tabular data in regulated domains.
method Combines Differential Privacy, Federated Learning, and Denoising Diffusion Probabilistic Models.
result Achieves significant privacy improvements without compromising data quality.