arXiv Machine Learning August 19, 2026 By Anh Tuan Nguyen, Viet Anh Nguyen

Tight Bounds for Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function

Read the original on arXiv Machine Learning →

The paper establishes tight pseudo-dimension bounds for data-driven multiple hyper‑parameter tuning with structured loss functions. By refining upper bounds through real algebraic geometry and analyzing invariant connected sign cells, the authors avoid over‑counting and achieve sharper sample complexities. A multi‑regime lower‑bound framework demonstrates that these upper bounds are tight, and the approach is extended to general bi‑level validation‑loss tuning and broader semi‑algebraic applications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

2d ago

How Many Samples Are Enough for Learning Across Domains?

The paper investigates how many data samples per domain are needed for effective learning across multiple domains. It derives criteria from learning bounds that reveal an inverse linear relationship between the number of training domains and the required samples per domain, offering theoretical guidance for dataset adequacy and construction. The study also establishes a close link between in-domain learning and out-of-domain generalization through new generalization bounds.

By Hong Zheng

arXiv Machine Learning

4d ago

Preferent Compression Bounds Are Tight

arXiv:2609.36030v1 Announce Type: new Abstract: The lack of rigorous safety and performance certificates remains a key bottleneck to the deployment of modern learning-based methods. Sample compressio...

By Dario Paccagnan, Marius Tirlea

benchmarks safety

arXiv Machine Learning

Jun 9

Parameter Tuning with Generalization Guarantees for GPU-Accelerated Linear Programming

arXiv:2606. 08638v1 Announce Type: cross Abstract: Recent research has developed practical, parallelizable first-order methods for large scale linear programming, but performance is highly dependent on hyperparameter selection.

By Siddharth Prasad, Dravyansh Sharma

benchmarks

arXiv Machine Learning

Jun 17

Tight $L_\infty$ Sample Complexity for Low-Degree and Sparse Boolean Polynomials

arXiv:2606. 17319v1 Announce Type: cross Abstract: Motivated by the optimization of bounded binary black-box functions, we study the problem of learning polynomial surrogates over the Boolean hypercube.

By Jasper van Doornmalen, Mathieu Molina, Victor Verdugo, Jos\'e Verschae

arXiv Machine Learning

Jul 27

Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates

arXiv:2607. 22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are otherwise numerically expensive to solve.

By Anjian Li, Ryne Beeson

arXiv Machine Learning

Jun 10

The hyper-scaled NLP bound for maximum-entropy remote sampling

arXiv:2601. 20970v3 Announce Type: replace-cross Abstract: The maximum-entropy remote sampling problem (MERSP) is to select a subset of $s$ random variables from a set of $n$ random variables, so as to maximize the information concerning a set of target random variables that are not directly observable.

By Gabriel Ponte, Marcia Fampa, Jon Lee

nlp benchmarks

One email a morning, machine-written

Related stories

How Many Samples Are Enough for Learning Across Domains?

Preferent Compression Bounds Are Tight

Parameter Tuning with Generalization Guarantees for GPU-Accelerated Linear Programming

Tight $L_\infty$ Sample Complexity for Low-Degree and Sparse Boolean Polynomials

Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates

The hyper-scaled NLP bound for maximum-entropy remote sampling