Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting
Forecasting in probabilistic time series is a complex endeavor that extends beyond predicting future values to also quantifying the uncertainty inherent in these predictions. Gaussian process regression stands out as a B…
Bayesian OptimizationTime SeriesTime Series ForecastingGravix: Active Learning for Gravitational Waves Classification Algorithms
This project explores the integration of Bayesian Optimization (BO) algorithms into a base machine learning model, specifically Convolutional Neural Networks (CNNs), for classifying gravitational waves among background n…
Active LearningBayesian OptimizationClassificationHyperparameter OptimizationAn Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management Systems
Complex urban road networks with high vehicle occupancy frequently face severe traffic congestion. Designing an effective strategy for managing multiple traffic lights plays a crucial role in managing congestion. However…
Bayesian OptimizationManagementBeyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation
Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is …
Bayesian OptimizationRecommendation SystemsSequential RecommendationDifferentiating Policies for Non-Myopic Bayesian Optimization
Bayesian optimization (BO) methods choose sample points by optimizing an acquisition function derived from a statistical model of the objective. These acquisition functions are chosen to balance sampling regions with pre…
Bayesian OptimizationDynamic Exclusion of Low-Fidelity Data in Bayesian Optimization for Autonomous Beamline Alignment
Aligning beamlines at synchrotron light sources is a high-dimensional, expensive-to-sample optimization problem, as beams are focused using a series of dynamic optical components. Bayesian Optimization is an efficient ma…
Bayesian OptimizationArtificial Neural Network and Deep Learning: Fundamentals and Theory
"Artificial Neural Network and Deep Learning: Fundamentals and Theory" offers a comprehensive exploration of the foundational principles and advanced methodologies in neural networks and deep learning. This book begins w…
Bayesian OptimizationDeep LearningDescriptiveGaussian Processes+1Generalizing Few Data to Unseen Domains Flexibly Based on Label Smoothing Integrated with Distributionally Robust Optimization
Overfitting commonly occurs when applying deep neural networks (DNNs) on small-scale datasets, where DNNs do not generalize well from existing data to unseen data. The main reason resulting in overfitting is that small-s…
Anomaly ClassificationBayesian OptimizationBoFire: Bayesian Optimization Framework Intended for Real Experiments
Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Previous BO implementations, for example as…
Bayesian OptimizationHigh dimensional Bayesian Optimization via Condensing-Expansion Projection
In high-dimensional settings, Bayesian optimization (BO) can be expensive and infeasible. The random embedding Bayesian optimization algorithm is commonly used to address high-dimensional BO challenges. However, this met…
Bayesian OptimizationModel-Based Transfer Learning for Contextual Reinforcement Learning
Deep reinforcement learning (RL) is a powerful approach to complex decision making. However, one issue that limits its practical application is its brittleness, sometimes failing to train in the presence of small changes…
Bayesian Optimizationcontinuous-controlContinuous ControlDeep Reinforcement Learning+6Maximum a Posteriori Estimation for Linear Structural Dynamics Models Using Bayesian Optimization with Rational Polynomial Chaos Expansions
Bayesian analysis enables combining prior knowledge with measurement data to learn model parameters. Commonly, one resorts to computing the maximum a posteriori (MAP) estimate, when only a point estimate of the parameter…
Bayesian OptimizationExperimental DesignDeep Generative Models for Subgraph Prediction
Graph Neural Networks (GNNs) are important across different domains, such as social network analysis and recommendation systems, due to their ability to model complex relational data. This paper introduces subgraph queri…
Bayesian OptimizationGraph LearningLink PredictionNode Classification+2Few-shot Scooping Under Domain Shift via Simulated Maximal Deployment Gaps
Autonomous lander missions on extraterrestrial bodies need to sample granular materials while coping with domain shifts, even when sampling strategies are extensively tuned on Earth. To tackle this challenge, this paper …
Bayesian OptimizationMeta-LearningSequential Decision MakingProcess-constrained batch Bayesian approaches for yield optimization in multi-reactor systems
The optimization of yields in multi-reactor systems, which are advanced tools in heterogeneous catalysis research, presents a significant challenge due to hierarchical technical constraints. To this respect, this work in…
Bayesian OptimizationThompson SamplingA Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific Fitting
This work presents a novel and effective method for fitting multidimensional ellipsoids to scattered data in the contamination of noise and outliers. We approach the problem as a Bayesian parameter estimate process and m…
3D ReconstructionBayesian OptimizationSurrogate-guided optimization in quantum networks
We propose an optimization algorithm to improve the design and performance of quantum communication networks. When physical architectures become too complex for analytical methods, numerical simulation becomes essential …
Bayesian OptimizationAddressing Out-of-Distribution Challenges in Image Semantic Communication Systems with Multi-modal Large Language Models
Semantic communication is a promising technology for next-generation wireless networks. However, the out-of-distribution (OOD) problem, where a pre-trained machine learning (ML) model is applied to unseen tasks that are …
Bayesian OptimizationImage ReconstructionSemantic CommunicationSemantic CompressionTrading Devil Final: Backdoor attack via Stock market and Bayesian Optimization
Since the advent of generative artificial intelligence, every company and researcher has been rushing to develop their own generative models, whether commercial or not. Given the large number of users of these powerful n…
Automatic Speech RecognitionBackdoor AttackBayesian OptimizationData Poisoning+2Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning
This paper focuses on hyperparameter optimization for autonomous driving strategies based on Reinforcement Learning. We provide a detailed description of training the RL agent in a simulation environment. Subsequently, w…
Autonomous DrivingBayesian Optimizationglobal-optimizationHyperparameter Optimization+2