Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
In recent years, large language models (LLMs) have driven advances in natural language processing. Still, their growing scale has increased the computational burden, necessitating a balance between efficiency and perform…
Bayesian OptimizationLow-rank compressionCMA-ES for Safe Optimization
In several real-world applications in medical and control engineering, there are unsafe solutions whose evaluations involve inherent risk. This optimization setting is known as safe optimization and formulated as a speci…
Bayesian OptimizationCatCMA : Stochastic Optimization for Mixed-Category Problems
Black-box optimization problems often require simultaneously optimizing different types of variables, such as continuous, integer, and categorical variables. Unlike integer variables, categorical variables do not necessa…
Bayesian OptimizationStochastic OptimizationExpensive Multi-Objective Bayesian Optimization Based on Diffusion Models
Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal s…
Bayesian OptimizationAn adaptive approach to Bayesian Optimization with switching costs
We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost. This models…
Bayesian OptimizationExperimental DesignThe DNA of Calabi-Yau Hypersurfaces
We implement Genetic Algorithms for triangulations of four-dimensional reflexive polytopes which induce Calabi-Yau threefold hypersurfaces via Batryev's construction. We demonstrate that such algorithms efficiently optim…
Bayesian OptimizationCAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization
Bayesian optimization (BO) is a popular approach for optimizing expensive-to-evaluate black-box objective functions. An important challenge in BO is its application to high-dimensional search spaces due in large part to …
Bayesian OptimizationReinforcement Learning (RL)Don't Waste Your Time: Early Stopping Cross-Validation
State-of-the-art automated machine learning systems for tabular data often employ cross-validation; ensuring that measured performances generalize to unseen data, or that subsequent ensembling does not overfit. However, …
Bayesian OptimizationModel SelectionBayesian optimization for stable properties amid processing fluctuations in sputter deposition
We introduce a Bayesian optimization approach to guide the sputter deposition of molybdenum thin films, aiming to achieve desired residual stress and sheet resistance while minimizing susceptibility to stochastic fluctua…
Bayesian OptimizationQuality-Weighted Vendi Scores And Their Application To Diverse Experimental Design
Experimental design techniques such as active search and Bayesian optimization are widely used in the natural sciences for data collection and discovery. However, existing techniques tend to favor exploitation over explo…
Bayesian OptimizationDiversityDrug DiscoveryExperimental DesignBayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation
Designing preference elicitation (PE) methodologies that can quickly ascertain a user's top item preferences in a cold-start setting is a key challenge for building effective and personalized conversational recommendatio…
Bayesian OptimizationConversational RecommendationNatural Language InferenceThompson SamplingDynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
We propose a novel algorithm that extends the methods of ball smoothing and Gaussian smoothing for noisy derivative-free optimization by accounting for the heterogeneous curvature of the objective function. The algorithm…
Bayesian OptimizationCombinatorial OptimizationData Augmentation Policy Search for Long-Term Forecasting
Data augmentation serves as a popular regularization technique to combat overfitting challenges in neural networks. While automatic augmentation has demonstrated success in image classification tasks, its application to …
Bayesian OptimizationBilevel OptimizationData Augmentationimage-classification+2Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning
Machine learning algorithms emerge as a promising approach in energy fields, but its practical is hindered by data barriers, stemming from high collection costs and privacy concerns. This study introduces a novel federat…
Bayesian OptimizationBinary ClassificationFederated LearningPrivacy PreservingBayesian-Guided Generation of Synthetic Microbiomes with Minimized Pathogenicity
Synthetic microbiomes offer new possibilities for modulating microbiota, to address the barriers in multidtug resistance (MDR) research. We present a Bayesian optimization approach to enable efficient searching over the …
Bayesian OptimizationThompson SamplingOptimal Initialization of Batch Bayesian Optimization
Field experiments and computer simulations are effective but time-consuming methods of measuring the quality of engineered systems at different settings. To reduce the total time required, experimenters may employ Bayesi…
Bayesian OptimizationA Continuous Relaxation for Discrete Bayesian Optimization
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the objective function and show that inference and…
Bayesian OptimizationEfficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models
Image classifiers should be used with caution in the real world. Performance evaluated on a validation set may not reflect performance in the real world. In particular, classifiers may perform well for conditions that ar…
AttributeBayesian OptimizationBenchmarkingEfficient Exploration+1In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
With the increasing computational costs associated with deep learning, automated hyperparameter optimization methods, strongly relying on black-box Bayesian optimization (BO), face limitations. Freeze-thaw BO offers a pr…
Bayesian OptimizationHyperparameter OptimizationIn-Context LearningEvolve Cost-aware Acquisition Functions Using Large Language Models
Many real-world optimization scenarios involve expensive evaluation with unknown and heterogeneous costs. Cost-aware Bayesian optimization stands out as a prominent solution in addressing these challenges. To approach th…
Bayesian OptimizationDecision Making