Theoretical foundations of the integral indicator application in hyperparametric optimization

• URL: http://arxiv.org/abs/2508.20550v1
• Date: Thu, 28 Aug 2025 08:41:38 GMT
• Title: Theoretical foundations of the integral indicator application in hyperparametric optimization
• Abstract summary: The article discusses the concept of hyperparametric optimization of recommendation algorithms using an integral assessment that combines various performance indicators into a single consolidated criterion.<n>The theoretical significance of the research lies in the development of a universal multi-criteria optimization tool that is applicable not only in recommendation systems, but also in a wide range of machine learning and data analysis tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The article discusses the concept of hyperparametric optimization of recommendation algorithms using an integral assessment that combines various performance indicators into a single consolidated criterion. This approach is opposed to traditional methods of setting up a single metric and allows you to achieve a balance between accuracy, ranking quality, variety of output and the resource intensity of algorithms. The theoretical significance of the research lies in the development of a universal multi-criteria optimization tool that is applicable not only in recommendation systems, but also in a wide range of machine learning and data analysis tasks.

Related papers

• Principled Algorithms for Optimizing Generalized Metrics in Multi-Label Learning [42.67092904252001]
  We develop principled learning algorithms for optimizing a broad class of generalized metrics within the Empirical Utility Maximization framework.<n>Our key contribution is the design of novel surrogate loss functions for multi-label learning that provable $H$-consistency bounds.<n>Our results offer both theoretical rigor and practical effectiveness for general multi-label metric optimization.
  arXiv  Detail & Related papers  (2026-05-27T17:23:58Z)
• Principled Algorithms for Optimizing Generalized Metrics in Binary Classification [53.604375124674796]
  We introduce principled algorithms for optimizing generalized metrics, supported by $H$-consistency and finite-sample generalization bounds.<n>Our approach reformulates metric optimization as a generalized cost-sensitive learning problem.<n>We develop new algorithms, METRO, with strong theoretical performance guarantees.
  arXiv  Detail & Related papers  (2025-12-29T01:33:42Z)
• Deep Unfolding: Recent Developments, Theory, and Design Guidelines [99.63555420898554]
  This article provides a tutorial-style overview of deep unfolding, a framework that transforms optimization algorithms into structured, trainable ML architectures.<n>We review the foundations of optimization for inference and for learning, introduce four representative design paradigms for deep unfolding, and discuss the distinctive training schemes that arise from their iterative nature.
  arXiv  Detail & Related papers  (2025-12-03T13:16:35Z)
• EVA-MILP: Towards Standardized Evaluation of MILP Instance Generation [13.49043811341421]
  Mixed-Integer Linear Programming (MILP) is fundamental to solving complex decision-making problems.<n>The proliferation of MILP instance generation methods, driven by machine learning's demand for diverse datasets, has significantly outpaced standardized evaluation techniques.<n>This paper introduces a comprehensive benchmark framework designed for the systematic and objective evaluation of MILP instance generation methods.
  arXiv  Detail & Related papers  (2025-05-30T16:42:15Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler [0.7418044931036347]
  We propose a new software tool which uses principles from unbounded archiving as a logging structure.<n>This leads to a clearer separation between experimental design and subsequent analysis decisions.
  arXiv  Detail & Related papers  (2024-12-10T12:00:53Z)
• Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives [17.10165955576643]
  Current state-of-the-art empirical techniques offer sub-optimal performance on practical, non-decomposable performance objectives. We propose SelMix, a selective mixup-based inexpensive fine-tuning technique for pre-trained models. We find that proposed SelMix fine-tuning significantly improves the performance for various practical non-decomposable objectives across benchmarks.
  arXiv  Detail & Related papers  (2024-03-27T06:55:23Z)
• A Hierarchical Federated Learning Approach for the Internet of Things [3.28418927821443]
  We present a novel federated learning solution, QHetFed, suitable for large-scale Internet of Things deployments.<n>We show that QHetFed consistently achieves high learning accuracy and significantly outperforms other hierarchical algorithms.
  arXiv  Detail & Related papers  (2024-03-03T15:40:24Z)
• Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms [13.134564730161983]
  This paper adopts a novel approach to deep learning optimization, focusing on gradient descent (SGD) and its variants. We show that SGD and its variants demonstrate performance on par with flat-minimas like SAM, albeit with half the gradient evaluations. Our study uncovers several key findings regarding the relationship between training loss and hold-out accuracy, as well as the comparable performance of SGD and noise-enabled variants.
  arXiv  Detail & Related papers  (2024-03-01T14:55:22Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [62.997667081978825]
  This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms. We distinguish between metaheuristic-based algorithms, metamodel-based algorithms, and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.
  arXiv  Detail & Related papers  (2021-11-23T10:22:30Z)
• Outlier-Robust Sparse Estimation via Non-Convex Optimization [73.18654719887205]
  We explore the connection between high-dimensional statistics and non-robust optimization in the presence of sparsity constraints. We develop novel and simple optimization formulations for these problems. As a corollary, we obtain that any first-order method that efficiently converges to station yields an efficient algorithm for these tasks.
  arXiv  Detail & Related papers  (2021-09-23T17:38:24Z)
• A Field Guide to Federated Optimization [161.3779046812383]
  Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms.
  arXiv  Detail & Related papers  (2021-07-14T18:09:08Z)
• Bilevel Optimization: Convergence Analysis and Enhanced Design [63.64636047748605]
  Bilevel optimization is a tool for many machine learning problems. We propose a novel stoc-efficientgradient estimator named stoc-BiO.
  arXiv  Detail & Related papers  (2020-10-15T18:09:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.