Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
LLMs as In-Context Meta-Learners for Model and Hyperparameter Selection
Model and hyperparameter selection are critical but challenging in machine learning, typically requiring expert intuition or expensive automated search. We investigate whether large language models (LLMs) can act as in-c…
Hyperparameter OptimizationLocalized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detection
This paper presents Two-Stage LKPLO, a novel multi-stage outlier detection framework that overcomes the coexisting limitations of conventional projection-based methods: their reliance on a fixed statistical metric and th…
Hyperparameter OptimizationOutlier DetectionMethodology for Comparing Machine Learning Algorithms for Survival Analysis
This study presents a comparative methodological analysis of six machine learning models for survival analysis (MLSA). Using data from nearly 45,000 colorectal cancer patients in the Hospital-Based Cancer Registries of S…
Hyperparameter OptimizationDecision MakingBBOPlace-Bench: Benchmarking Black-Box Optimization for Chip Placement
Chip placement is a vital stage in modern chip design as it has a substantial impact on the subsequent processes and the overall quality of the final chip. The use of black-box optimization (BBO) for chip placement has a…
Hyperparameter OptimizationAdaptive Training of INRs via Pruning and Densification
Encoding input coordinates with sinusoidal functions into multilayer perceptrons (MLPs) has proven effective for implicit neural representations (INRs) of low-dimensional signals, enabling the modeling of high-frequency …
Hyperparameter OptimizationCost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning
In this paper, we address the problem of \emph{cost-sensitive} hyperparameter optimization (HPO) built upon freeze-thaw Bayesian optimization (BO). Specifically, we assume a scenario where users want to early-stop the HP…
Hyperparameter OptimizationTransfer LearningEnhancing Diagnostic Accuracy for Urinary Tract Disease through Explainable SHAP-Guided Feature Selection and Classification
In this paper, we propose an approach to support the diagnosis of urinary tract diseases, with a focus on bladder cancer, using SHAP (SHapley Additive exPlanations)-based feature selection to enhance the transparency and…
Hyperparameter OptimizationBinary ClassificationHyperparameter Optimization and Reproducibility in Deep Learning Model Training
Reproducibility remains a critical challenge in foundation model training for histopathology, often hindered by software randomness, hardware non-determinism, and inconsistent hyperparameter reporting. To investigate the…
Hyperparameter OptimizationA Novel Ensemble Learning Approach for Enhanced IoT Attack Detection: Redefining Security Paradigms in Connected Systems
The rapid expansion of Internet of Things (IoT) devices has transformed industries and daily life by enabling widespread connectivity and data exchange. However, this increased interconnection has introduced serious secu…
Hyperparameter OptimizationEnsemble LearningBilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes
Methods for solving scientific computing and inference problems, such as kernel- and neural network-based approaches for partial differential equations (PDEs), inverse problems, and supervised learning tasks, depend cruc…
Hyperparameter OptimizationBilevel OptimizationGaussian ProcessesAdaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches
Gaussian process (GP) regression provides a strategy for accelerating saddle point searches on high-dimensional energy surfaces by reducing the number of times the energy and its derivatives with respect to atomic coordi…
Hyperparameter OptimizationComparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
This study evaluates the performance of Soft Actor Critic (SAC), Greedy Actor Critic (GAC), and Truncated Quantile Critics (TQC) in high-dimensional decision-making tasks using fully observable environments. The focus is…
Hyperparameter OptimizationReinforcement LearningSearching for the Most Human-like Emergent Language
In this paper, we design a signalling game-based emergent communication environment to generate state-of-the-art emergent languages in terms of similarity to human language. This is done with hyperparameter optimization,…
Hyperparameter OptimizationTransfer LearningVarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI
Accounting for inter-individual variability in brain function is key to precision medicine. Here, by considering functional inter-individual variability as meaningful data rather than noise, we introduce VarCoNet, an enh…
Hyperparameter OptimizationContrastive LearningAutomating Data-Driven Modeling and Analysis for Engineering Applications using Large Language Model Agents
Modern engineering increasingly relies on vast datasets generated by experiments and simulations, driving a growing demand for efficient, reliable, and broadly applicable modeling strategies. There is also heightened int…
Hyperparameter OptimizationTuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning
Automatic performance tuning (auto-tuning) is widely used to optimize performance-critical applications across many scientific domains by finding the best program variant among many choices. Efficient optimization algori…
Hyperparameter Optimizationo-MEGA: Optimized Methods for Explanation Generation and Analysis
The proliferation of transformer-based language models has revolutionized NLP domain while simultaneously introduced significant challenges regarding model transparency and trustworthiness. The complexity of achieving ex…
Hyperparameter OptimizationExplanation GenerationExplicit modelling of subject dependency in BCI decoding
Brain-Computer Interfaces (BCIs) suffer from high inter-subject variability and limited labeled data, often requiring lengthy calibration phases. In this work, we present an end-to-end approach that explicitly models the…
Hyperparameter OptimizationBayesian Transfer Operators in Reproducing Kernel Hilbert Spaces
The Koopman operator, as a linear representation of a nonlinear dynamical system, has been attracting attention in many fields of science. Recently, Koopman operator theory has been combined with another concept that is …
Hyperparameter OptimizationAdaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
The efficiency of Bayesian optimization (BO) relies heavily on the choice of the Gaussian process (GP) kernel, which plays a central role in balancing exploration and exploitation under limited evaluation budgets. Tradit…
Hyperparameter Optimization