Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
On the Stability and Generalization of First-order Bilevel Minimax Optimization
Bilevel optimization and bilevel minimax optimization have recently emerged as unifying frameworks for a range of machine-learning tasks, including hyperparameter optimization and reinforcement learning. The existing lit…
Hyperparameter OptimizationReinforcement LearningBilevel OptimizationMobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment
Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present MobileAgeNet, a lightweight age-regression framework that achieves a…
Hyperparameter OptimizationAge EstimationAerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach
To address high data traffic demands of sixth-generation (6G) networks, this paper proposes a novel architecture that integrates autonomous aerial vehicles (AAVs) and multi-functional reconfigurable intelligent surfaces …
Hyperparameter OptimizationReinforcement LearningAIBuildAI: An AI Agent for Automatically Building AI Models
AI models underpin modern intelligent systems, driving advances across science, medicine, finance, and technology. Yet developing high-performing AI models remains a labor-intensive process that requires expert practitio…
Hyperparameter OptimizationExascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data
We present an exascale workflow for materials discovery using atomistic graph foundation models built on HydraGNN. We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) usi…
Hyperparameter OptimizationAutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow
High-fidelity numerical simulation of subsurface flow is computationally intensive, especially for many-query tasks such as uncertainty quantification and data assimilation. Deep learning (DL) surrogates can significantl…
Hyperparameter OptimizationLecture notes on Machine Learning applications for global fits
These lecture notes provide a comprehensive framework for performing global statistical fits in high-energy physics using modern Machine Learning (ML) surrogates. We begin by reviewing the statistical foundations of mode…
Hyperparameter OptimizationGaussian ProcessesActive LearningNon-Destructive Prediction of Fruit Ripeness and Firmness Using Hyperspectral Imaging and Lightweight Machine Learning Models
Post-harvest fruit quality assessment is essential for reducing food waste, yet reliable non-destructive methods typically depend on expensive hyperspectral cameras and computationally intensive deep learning models. The…
Hyperparameter OptimizationFine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization
Stochastic bilevel optimization (SBO) has been integrated into many machine learning paradigms recently, including hyperparameter optimization, meta learning, and reinforcement learning. Along with the wide range of appl…
Hyperparameter OptimizationReinforcement LearningBilevel OptimizationFinding Sets of Pareto Sets in Real-World Scenarios -- A Multitask Multiobjective Perspective
Recently, evolutionary multitasking has been employed to generate a ``set of Pareto sets" (SOS) for machine learning models, addressing diverse task settings across heterogeneous environments. This involves creating a re…
Hyperparameter OptimizationRealistic Market Impact Modeling for Reinforcement Learning Trading Environments
Reinforcement learning (RL) has shown promise for trading, yet most open-source backtesting environments assume negligible or fixed transaction costs, causing agents to learn trading behaviors that fail under realistic e…
Hyperparameter OptimizationReinforcement LearningPortfolio OptimizationGreedy Is a Strong Default: Agents as Iterative Optimizers
Classical optimization algorithms--hill climbing, simulated annealing, population-based methods--generate candidate solutions via random perturbations. We replace the random proposal generator with an LLM agent that reas…
Hyperparameter OptimizationCancer ClassificationSTN-GPR: A Singularity Tensor Network Framework for Efficient Option Pricing
We develop a tensor-network surrogate for option pricing, targeting large-scale portfolio revaluation problems arising in market risk management (e.g., VaR and Expected Shortfall computations). The method involves repres…
Hyperparameter OptimizationMAGNET: Autonomous Expert Model Generation via Decentralized Autoresearch and BitNet Training
We present MAGNET (Model Autonomously Growing Network), a decentralized system for autonomous generation, training, and serving of domain-expert language models across commodity hardware. MAGNET integrates four component…
Hyperparameter OptimizationCan LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
The autoresearch repository enables an LLM agent to optimize hyperparameters by editing training code directly. We use it as a testbed to compare classical HPO algorithms against LLM-based methods on tuning the hyperpara…
Hyperparameter OptimizationSelf-Tuning Sparse Attention: Multi-Fidelity Hyperparameter Optimization for Transformer Acceleration
Sparse attention mechanisms promise to break the quadratic bottleneck of long-context transformers, yet production adoption remains limited by a critical usability gap: optimal hyperparameters vary substantially across l…
Hyperparameter OptimizationAuto-Unrolled Proximal Gradient Descent: An AutoML Approach to Interpretable Waveform Optimization
This study explores the combination of automated machine learning (AutoML) with model-based deep unfolding (DU) for optimizing wireless beamforming and waveforms. We convert the iterative proximal gradient descent (PGD) …
Hyperparameter OptimizationSAMSEM -- A Generic and Scalable Approach for IC Metal Line Segmentation
In light of globalized hardware supply chains, the assurance of hardware components has gained significant interest, particularly in cryptographic applications and high-stakes scenarios. Identifying metal lines on scanni…
Hyperparameter OptimizationCASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters
Rashomon sets are model sets within one model class that perform nearly as well as a reference model from the same model class. They reveal the existence of alternative well-performing models, which may support different…
Hyperparameter OptimizationActive LearningA Systematic Evaluation Protocol of Graph-Derived Signals for Tabular Machine Learning
While graph-derived signals are widely used in tabular learning, existing studies typically rely on limited experimental setups and average performance comparisons, leaving the statistical reliability and robustness of o…
Hyperparameter OptimizationFraud Detection