Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Bayesian Optimization by Kernel Regression and Density-based Exploration
Bayesian optimization is highly effective for optimizing expensive-to-evaluate black-box functions, but it faces significant computational challenges due to the high computational complexity of Gaussian processes, which …
Bayesian OptimizationComputational EfficiencyGaussian ProcessesregressionUser-Preference Meets Pareto-Optimality: Multi-Objective Bayesian Optimization with Local Gradient Search
Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the optimization procedure. Preferences are often abstracted in the form of an unknown utility function, esti…
Bayesian OptimizationMetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep Learning Acceleration
This paper presents a unified framework for codifying and automating optimization strategies to efficiently deploy deep neural networks (DNNs) on resource-constrained hardware, such as FPGAs, while maintaining high perfo…
Bayesian OptimizationHigh-Level SynthesisTowards Autonomous Experimentation: Bayesian Optimization over Problem Formulation Space for Accelerated Alloy Development
Accelerated discovery in materials science demands autonomous systems capable of dynamically formulating and solving design problems. In this work, we introduce a novel framework that leverages Bayesian optimization over…
AttributeBayesian OptimizationScalable Differentially Private Bayesian Optimization
In recent years, there has been much work on scaling Bayesian Optimization to high-dimensional problems, for example hyperparameter tuning in large machine learning models. These scalable methods have been successful, fi…
Bayesian OptimizationProbabilistic Artificial Intelligence
Artificial intelligence commonly refers to the science and engineering of artificial systems that can carry out tasks generally associated with requiring aspects of human intelligence, such as playing games, translating …
Active LearningBayesian OptimizationBILBO: BILevel Bayesian Optimization
Bilevel optimization is characterized by a two-level optimization structure, where the upper-level problem is constrained by optimal lower-level solutions, and such structures are prevalent in real-world problems. The co…
Bayesian OptimizationBilevel OptimizationLLM-USO: Large Language Model-based Universal Sizing Optimizer
The design of analog circuits is a cornerstone of integrated circuit (IC) development, requiring the optimization of complex, interconnected sub-structures such as amplifiers, comparators, and buffers. Traditionally, thi…
Bayesian OptimizationLanguage ModelingLanguage ModellingLarge Language Model+1Honegumi: An Interface for Accelerating the Adoption of Bayesian Optimization in the Experimental Sciences
Bayesian optimization (BO) has emerged as a powerful tool for guiding experimental design and decision- making in various scientific fields, including materials science, chemistry, and biology. However, despite its growi…
Bayesian OptimizationExperimental DesignMeta-Prompt Optimization for LLM-Based Sequential Decision Making
Large language models (LLMs) have recently been employed as agents to solve sequential decision-making tasks such as Bayesian optimization and multi-armed bandits (MAB). These works usually adopt an LLM for sequential ac…
Bayesian OptimizationDecision MakingMulti-Armed BanditsSequential Decision MakingDUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks
The performance of an LLM depends heavily on the relevance of its training data to the downstream evaluation task. However, in practice, the data involved in an unseen evaluation task is often unknown (e.g., conversation…
Bayesian OptimizationCovering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization
In multi-objective black-box optimization, the goal is typically to find solutions that optimize a set of $T$ black-box objective functions, $f_1$, ..., $f_T$, simultaneously. Traditional approaches often seek a single P…
Bayesian OptimizationDrug DesignDrug DiscoveryPareto-frontier Entropy Search with Variational Lower Bound Maximization
This study considers multi-objective Bayesian optimization (MOBO) through the information gain of the Pareto-frontier. To calculate the information gain, a predictive distribution conditioned on the Pareto-frontier plays…
Bayesian OptimizationOptimizing Through Change: Bounds and Recommendations for Time-Varying Bayesian Optimization Algorithms
Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying, expensive, noisy black-box function. However, most of the solutions proposed so far either rely on unrealistic assumptions o…
Bayesian OptimizationBayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble
Many real-world black-box optimization problems have multiple conflicting objectives. Rather than attempting to approximate the entire set of Pareto-optimal solutions, interactive preference learning allows to focus the …
Bayesian OptimizationA Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization
Bayesian optimization is a widely used method for optimizing expensive black-box functions, with Expected Improvement being one of the most commonly used acquisition functions. In contrast, information-theoretic acquisit…
Bayesian OptimizationVariational InferenceMachine-Learning-Enhanced Optimization of Noise-Resilient Variational Quantum Eigensolvers
Variational Quantum Eigensolvers (VQEs) are a powerful class of hybrid quantum-classical algorithms designed to approximate the ground state of a quantum system described by its Hamiltonian. VQEs hold promise for various…
Bayesian OptimizationGaussian ProcessesA finite element-based machine learning model for hydro-mechanical analysis of swelling behavior in clay-sulfate rocks
The hydro-mechanical behavior of clay-sulfate rocks, especially their swelling properties, poses significant challenges in geotechnical engineering. This study presents a hybrid constrained machine learning (ML) model de…
Bayesian OptimizationCONELPABO: composite networks learning via parallel Bayesian optimization to predict remaining useful life in predictive maintenance
Maintaining equipment and machinery in industries is imperative for maximizing operational efficiency and prolonging their lifespan. The adoption of predictive maintenance enhances resource allocation, productivity, and …
Bayesian OptimizationTime SeriesB-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM Pruning
Face detection is a computer vision application that increasingly demands lightweight models to facilitate deployment on devices with limited computational resources. Neural network pruning is a promising technique that …
Bayesian OptimizationFace DetectionNetwork Pruning