Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation
This research presents a robust approach to classifying COVID-19 cough sounds using cutting-edge machine-learning techniques. Leveraging deep neural decision trees and deep neural decision forests, our methodology demons…
Bayesian OptimizationOn the Implementation of a Bayesian Optimization Framework for Interconnected Systems
Bayesian optimization (BO) is an effective paradigm for the optimization of expensive-to-sample systems. Standard BO learns the performance of a system $f(x)$ by using a Gaussian Process (GP) model; this treats the syste…
Bayesian OptimizationChemical ProcessPredicting Barge Presence and Quantity on Inland Waterways using Vessel Tracking Data: A Machine Learning Approach
This study presents a machine learning approach to predict the number of barges transported by vessels on inland waterways using tracking data from the Automatic Identification System (AIS). While AIS tracks the location…
Bayesian OptimizationScalable Bayesian Optimization via Focalized Sparse Gaussian Processes
Bayesian optimization is an effective technique for black-box optimization, but its applicability is typically limited to low-dimensional and small-budget problems due to the cubic complexity of computing the Gaussian pr…
Bayesian OptimizationGaussian ProcessesSafe Bayesian Optimization for the Control of High-Dimensional Embodied Systems
Learning to move is a primary goal for animals and robots, where ensuring safety is often important when optimizing control policies on the embodied systems. For complex tasks such as the control of human or humanoid con…
Bayesian OptimizationHumanoid ControlSafe ExplorationOn Improved Regret Bounds In Bayesian Optimization with Gaussian Noise
Bayesian optimization (BO) with Gaussian process (GP) surrogate models is a powerful black-box optimization method. Acquisition functions are a critical part of a BO algorithm as they determine how the new samples are se…
Bayesian OptimizationThompson SamplingAutomated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries
Combinatorial libraries are a powerful approach for exploring the evolution of physical properties across binary and ternary cross-sections in multicomponent phase diagrams. Although the synthesis of these libraries has …
Bayesian OptimizationBayesian Optimization of Bilevel Problems
Bilevel optimization, a hierarchical mathematical framework where one optimization problem is nested within another, has emerged as a powerful tool for modeling complex decision-making processes in various fields such as…
Bayesian OptimizationBilevel OptimizationDecision MakingGaussian Processes+1HyperQ-Opt: Q-learning for Hyperparameter Optimization
Hyperparameter optimization (HPO) is critical for enhancing the performance of machine learning models, yet it often involves a computationally intensive search across a large parameter space. Traditional approaches such…
Bayesian OptimizationHyperparameter OptimizationQ-Learningreinforcement-learning+2Tiered Acquisition for Constrained Bayesian Optimization: An Application to Analog Circuits
Analog circuit design can be considered as an optimization problem with the targeted circuit specifications as constraints. When stringent circuit specifications are considered, it is desired to have an optimization meth…
Bayesian OptimizationUnsupervised learning of spatially varying regularization for diffeomorphic image registration
Spatially varying regularization accommodates the deformation variations that may be necessary for different anatomical regions during deformable image registration. Historically, optimization-based registration models h…
Bayesian OptimizationImage RegistrationTrading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning
With the rapid development of generative artificial intelligence, particularly large language models a number of sub-fields of deep learning have made significant progress and are now very useful in everyday applications…
Backdoor AttackBayesian OptimizationData Poisoningreinforcement-learning+3Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models
Jailbreaking in Large Language Models (LLMs) is a major security concern as it can deceive LLMs to generate harmful text. Yet, there is still insufficient understanding of how jailbreaking works, which makes it hard to d…
Bayesian OptimizationHigh-Dimensional Bayesian Optimization via Random Projection of Manifold Subspaces
Bayesian Optimization (BO) is a popular approach to optimizing expensive-to-evaluate black-box functions. Despite the success of BO, its performance may decrease exponentially as the dimensionality increases. A common fr…
Bayesian OptimizationRepresentation LearningBayesian Optimization for Unknown Cost-Varying Variable Subsets with No-Regret Costs
Bayesian Optimization (BO) is a widely-used method for optimizing expensive-to-evaluate black-box functions. Traditional BO assumes that the learner has full control over all query variables without additional constraint…
Bayesian OptimizationA Comprehensive Forecasting Framework based on Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment
Ads demand forecasting for Walmart's ad products plays a critical role in enabling effective resource planning, allocation, and management of ads performance. In this paper, we introduce a comprehensive demand forecastin…
Bayesian OptimizationDemand ForecastingTime SeriesTime Series ForecastingEfficient Language-instructed Skill Acquisition via Reward-Policy Co-Evolution
The ability to autonomously explore and resolve tasks with minimal human guidance is crucial for the self-development of embodied intelligence. Although reinforcement learning methods can largely ease human effort, it's …
Bayesian OptimizationIndirect Query Bayesian Optimization with Integrated Feedback
We develop the framework of Indirect Query Bayesian Optimization (IQBO), a new class of Bayesian optimization problems where the integrated feedback is given via a conditional expectation of the unknown function $f$ to b…
Bayesian OptimizationComputational EfficiencyBOIDS: High-dimensional Bayesian Optimization via Incumbent-guided Direction Lines and Subspace Embeddings
When it comes to expensive black-box optimization problems, Bayesian Optimization (BO) is a well-known and powerful solution. Many real-world applications involve a large number of dimensions, hence scaling BO to high di…
Bayesian OptimizationQPruner: Probabilistic Decision Quantization for Structured Pruning in Large Language Models
The rise of large language models (LLMs) has significantly advanced various natural language processing (NLP) tasks. However, the resource demands of these models pose substantial challenges. Structured pruning is an eff…
Bayesian OptimizationQuantization