Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting
Hyperparameter optimization (HPO) plays a central role in the performance of deep learning models, yet remains computationally expensive and difficult to interpret, particularly for time-series forecasting. While Bayesia…
Multivariate Time Series ForecastingHyperparameter OptimizationProactive SFC Provisioning with Forecast-Driven DRL in Data Centers
Service Function Chaining (SFC) requires efficient placement of Virtual Network Functions (VNFs) to satisfy diverse service requirements while maintaining high resource utilization in Data Centers (DCs). Conventional sta…
Hyperparameter OptimizationReinforcement LearningGraph Neural NetworkGEGO: A Hybrid Golden Eagle and Genetic Optimization Algorithm for Efficient Hyperparameter Tuning in Resource-Constrained Environments
Hyperparameter tuning is a critical yet computationally expensive step in training neural networks, particularly when the search space is high dimensional and nonconvex. Metaheuristic optimization algorithms are often us…
Hyperparameter OptimizationNon-Stationary Functional Bilevel Optimization
Functional bilevel optimization (FBO) provides a powerful framework for hierarchical learning in function spaces, yet current methods are limited to static offline settings and perform suboptimally in online, non-station…
Hyperparameter OptimizationReinforcement LearningBilevel OptimizationGeneralizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images
Deep learning for cancer histopathology training conflicts with privacy constraints in clinical settings. Federated Learning (FL) mitigates this by keeping data local; however, its performance depends on hyperparameter c…
Hyperparameter OptimizationFederated LearningSpeculative Sampling with Reinforcement Learning
Inference time latency has remained an open challenge for real world applications of large language models (LLMs). State-of-the-art (SOTA) speculative sampling (SpS) methods for LLMs, like EAGLE-3, use tree-based draftin…
Hyperparameter OptimizationReinforcement LearningHyperparameter Optimization of Constraint Programming Solvers
The performance of constraint programming solvers is highly sensitive to the choice of their hyperparameters. Manually finding the best solver configuration is a difficult, time-consuming task that typically requires exp…
Hyperparameter OptimizationAdvancing Model Refinement: Muon-Optimized Distillation and Quantization for LLM Deployment
Large Language Models (LLMs) enable advanced natural language processing but face deployment challenges on resource-constrained edge devices due to high computational, memory, and energy demands. Optimizing these models …
Hyperparameter OptimizationKnowledge DistillationXGBoost Forecasting of NEPSE Index Log Returns with Walk Forward Validation
This study develops a robust machine learning framework for one-step-ahead forecasting of daily log-returns in the Nepal Stock Exchange (NEPSE) Index using the XGBoost regressor. A comprehensive feature set is engineered…
Hyperparameter OptimizationFeature ImportanceCommunity-Based Model Sharing and Generalisation: Anomaly Detection in IoT Temperature Sensor Networks
The rapid deployment of Internet of Things (IoT) devices has led to large-scale sensor networks that monitor environmental and urban phenomena in real time. Communities of Interest (CoIs) provide a promising paradigm for…
Hyperparameter OptimizationAnomaly DetectionHow Much Can a Few Engine Moves Help? Quantifying Limited Cheating in Chess
Cheating in chess, by using advice from powerful software, has become a major problem, reaching the highest levels. As opposed to the large majority of previous work, which concerned {\em detection} of cheating, here we …
Hyperparameter OptimizationLatent Geometry of Taste: Scalable Low-Rank Matrix Factorization for Recommender Systems
Scalability and data sparsity remain critical bottlenecks for collaborative filtering on massive interaction datasets. This work investigates the latent geometry of user preferences using the MovieLens 32M dataset, imple…
Hyperparameter OptimizationCollaborative FilteringComparative Efficiency Analysis of Lightweight Transformer Models: A Multi-Domain Empirical Benchmark for Enterprise NLP Deployment
In the rapidly evolving landscape of enterprise natural language processing (NLP), the demand for efficient, lightweight models capable of handling multi-domain text automation tasks has intensified. This study conducts …
Hyperparameter OptimizationHate Speech DetectionCan Small Training Runs Reliably Guide Data Curation? Rethinking Proxy-Model Practice
Data teams at frontier AI companies routinely train small proxy models to make critical decisions about pretraining data recipes for full-scale training runs. However, the community has a limited understanding of whether…
Hyperparameter OptimizationMechanical Strength Prediction of Steel-Polypropylene Fiber-based High-Performance Concrete Using Hybrid Machine Learning Algorithms
This research develops and evaluates machine learning models to predict the mechanical properties of steel-polypropylene fiber-reinforced high-performance concrete (HPC). Three model families were investigated: Extra Tre…
Hyperparameter OptimizationAtomistic Simulation Guided Convolutional Neural Networks for Thermal Modeling of Friction Stir Welding
Accurate prediction of temperature evolution is essential for understanding thermomechanical behavior in friction stir welding. In this study, molecular dynamics simulations were performed using LAMMPS to model aluminum …
Hyperparameter OptimizationAuto-Tuning Safety Guardrails for Black-Box Large Language Models
Large language models (LLMs) are increasingly deployed behind safety guardrails such as system prompts and content filters, especially in settings where product teams cannot modify model weights. In practice these guardr…
Hyperparameter OptimizationSoil Compaction Parameters Prediction Based on Automated Machine Learning Approach
Soil compaction is critical in construction engineering to ensure the stability of structures like road embankments and earth dams. Traditional methods for determining optimum moisture content (OMC) and maximum dry densi…
Hyperparameter OptimizationDeep Reinforcement Learning for Dynamic Algorithm Configuration: A Case Study on Optimizing OneMax with the (1+($λ$,$λ$))-GA
Dynamic Algorithm Configuration (DAC) studies the efficient identification of control policies for parameterized optimization algorithms. Numerous studies leverage Reinforcement Learning (RL) to address DAC challenges; h…
Hyperparameter OptimizationReinforcement LearningEfficient Hyperparameter Search for Non-Stationary Model Training
Online learning is the cornerstone of applications like recommendation and advertising systems, where models continuously adapt to shifting data distributions. Model training for such systems is remarkably expensive, a c…
Hyperparameter Optimization