Cover Image

Rethinking Distribution Shifts: Empirical Analysis and Inductive Modeling for Tabular Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Tianyu, Liu, Jiashuo, Cui, Peng, Namkoong, Hongseok
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LLM Embeddings Improve Test-time Adaptation to Tabular $Y|X$-Shifts
by: Zeng, Yibo, et al.
Published: (2024)

Data Heterogeneity Modeling for Trustworthy Machine Learning
by: Liu, Jiashuo, et al.
Published: (2025)

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
by: Namkoong, Hongseok, et al.
Published: (2020)

Empirical Likelihood for Nonsmooth Functionals
by: Namkoong, Hongseok
Published: (2026)

LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics
by: Liu, Jiashuo, et al.
Published: (2025)

Contextual Thompson Sampling via Generation of Missing Data
by: Zhang, Kelly W., et al.
Published: (2025)

DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
by: Liu, Jiashuo, et al.
Published: (2025)

Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift
by: Wu, Jiayun, et al.
Published: (2024)

Adaptive Elicitation of Latent Information Using Natural Language
by: Wang, Jimmy, et al.
Published: (2025)

Minimax Optimal Estimation of Stability Under Distribution Shift
by: Namkoong, Hongseok, et al.
Published: (2022)

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

Rethinking the Evaluation Protocol of Domain Generalization
by: Yu, Han, et al.
Published: (2023)

Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis
by: He, Zhipeng, et al.
Published: (2024)

Bias as a Virtue: Rethinking Generalization under Distribution Shifts
by: Chen, Ruixuan, et al.
Published: (2025)

Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
by: Liu, Tianyu, et al.
Published: (2024)

Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios
by: Apellániz, Patricia A., et al.
Published: (2024)

Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling
by: Mittal, Daksh, et al.
Published: (2025)

Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts
by: Ye, Naimeng, et al.
Published: (2024)

AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler
by: Kim, Changhun, et al.
Published: (2024)

OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale
by: Jiang, Dihong, et al.
Published: (2026)

SynthTools: A Framework for Scaling Synthetic Tools for Agent Development
by: Castellani, Tommaso, et al.
Published: (2025)

Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift
by: Brima, Yusuf, et al.
Published: (2026)

Rethinking Soft Actor-Critic in High-Dimensional Action Spaces: The Cost of Ignoring Distribution Shift
by: Chen, Yanjun, et al.
Published: (2024)

Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning
by: Rabbani, Shourav B., et al.
Published: (2023)

UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
by: Wang, Tongze, et al.
Published: (2026)

Automated Model Selection for Tabular Data
by: Amballa, Avinash, et al.
Published: (2024)

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms
by: Hu, James, et al.
Published: (2026)

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

Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution Detection
by: Dadalto, Eduardo, et al.
Published: (2024)

A Survey on Self-Supervised Learning for Non-Sequential Tabular Data
by: Wang, Wei-Yao, et al.
Published: (2024)

LoReC: Rethinking Large Language Models for Graph Data Analysis
by: Zhan, Hongyu, et al.
Published: (2026)

Generating Realistic Tabular Data with Large Language Models
by: Nguyen, Dang, et al.
Published: (2024)

Distilling Tabular Foundation Models for Structured Health Data
by: Tanna, Aditya, et al.
Published: (2026)

Comparing Task-Agnostic Embedding Models for Tabular Data
by: Hoppe, Frederik, et al.
Published: (2025)

TabularQGAN: A Quantum Generative Model for Tabular Data
by: Bhardwaj, Pallavi, et al.
Published: (2025)

Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective
by: Duan, Tianyang, et al.
Published: (2025)

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

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

TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
by: Fang, Liancheng, et al.
Published: (2025)

CausalTAD: Injecting Causal Knowledge into Large Language Models for Tabular Anomaly Detection
by: Wang, Ruiqi, et al.
Published: (2026)