:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Sergazinov, Renat, Wu, Jing, Yin, Shao-An
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2506.02406
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Chunked TabPFN: Exact Training-Free In-Context Learning for Long-Context Tabular Data
by: Sergazinov, Renat, et al.
Published: (2025)

NTK-Guided Few-Shot Class Incremental Learning
by: Liu, Jingren, et al.
Published: (2024)

Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data
by: Yakushev, George, et al.
Published: (2025)

A spectral method for multi-view subspace learning using the product of projections
by: Sergazinov, Renat, et al.
Published: (2024)

Feature Identification via the Empirical NTK
by: Lin, Jennifer
Published: (2025)

Reactivation: Empirical NTK Dynamics Under Task Shifts
by: Liu, Yuzhi, et al.
Published: (2025)

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer
by: Wu, Yulun, et al.
Published: (2025)

HyperFast: Instant Classification for Tabular Data
by: Bonet, David, et al.
Published: (2024)

SwitchTab: Switched Autoencoders Are Effective Tabular Learners
by: Wu, Jing, et al.
Published: (2024)

Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process
by: Saito, Issei, et al.
Published: (2025)

Shortcut Features as Top Eigenfunctions of NTK: A Linear Neural Network Case and More
by: Lim, Jinwoo, et al.
Published: (2026)

Fast and Lightweight Backdoor Detection via Head Random Probing
by: Yu, Yinbo, et al.
Published: (2026)

An Explainable Gaussian Process Auto-encoder for Tabular Data
by: Zhang, Wei, et al.
Published: (2025)

A Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated Learning
by: Gai, Keke, et al.
Published: (2025)

FASTGEN: Fast and Cost-Effective Synthetic Tabular Data Generation with LLMs
by: Nguyen, Anh, et al.
Published: (2025)

Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
by: Livernoche, Victor, et al.
Published: (2026)

Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
by: Zhang, Zheyu, et al.
Published: (2026)

Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching
by: Xu, Huibo, et al.
Published: (2025)

APAR: Modeling Irregular Target Functions in Tabular Regression via Arithmetic-Aware Pre-Training and Adaptive-Regularized Fine-Tuning
by: Wu, Hong-Wei, et al.
Published: (2024)

TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees
by: Panagiotou, Emmanouil, et al.
Published: (2025)

Deep Feature Embedding for Tabular Data
by: Wu, Yuqian, et al.
Published: (2024)

Data Uniformity Improves Training Efficiency and More, with a Convergence Framework Beyond the NTK Regime
by: Wang, Yuqing, et al.
Published: (2025)

Pre-Hoc Predictions in AutoML: Leveraging LLMs to Enhance Model Selection and Benchmarking for Tabular datasets
by: Belkhiter, Yannis, et al.
Published: (2025)

TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data
by: Chen, Suiyao, et al.
Published: (2024)

Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training
by: Long, Yunbo, et al.
Published: (2026)

Minor First, Major Last: A Depth-Induced Implicit Bias of Sharpness-Aware Minimization
by: Moon, Chaewon, et al.
Published: (2026)

RVFL-X: A Novel Randomized Network Based on Complex Transformed Real-Valued Tabular Datasets
by: Sajid, M., et al.
Published: (2025)

FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
by: Chen, Kun, et al.
Published: (2024)

Learning Relational Tabular Data without Shared Features
by: Wu, Zhaomin, et al.
Published: (2025)

GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks
by: Sergazinov, Renat, et al.
Published: (2024)

Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning
by: Fei, Wu, et al.
Published: (2025)

DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model
by: Wen, Yizhu, et al.
Published: (2024)

Missing Data Multiple Imputation for Tabular Q-Learning in Online RL
by: Chasalow, Kyla, et al.
Published: (2025)

TabularMath: Evaluating Computational Extrapolation in Tabular Learning via Program-Verified Synthesis
by: Cheng, Zerui, et al.
Published: (2026)

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
by: Garg, Anurag, et al.
Published: (2025)

Cross-domain Random Pre-training with Prototypes for Reinforcement Learning
by: Liu, Xin, et al.
Published: (2023)

SG-XDEAT: Sparsity-Guided Cross-Dimensional and Cross-Encoding Attention with Target-Aware Conditioning in Tabular Learning
by: Cheng, Chih-Chuan, et al.
Published: (2025)

FASTER: Value-Guided Sampling for Fast RL
by: Dong, Perry, et al.
Published: (2026)

Robust Tabular Foundation Models
by: Peroni, Matthew, et al.
Published: (2025)

Universal Embeddings of Tabular Data
by: Franz, Astrid, et al.
Published: (2025)