Cover Image

Discrete Tokenization Unlocks Transformers for Calibrated Tabular Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Elmatad, Yael S.
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits
by: Czech, Eric, et al.
Published: (2026)

Weather Maps as Tokens: Transformers for Renewable Energy Forecasting
by: Battini, Federico
Published: (2025)

Calibrating Tabular Anomaly Detection via Optimal Transport
by: Ye, Hangting, et al.
Published: (2026)

Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistency
by: Itzhakev, Yael, et al.
Published: (2024)

Unlocking the Potential of Model Calibration in Federated Learning
by: Chu, Yun-Wei, et al.
Published: (2024)

HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting
by: Feng, Shibo, et al.
Published: (2025)

TFWT: Tabular Feature Weighting with Transformer
by: Zhang, Xinhao, et al.
Published: (2024)

PORTAL: Scalable Tabular Foundation Models via Content-Specific Tokenization
by: Spinaci, Marco, et al.
Published: (2024)

How to RETIRE Tabular Data in Favor of Discrete Digital Signal Representation
by: Zyblewski, Paweł, et al.
Published: (2025)

SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
by: Ilbert, Romain, et al.
Published: (2024)

PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
by: Ye, Hangting, et al.
Published: (2024)

Transformers with Stochastic Competition for Tabular Data Modelling
by: Voskou, Andreas, et al.
Published: (2024)

Basis Transformers for Multi-Task Tabular Regression
by: Loh, Wei Min, et al.
Published: (2025)

Calibrated Probabilistic Forecasts for Arbitrary Sequences
by: Marx, Charles, et al.
Published: (2024)

Forecast Evaluation and the Relationship of Regret and Calibration
by: Derr, Rabanus, et al.
Published: (2024)

Unlocking Guidance for Discrete State-Space Diffusion and Flow Models
by: Nisonoff, Hunter, et al.
Published: (2024)

Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs
by: Nakkiran, Preetum, et al.
Published: (2025)

From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization
by: Tao, Xiaoyu, et al.
Published: (2025)

Towards a Relationship-Aware Transformer for Tabular Data
by: Konstantinov, Andrei V., et al.
Published: (2025)

Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
by: Lotfi, Sanae, et al.
Published: (2024)

Unlocking the Power of LSTM for Long Term Time Series Forecasting
by: Kong, Yaxuan, et al.
Published: (2024)

Patch-Level Tokenization with CNN Encoders and Attention for Improved Transformer Time-Series Forecasting
by: Nagrath, Saurish, et al.
Published: (2026)

Tab-PET: Graph-Based Positional Encodings for Tabular Transformers
by: Leng, Yunze, et al.
Published: (2025)

Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap
by: Tanna, Aditya, et al.
Published: (2026)

DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models
by: Van Ness, Mike, et al.
Published: (2024)

Mitigating Premature Discretization with Progressive Quantization for Robust Vector Tokenization
by: Zhao, Wenhao, et al.
Published: (2026)

Posterior-Calibrated Causal Circuits in Variational Autoencoders: Why Image-Domain Interpretability Fails on Tabular Data
by: Roy, Dip, et al.
Published: (2026)

Non-collective Calibrating Strategy for Time Series Forecasting
by: Wang, Bin, et al.
Published: (2025)

Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
by: Wang, Yingshuo, et al.
Published: (2026)

Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting
by: Wang, Chengsen, et al.
Published: (2025)

Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers
by: Bechler-Speicher, Maya, et al.
Published: (2026)

TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
by: Wang, Haiyang, et al.
Published: (2024)

Unlocking the Pre-Trained Model as a Dual-Alignment Calibrator for Post-Trained LLMs
by: Luo, Beier, et al.
Published: (2026)

Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model
by: Yang, Yuting, et al.
Published: (2025)

GeoAggregator: An Efficient Transformer Model for Geo-Spatial Tabular Data
by: Deng, Rui, et al.
Published: (2025)

Fine-tuned In-Context Learning Transformers are Excellent Tabular Data Classifiers
by: Breejen, Felix den, et al.
Published: (2024)

A Hybrid Machine Learning Approach for Synthetic Data Generation with Post Hoc Calibration for Clinical Tabular Datasets
by: Mahin, Md Ibrahim Shikder, et al.
Published: (2025)

Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science with Table-Specific Pretraining
by: Yang, Yazheng, et al.
Published: (2024)

NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
by: Liu, Enshu, et al.
Published: (2026)

Full Swap Regret and Discretized Calibration
by: Fishelson, Maxwell, et al.
Published: (2025)