arXiv subjects

Min-Kook Suh

Publications and source records attributed to Min-Kook Suh.

4 recordsLinked to original sources

arXiv2026

EXAONE Tabular 1.0 : Technical Report

EXAONE Tabular is a compact tabular foundation model family for classification and regression via in-context learning, producing predictions without dataset-specific gradient updates. Pretrained exclusively on a synthetic structural-causal-model (SCM) prior, its central contribution is an architecture-centered redesign of tabular in-context learning. Rather than compressing features into a fixed row embedding before a separate row-level learner, EXAONE Tabular interleaves feature-axis attention within each item with support-conditioned item-axis attention within each feature at every Transformer layer, mediated by item-summary and feature-summary tokens. Across four public benchmarks, EXAONE Tabular combines strong predictive performance with high efficiency. On TabArena, its 20.81M-parameter classification model ranks first overall, surpassing tuned ensembles and 4-hour AutoML pipelines, while regression reaches the performance regime of the 1.64B-parameter TabFM at roughly 1/11 the inference cost. On BCCO and TALENT, EXAONE Tabular ranks second in classification and first in regression. On ScoringBench, it achieves the best mean rank for both point-estimation and predictive-distribution quality, leading the $R^2$, RMSE, and CRPS evaluations. Together, these results establish EXAONE Tabular as a state-of-the-art compact tabular foundation model family, combining strong predictive performance across classification, point regression, and probabilistic regression with an efficient model design.

cs.LG↗

arXiv2025

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains

Despite the widespread use of tabular data in real-world applications, most benchmarks rely on average-case metrics, which fail to reveal how model behavior varies across diverse data regimes. To address this, we propose MultiTab, a benchmark suite and evaluation framework for multi-dimensional, data-aware analysis of tabular learning algorithms. Rather than comparing models only in aggregate, MultiTab categorizes 196 publicly available datasets along key data characteristics, including sample size, label imbalance, and feature interaction, and evaluates 13 representative models spanning a range of inductive biases. Our analysis shows that model performance is highly sensitive to such regimes: for example, models using sample-level similarity excel on datasets with large sample sizes or high inter-feature correlation, while models encoding inter-feature dependencies perform best with weakly correlated features. These findings reveal that inductive biases do not always behave as intended, and that regime-aware evaluation is essential for understanding and improving model behavior. MultiTab enables more principled model design and offers practical guidance for selecting models tailored to specific data characteristics. All datasets, code, and optimization logs are publicly available at https://huggingface.co/datasets/LGAI-DILab/Multitab.

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arXiv2024

Interpreting Adaptive Gradient Methods by Parameter Scaling for Learning-Rate-Free Optimization

We address the challenge of estimating the learning rate for adaptive gradient methods used in training deep neural networks. While several learning-rate-free approaches have been proposed, they are typically tailored for steepest descent. However, although steepest descent methods offer an intuitive approach to finding minima, many deep learning applications require adaptive gradient methods to achieve faster convergence. In this paper, we interpret adaptive gradient methods as steepest descent applied on parameter-scaled networks, proposing learning-rate-free adaptive gradient methods. Experimental results verify the effectiveness of this approach, demonstrating comparable performance to hand-tuned learning rates across various scenarios. This work extends the applicability of learning-rate-free methods, enhancing training with adaptive gradient methods.

cs.LG↗

arXiv2023

Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels

Although contrastive learning methods have shown prevailing performance on a variety of representation learning tasks, they encounter difficulty when the training dataset is long-tailed. Many researchers have combined contrastive learning and a logit adjustment technique to address this problem, but the combinations are done ad-hoc and a theoretical background has not yet been provided. The goal of this paper is to provide the background and further improve the performance. First, we show that the fundamental reason contrastive learning methods struggle with long-tailed tasks is that they try to maximize the mutual information maximization between latent features and input data. As ground-truth labels are not considered in the maximization, they are not able to address imbalances between class labels. Rather, we interpret the long-tailed recognition task as a mutual information maximization between latent features and ground-truth labels. This approach integrates contrastive learning and logit adjustment seamlessly to derive a loss function that shows state-of-the-art performance on long-tailed recognition benchmarks. It also demonstrates its efficacy in image segmentation tasks, verifying its versatility beyond image classification.

cs.LG↗