arXiv Machine Learning

Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models

arXiv:2605. 13587v3 Announce Type: replace-cross Abstract: Preprocessing screening is often the most expensive part of a near-infrared spectroscopy calibration workflow.

arXiv Machine Learning

Jul 28

Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions

arXiv:2607. 22931v1 Announce Type: new Abstract: Analytic Continual Learning (ACL) offers a computationally efficient alternative to gradient-based approaches.

By Quyen Tran, Hai Nguyen, Quan Dao, Zhuowei Li, Nam Le, Trung Le, Dimitris Metaxas

benchmarks

arXiv Machine Learning

Jul 2

Spectroscopy Analysis with Machine Learning Regression for the Quantification of Carbon and Nitrogen Contents in Inceptisol and Oxisol Soil Types: Comparing Different Preprocessing and Validation methods as well as Feature Importance

arXiv:2607. 00834v1 Announce Type: new Abstract: Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing.

By Vinicius Herique Kieling, Guilherme Macedo Baggio, Felipe Augusto Bueno Rossi, Marco Antonio de Castro Barbosa, Dalcimar Casanova, Larissa Macedo dos Santos Tonial, Jefferson Tales Oliva

arXiv Machine Learning

Jul 20

Improving Improved Kernel PLS

arXiv:2607. 16138v1 Announce Type: new Abstract: Improved Kernel Partial Least Squares (IKPLS) algorithms 1 and 2 are among the fastest PLS calibration algorithms.

By Ole-Christian Galbo Engstr{\o}m

benchmarks

arXiv Machine Learning

Aug 27

Adaptive Regularization for Random Features: A Neighboring Early-Stopping Rule with Oracle-Rate Guarantees

The paper introduces a neighboring early‑stopping rule for adaptive regularization in kernel ridge regression with random features (KRR‑RF). By using a uniform grid in inverse regularization and comparing only adjacent estimators, the method reduces discrepancy checks and can be computed directly in the random‑feature space without forming the full kernel Gram matrix. Under standard source and capacity assumptions, the selected estimator achieves the oracle polynomial learning rate up to logarithmic factors, enabling regularization selection without prior knowledge of smoothness or capacity exponents.

By Caixing Wang, Zhibo Chen, Yue Wang

arXiv Statistics ML

Sep 24

Gaussian-process surrogate indicators for residual-based adaptive GMsFEM

arXiv:2609.26843v1 Announce Type: cross Abstract: Residual-based adaptive GMsFEM for high-contrast elliptic problems repeatedly evaluates local weighted $H^{-1}$ indicators on every coarse neighborho...

By Siqing Liu, Eric Chung, Yiran Wang

arXiv Machine Learning

Aug 4

From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy

arXiv:2608. 00608v1 Announce Type: new Abstract: Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties.

By Viacheslav Barkov, Jonas Schmidinger, Robin Gebbers, Martin Atzmueller

llms benchmarks

arXiv Machine Learning

Sep 23

Fast Matrix Multiplication in fp8: Certified Coefficient Optimization and Measured Error

arXiv:2609.26077v1 Announce Type: new Abstract: A Strassen-type algorithm has many realizations with the same exact product and multiplication count yet different fp8 error because basis changes resh...

By Shuxiao Xie, Shuyang Xie, Yuan Cao, Dezhi Ran, Wei Yang, Tao Xie

arXiv Machine Learning

Sep 22

Predicting Out-of-Distribution Generalization of Neural Operators via Observable Spectral Error Decomposition

arXiv:2609. 23529v1 Announce Type: new Abstract: Neural operators have emerged as powerful surrogates for solving partial differential equations (PDEs), yet their reliability under distribution shift remains a critical barrier to deployment.

By Hang-Cheng Dong, Pengcheng Cheng

benchmarks

arXiv Machine Learning

Aug 5

Joint Affine Spectral Shaping: Coupling Weight and Bias Updates Beyond Weight-Only Muon

arXiv:2608. 02991v1 Announce Type: new Abstract: Matrix spectral optimizers reshape weight-update spectra but usually delegate vector-valued biases to a separate optimizer.

By Gongyue Zhang, Honghai Liu

benchmarks safety

arXiv Machine Learning

Jun 16

Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data

arXiv:2606. 15950v1 Announce Type: cross Abstract: Conformal prediction gives prediction intervals with finite-sample coverage when the data are exchangeable.

By Jeffery Opoku, David Banahene

arXiv Machine Learning

Jul 30

Between Gradient and Natural Gradient: A Continuum of LoRA Initializations

arXiv:2607. 26247v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) fine-tunes large pretrained models at a fraction of the cost of full fine-tuning, but its performance depends strongly on how the adapters are initialized.

By Dianze Liu, Farshid Ghezelbash

llms fine-tuning

arXiv AI

Jul 29

Spectral Truncation in Synthetic Control

arXiv:2607. 25074v1 Announce Type: cross Abstract: Synthetic control (SC) matches a treated unit's pre-treatment trajectory to a weighted combination of donor units.

By Mojtaba Eslami

safety

Related stories