Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Predicting Cascading Failures in Power Systems using Machine Learning
Cascading failure studies help assess and enhance the robustness of power systems against severe power outages. Onset time is a critical parameter in the analysis and management of power system stability and reliability,…
Bayesian OptimizationManagementMulti-class ClassificationTuning-Free Structured Sparse PCA via Deep Unfolding Networks
Sparse principal component analysis (PCA) is a well-established dimensionality reduction technique that is often used for unsupervised feature selection (UFS). However, determining the regularization parameters is rather…
Bayesian OptimizationDimensionality Reductionfeature selectionChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model
Goal-oriented de novo molecule design, namely generating molecules with specific property or substructure constraints, is a crucial yet challenging task in drug discovery. Existing methods, such as Bayesian optimization …
Bayesian OptimizationDrug DiscoveryLanguage ModelingLanguage Modelling+3Bayesian Optimization for Controlled Image Editing via LLMs
In the rapidly evolving field of image generation, achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and th…
Bayesian OptimizationImage GenerationFaster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems
While Retrieval Augmented Generation (RAG) has emerged as a popular technique for improving Large Language Model (LLM) systems, it introduces a large number of choices, parameters and hyperparameters that must be made or…
Bayesian OptimizationHyperparameter OptimizationLanguage ModelingLanguage Modelling+3Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
Optimizing high-dimensional and complex black-box functions is crucial in numerous scientific applications. While Bayesian optimization (BO) is a powerful method for sample-efficient optimization, it struggles with the c…
Bayesian OptimizationUncertainty QuantificationAn Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces
We develop a form Thompson sampling for online learning under full feedback - also known as prediction with expert advice - where the learner's prior is defined over the space of an adversary's future actions, rather tha…
Bayesian OptimizationThompson SamplingMaximizing Spectrum Efficiency of Data-Carrying Reference Signals via Bayesian Optimization
Data-carrying reference signals are a type of reference signal (RS) constructed on the Grassmann manifold, which allows for simultaneous data transmission and channel estimation to achieve boosted spectral efficiency at …
Bayesian OptimizationMulti-Objective Causal Bayesian Optimization
In decision-making problems, the outcome of an intervention often depends on the causal relationships between system components and is highly costly to evaluate. In such settings, causal Bayesian optimization (CBO) can e…
Bayesian OptimizationDecision MakingMulti-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs
Designing modern industrial systems requires balancing several competing objectives, such as profitability, resilience, and sustainability, while accounting for complex interactions between technological, economic, and e…
Bayesian OptimizationNavigateUniversal Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery
We present a framework for generating universal semantic embeddings of chemical elements to advance materials inference and discovery. This framework leverages ElementBERT, a domain-specific BERT-based natural language p…
Bayesian OptimizationPushing the Limits of the Reactive Affine Shaker Algorithm to Higher Dimensions
Bayesian Optimization (BO) for the minimization of expensive functions of continuous variables uses all the knowledge acquired from previous samples (${\boldsymbol x}_i$ and $f({\boldsymbol x}_i)$ values) to build a surr…
Bayesian OptimizationGaussian ProcessesUncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization
Recommender systems (RSs) play a crucial role in shaping our digital interactions, influencing how we access and engage with information across various domains. Traditional research has predominantly centered on maximizi…
Autonomous DrivingBayesian OptimizationFairnessMeta-Learning+2Assessing the impacts of tradable credit schemes through agent-based simulation
Tradable credit schemes (TCS) have been attracting interest from the transportation research community as an appealing alternative to congestion pricing, due to the advantages of revenue neutrality and equity. Nonetheles…
Bayesian OptimizationEnhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective
Offline model-based reinforcement learning (MBRL) serves as a competitive framework that can learn well-performing policies solely from pre-collected data with the help of learned dynamics models. To fully unleash the po…
Bayesian OptimizationmodelModel-based Reinforcement LearningModel Selection+2Adaptive Multi-Objective Bayesian Optimization for Capacity Planning of Hybrid Heat Sources in Electric-Heat Coupling Systems of Cold Regions
The traditional heat-load generation pattern of combined heat and power generators has become a problem leading to renewable energy source (RES) power curtailment in cold regions, motivating the proposal of a planning mo…
Bayesian OptimizationUnderstanding High-Dimensional Bayesian Optimization
Recent work reported that simple Bayesian optimization methods perform well for high-dimensional real-world tasks, seemingly contradicting prior work and tribal knowledge. This paper investigates the 'why'. We identify f…
Bayesian OptimizationBayesian Optimization for Simultaneous Selection of Machine Learning Algorithms and Hyperparameters on Shared Latent Space
Selecting the optimal combination of a machine learning (ML) algorithm and its hyper-parameters is crucial for the development of high-performance ML systems. However, since the combination of ML algorithms and hyper-par…
Bayesian OptimizationA Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit
In this paper, we propose an optimized Transformer model that integrates Bayesian algorithms with a Bidirectional Gated Recurrent Unit (BiGRU), and apply it to fake news classification for the first time. First, we emplo…
Bayesian OptimizationFake News DetectionNews ClassificationBayesian Optimization for Building Social-Influence-Free Consensus
We introduce Social Bayesian Optimization (SBO), a vote-efficient algorithm for consensus-building in collective decision-making. In contrast to single-agent scenarios, collective decision-making encompasses group dynami…
Bayesian OptimizationDecision Makingenergy trading