arXiv AI

Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping

The paper systematically evaluates regression objectives for tabular deep learning across many datasets and neural backbones, finding that most alternatives to Mean Squared Error (MSE) offer limited or inconsistent gains, while quantile loss consistently improves performance. It identifies a limitation of global early stopping: different noise levels in target regions reach optimal performance at different epochs, causing a compromise. To address this, the authors propose a heteroskedasticity‑aware extension of quantile loss using input‑dependent label smoothing, which outperforms standard quantile loss on several backbones.

arXiv Machine Learning

Sep 11

ExpTest: Loss-Curve Hypothesis Testing for Autonomous Learning-Rate Selection in Deep Neural Networks

ExpTest is an autonomous learning‑rate controller that uses the training loss curve as an online signal to perform sequential statistical tests on theoretically motivated windows, detecting convergent behavior and triggering learning‑rate reductions. It combines a covariance‑based initial learning‑rate estimate, curvature‑motivated window sizing, and a two‑phase test‑driven decay, relying on the approximately exponential decay predicted under linearized network dynamics. Experiments on regression, classification, forecasting, and natural‑language tasks across various architectures show that ExpTest achieves competitive performance compared to hand‑tuned SGD baselines and recent learning‑rate‑free methods, without requiring manual initial learning‑rate selection or predefined scheduling.

By Zan Chaudhry, Naoko Mizuno

llms agents

arXiv AI

Aug 11

Tabular Numeric Stretch Transformation

arXiv:2608. 09162v1 Announce Type: cross Abstract: Tabular data presents unique challenges for deep learning due to its heterogeneous nature, where numeric features exhibit diverse distributions, scales, and statistical properties.

By Zihao Ye, Juyong Kim, Johnna Sundberg, Burak Varici, Pradeep Ravikumar

benchmarks

arXiv AI

Jun 9

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

arXiv:2602. 15327v2 Announce Type: replace-cross Abstract: Machine learning model performance improvements tend to arise from competition and application.

By Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham Kakade

llms benchmarks

arXiv Machine Learning

Jun 5

Mitigating the Curse of Dimensionality in Uniform Convergence of Deep Neural Networks via Smooth Activations

arXiv:2606. 05599v1 Announce Type: new Abstract: This paper establishes a theoretical framework for the uniform convergence of smoothly activated deep neural network (DNN) estimators.

By Yizhe Ding, Runze Li, Jia Liu, Lingzhou Xue

arXiv Machine Learning

Jul 8

Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning

arXiv:2607. 06151v1 Announce Type: new Abstract: Generalization remains a pivotal challenge in deep learning, where traditional optimizers like Stochastic Gradient Descent (SGD) often converge to sharp minima, leading to overfitting and reduced performance on unseen data.

By Yao Fu, Chunxia Zhang, Junmin Liu, Yihang Jin, Haishan Ye, Yuanao Yang

benchmarks

arXiv AI

Aug 11

iLTM: Integrated Large Tabular Model

arXiv:2511. 15941v2 Announce Type: replace-cross Abstract: Tabular data underpins decisions across science, industry, and public services.

By David Bonet, Mar\c{c}al Comajoan Cara, Alvaro Calafell, Daniel Mas Montserrat, Alexander G. Ioannidis

rag fine-tuning

arXiv Machine Learning

Aug 4

GradientStabilizer:Fix the Norm, Not the Gradient

arXiv:2502. 17055v5 Announce Type: replace Abstract: Training instability in modern deep learning systems is frequently triggered by rare but extreme gradient-norm spikes, which can induce oversized parameter updates, corrupt optimizer state, and lead to slow recovery or divergence.

By Tianjin Huang, Zhangyang Wang, Haotian Hu, Zhenyu Zhang, Gaojie Jin, Xiang Li, Li Shen, Jiaxing Shang, Tianlong Chen, Ke Li, Lu Liu, Qingsong Wen, Shiwei Liu

llms reinforcement-learning efficiency

arXiv Machine Learning

Jun 5

Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning

arXiv:2605. 27991v2 Announce Type: replace-cross Abstract: Gradient-flow optimization is usually viewed as an algorithmic procedure for minimizing empirical loss, with training duration selected by validation or heuristic early-stopping rules.

By Minhao Yao, Ruoyu Wang, Xihong Lin, Lin Liu, Zhonghua Liu

arXiv AI

Jun 30

Beyond IID: How General Are Tabular Foundation Models, Really?

arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.

By Lennart Purucker, Andrej Tschalzev, Nick Erickson, Gioia Blayer, David Holzm\"uller, Alan Arazi, Alexander Pfefferle, Mustafa Tajjar, Ga\"el Varoquaux, Frank Hutter

benchmarks

arXiv Machine Learning

Aug 14

TabH2O: A Unified Foundation Model for Tabular Prediction

arXiv:2605. 18383v2 Announce Type: replace Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning.

By Pascal Pfeiffer, Dmitry Gordeev, Mathias M\"uller, Laura Fink, Joan Salv\`a Soler, Mark Landry, Branden Murray, Marcos V. Conde, Sri Satish Ambati

llms benchmarks

arXiv Machine Learning

Aug 24

Generalization Measures under Controlled Covariate Shift: A Regime-Aware Benchmark

arXiv:2602.01718v2 Announce Type: replace Abstract: Predicting generalization from quantities available before target-test evaluation remains a central challenge in deep learning. The systematic benc...

By Sora Nakai, Youssef Fadhloun, Kacem Mathlouthi, Kotaro Yoshida, Ganesh Talluri, Ioannis Mitliagkas, Hiroki Naganuma

benchmarks

arXiv Machine Learning

Jun 8

MIST: Mutual Information Estimation Via Supervised Training

arXiv:2511. 18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators.

By German Gritsai, Megan Richards, Maxime M\'eloux, Kyunghyun Cho, Maxime Peyrard

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