Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Active search for Bifurcations
Bifurcations mark qualitative changes of long-term behavior in dynamical systems and can often signal sudden ("hard") transitions or catastrophic events (divergences). Accurately locating them is critical not just for de…
Active LearningBayesian OptimizationUncertainty QuantificationBayesian Intervention Optimization for Causal Discovery
Causal discovery is crucial for understanding complex systems and informing decisions. While observational data can uncover causal relationships under certain assumptions, it often falls short, making active intervention…
Bayesian OptimizationCausal DiscoveryDecision MakingPareto Front-Diverse Batch Multi-Objective Bayesian Optimization
We consider the problem of multi-objective optimization (MOO) of expensive black-box functions with the goal of discovering high-quality and diverse Pareto fronts where we are allowed to evaluate a batch of inputs. This …
Bayesian OptimizationDiversityPoint ProcessesDiagnosing and fixing common problems in Bayesian optimization for molecule design
Bayesian optimization (BO) is a principled approach to molecular design tasks. In this paper we explain three pitfalls of BO which can cause poor empirical performance: an incorrect prior width, over-smoothing, and inade…
Bayesian OptimizationHow Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?
Bayesian optimization (BO) is an integral part of automated scientific discovery -- the so-called self-driving lab -- where human inputs are ideally minimal or at least non-blocking. However, scientists often have strong…
Bayesian OptimizationBlockingscientific discoveryPhysics-Informed Bayesian Optimization of Variational Quantum Circuits
In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs) -- a hybrid quantum-classical protocol used to approximate the ground state of a quantum …
Bayesian OptimizationInductive BiasMulti-Objective Neural Architecture Search for In-Memory Computing
In this work, we employ neural architecture search (NAS) to enhance the efficiency of deploying diverse machine learning (ML) tasks on in-memory computing (IMC) architectures. Initially, we design three fundamental compo…
Bayesian Optimizationimage-classificationImage ClassificationNeural Architecture SearchGenerating Piano Practice Policy with a Gaussian Process
A typical process of learning to play a piece on a piano consists of a progression through a series of practice units that focus on individual dimensions of the skill, the so-called practice modes. Practice modes in lear…
Bayesian OptimizationDevelopment and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study
INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial intelligence (AI) model aiming to personalize treatment and improve outcomes…
Bayesian Optimizationfeature selectionFunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
The sample efficiency of Bayesian optimization algorithms depends on carefully crafted acquisition functions (AFs) guiding the sequential collection of function evaluations. The best-performing AF can vary significantly …
Bayesian Optimizationglobal-optimizationHyperparameter OptimizationTransfer LearningA survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficiency, Bayesian optimization is an ideal c…
Bayesian OptimizationDrug DesignLLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation
Analog layout synthesis faces significant challenges due to its dependence on manual processes, considerable time requirements, and performance instability. Current Bayesian Optimization (BO)-based techniques for analog …
Bayesian OptimizationFew-Shot LearningApproximation-Aware Bayesian Optimization
High-dimensional Bayesian optimization (BO) tasks such as molecular design often require 10,000 function evaluations before obtaining meaningful results. While methods like sparse variational Gaussian processes (SVGPs) r…
Bayesian OptimizationGaussian ProcessesVariational InferenceNo-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints
We consider the problem of sequentially maximizing an unknown function $f$ over a set of actions of the form $(s,\mathbf{x})$, where the selected actions must satisfy a safety constraint with respect to an unknown safety…
Bayesian OptimizationBEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems
Novelty search (NS) refers to a class of exploration algorithms that automatically uncover diverse system behaviors through simulations or experiments. Uncovering diversity is a key aspect of engineering design problems …
Bayesian OptimizationDrug DiscoveryGaussian ProcessesNeural Architecture Search+1Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction
In engineering design, surrogate models are widely employed to replace computationally expensive simulations by leveraging design variables and geometric parameters from computer-aided design (CAD) models. However, these…
Bayesian OptimizationGraph Neural NetworkSystem-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
We consider the problem of optimizing initial conditions and termination time in dynamical systems governed by unknown ordinary differential equations (ODEs), where evaluating different initial conditions is costly and t…
Bayesian OptimizationLogistic Variational Bayes Revisited
Variational logistic regression is a popular method for approximate Bayesian inference seeing wide-spread use in many areas of machine learning including: Bayesian optimization, reinforcement learning and multi-instance …
Bayesian InferenceBayesian OptimizationregressionGated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy
The recent advancement of deep learning architectures, neural networks, and the combination of abundant financial data and powerful computers are transforming finance, leading us to develop an advanced method for predict…
Bayesian Optimizationfeature selectionHyperparameter OptimizationMulti-Objective Neural Architecture Search by Learning Search Space Partitions
Deploying deep learning models requires taking into consideration neural network metrics such as model size, inference latency, and #FLOPs, aside from inference accuracy. This results in deep learning model designers lev…
Bayesian OptimizationGPUNeural Architecture Search