A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

Abstract

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences.

References in corpus (3)

• PEGASUS: A Policy Search Method for Large MDPs and POMDPs
• Portfolio Allocation for Bayesian Optimization
• Convergence properties of the expected improvement algorithm

Cited by in corpus (395)

• Practical Bayesian Optimization of Machine Learning Algorithms
• Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
• Robots that can adapt like animals
• Gaussian Processes for Data-Efficient Learning in Robotics and Control
• Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data
• Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
• Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach
• Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
• A Tutorial on Bayesian Optimization
• Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets
• Convergence rates of efficient global optimization algorithms
• Bayesian Optimization with Unknown Constraints
• Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection
• Neural Architecture Search with Bayesian Optimisation and Optimal Transport
• BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
• Bayesian optimization for materials design
• Assessment of supervised machine learning methods for fluid flows
• Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
• Training of Quantum Circuits on a Hybrid Quantum Computer
• Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization
• Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids
• Probabilistic neural networks for fluid flow surrogate modeling and data recovery
• Learning to Optimize
• Bayesian optimization of a free-electron laser
• A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic
• A Framework to Integrate Mode Choice in the Design of Mobility-on-Demand Systems
• Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
• Hyperparameter optimization with approximate gradient
• Student-t Processes as Alternatives to Gaussian Processes
• High Dimensional Bayesian Optimisation and Bandits via Additive Models
• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
• An Efficient Batch Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-objective Acquisition Ensemble
• A new Taxonomy of Continuous Global Optimization Algorithms
• Variance Reduction for Faster Non-Convex Optimization
• Freeze-Thaw Bayesian Optimization
• Discovery of Self-Assembling $\pi$-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation
• Representation Learning: A Review and New Perspectives
• Automated Machine Learning in Practice: State of the Art and Recent Results
• Experimental velocity data estimation for imperfect particle images using machine learning
• GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
• Multi-fidelity Bayesian Optimisation with Continuous Approximations
• The Automatic Neuroscientist: automated experimental design with real-time fMRI
• Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
• Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
• Portfolio Allocation for Bayesian Optimization
• Adaptation Strategies for Automated Machine Learning on Evolving Data
• Sequential Gallery for Interactive Visual Design Optimization
• Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
• Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
• On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
• Neural-net-induced Gaussian process regression for function approximation and PDE solution
• Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
• Bayesian Optimization in AlphaGo
• Escaping local minima with derivative-free methods: a numerical investigation
• Bayesian optimisation for likelihood-free cosmological inference
• Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning
• Bayesian Optimization of Combinatorial Structures
• Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors
• Differential Evolution and Bayesian Optimisation for Hyper-Parameter Selection in Mixed-Signal Neuromorphic Circuits Applied to UAV Obstacle Avoidance
• pySOT and POAP: An event-driven asynchronous framework for surrogate optimization
• Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization
• Bayesian emulator optimisation for cosmology: application to the Lyman-alpha forest
• Bayesian Multi-Scale Optimistic Optimization
• ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
• One button machine for automating feature engineering in relational databases
• Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
• Bayesian Optimization with Output-Weighted Optimal Sampling
• GLASSES: Relieving The Myopia Of Bayesian Optimisation
• Hybrid Batch Bayesian Optimization
• ENTMOOT: A Framework for Optimization over Ensemble Tree Models
• Natural Language Understanding with Distributed Representation
• Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
• Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations
• New Paradigms for Exploiting Parallel Experiments in Bayesian Optimization
• Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination
• Inferring unknown unknowns: Regularized bias-aware ensemble Kalman filter
• Differentiable Compositional Kernel Learning for Gaussian Processes
• General framework for cosmological dark matter bounds using $N$-body simulations
• Bayesian Optimization for Synthetic Gene Design
• An Entropy Search Portfolio for Bayesian Optimization
• Task-Based Analog-to-Digital Converters
• Automatic Selection of t-SNE Perplexity
• Bayesian Optimization with Adaptive Kernels for Robot Control
• Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters
• Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
• A Population-based Hybrid Approach to Hyperparameter Optimization for Neural Networks
• Time-Varying Gaussian Process Bandit Optimization
• Pre-trained Language Models in Biomedical Domain: A Systematic Survey
• Active Domain Randomization
• Hyperparameters Optimization in Deep Convolutional Neural Network / Bayesian Approach with Gaussian Process Prior
• Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search
• Joint Optimization and Variable Selection of High-dimensional Gaussian Processes
• Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA
• Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization
• Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
• Variational Bayesian Monte Carlo with Noisy Likelihoods
• Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol
• FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
• Active Learning of Linear Embeddings for Gaussian Processes
• High Speed Human Action Recognition using a Photonic Reservoir Computer
• Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles
• Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning
• Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers
• Implicit differentiation of Lasso-type models for hyperparameter optimization
• A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling
• On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials
• Heteroscedastic Treed Bayesian Optimisation
• On the discriminative power of Hyper-parameters in Cross-Validation and how to choose them
• MPC Controller Tuning using Bayesian Optimization Techniques
• Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks
• Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion
• Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization
• Learning to Warm-Start Bayesian Hyperparameter Optimization
• ContentWise Impressions: An Industrial Dataset with Impressions Included
• The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
• Cross-validation based adaptive sampling for Gaussian process models
• High-performance designs for fiber-pigtailed quantum-light sources based on quantum dots in electrically-controlled circular Bragg gratings
• c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization
• A Programmable Approach to Neural Network Compression
• Automatic Document Image Binarization using Bayesian Optimization
• Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
• Machine Learning based optimization for interval uncertainty propagation
• Characterizing Bias in Classifiers using Generative Models
• Inference of Regulatory Networks Through Temporally Sparse Data
• Structure Prediction of Epitaxial Inorganic Interfaces by Lattice and Surface Matching with Ogre
• Randomized Truncated SVD Levenberg-Marquardt Approach to Geothermal Natural State and History Matching
• Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
• Identifying Bias in AI using Simulation
• Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
• Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels
• Meta-Learning for Stochastic Gradient MCMC
• Deep Task-Based Analog-to-Digital Conversion
• Neural Architecture Generator Optimization
• Optimization of the Shape of a Hydrokinetic Turbine's Draft Tube and Hub Assembly Using Design-by-Morphing with Bayesian Optimization
• Suggesting Cooking Recipes Through Simulation and Bayesian Optimization
• Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
• A primer on model-guided exploration of fitness landscapes for biological sequence design
• What do you Mean? The Role of the Mean Function in Bayesian Optimisation
• Adaptive Nonparametric Psychophysics
• Bayesian Optimization in a Billion Dimensions via Random Embeddings
• Scalable Exact Inference in Multi-Output Gaussian Processes
• Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization
• Deep Photonic Reservoir Computer for Speech Recognition
• Machine Learning Accelerated Likelihood-Free Event Reconstruction in Dark Matter Direct Detection
• Mondrian Forests for Large-Scale Regression when Uncertainty Matters
• Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
• On Optimal Generalizability in Parametric Learning
• Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits
• Learning Directed Locomotion in Modular Robots with Evolvable Morphologies
• Counterdiabatic optimized driving in quantum phase sensitive models
• Neural-BO: A Black-box Optimization Algorithm using Deep Neural Networks
• Limbo: A Fast and Flexible Library for Bayesian Optimization
• Query-limited Black-box Attacks to Classifiers
• Apollo: Transferable Architecture Exploration
• Finding safe 3D robot grasps through efficient haptic exploration with unscented Bayesian optimization and collision penalty
• A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
• On Local Optimizers of Acquisition Functions in Bayesian Optimization
• Bayesian optimization of hyper-parameters in reservoir computing
• A multifidelity Bayesian optimization method for inertial confinement fusion design
• Particle filter-based Gaussian process optimisation for parameter inference
• Fast Efficient Hyperparameter Tuning for Policy Gradients
• Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design
• Black-Box Optimization in Machine Learning with Trust Region Based Derivative Free Algorithm
• Bayesian Optimisation over Multiple Continuous and Categorical Inputs
• Big Learning with Bayesian Methods
• Inverse Reinforcement Learning from Summary Data
• Implementation of Deep Convolutional Neural Network in Multi-class Categorical Image Classification
• Robust Bayesian Target Value Optimization
• High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
• Knowing The What But Not The Where in Bayesian Optimization
• Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm
• Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
• Collaborative Distributed Machine Learning
• Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization
• BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture
• Multi-objective multi-generation Gaussian process optimizer for design optimization
• Designing deep neural networks for driver intention recognition
• Open-Category Classification by Adversarial Sample Generation
• Kernel Recursive ABC: Point Estimation with Intractable Likelihood
• Kalman Filter Tuning with Bayesian Optimization
• Region of Attraction for Power Systems using Gaussian Process and Converse Lyapunov Function -- Part I: Theoretical Framework and Off-line Study
• Bayesian Optimization for Dynamic Problems
• Neural Feature Learning From Relational Database
• Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator
• Faster variational quantum algorithms with quantum kernel-based surrogate models
• Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
• Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
• Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters
• Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization
• Inverse Bayesian Optimization: Learning Human Acquisition Functions in an Exploration vs Exploitation Search Task
• Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
• ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning
• Automatic Model Selection for Neural Networks
• Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation
• Multi-objective Bayesian optimisation with preferences over objectives
• Tuneful: An Online Significance-Aware Configuration Tuner for Big Data Analytics
• UAVs using Bayesian Optimization to Locate WiFi Devices
• Attacking Vision-based Perception in End-to-End Autonomous Driving Models
• Bayesian Optimization for Selecting Efficient Machine Learning Models
• Bootstrapping Neural Processes
• Cost-aware Multi-objective Bayesian optimisation
• FLASH: Fast Bayesian Optimization for Data Analytic Pipelines
• Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
• Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations
• Global Continuous Optimization with Error Bound and Fast Convergence
• Learned Hardware/Software Co-Design of Neural Accelerators
• Better safe than sorry: Risky function exploitation through safe optimization
• How Bayesian Should Bayesian Optimisation Be?
• PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization
• Optimised one-class classification performance
• Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
• Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction
• Hyper-parameter estimation method with particle swarm optimization
• Combinatorial 3D Shape Generation via Sequential Assembly
• Variational Adaptive-Newton Method for Explorative Learning
• Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM
• A Nonparametric Unsupervised Learning Approach for Structural Damage Detection
• Using Bayesian Optimization to Accelerate Virtual Screening for the Discovery of Therapeutics Appropriate for Repurposing for COVID-19
• Learning where to Attend with Deep Architectures for Image Tracking
• A New Knowledge Gradient-based Method for Constrained Bayesian Optimization
• Efficient batch-sequential Bayesian optimization with moments of truncated Gaussian vectors
• Active pooling design in group testing based on Bayesian posterior prediction
• Dynamic Batch Bayesian Optimization
• Bayesian Active Learning for Structured Output Design
• Preference-based MPC calibration
• Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling
• Benchmark of Data Preprocessing Methods for Imbalanced Classification
• Neuroadaptive electroencephalography: a proof-of-principle study in infants
• Adaptive and Resilient Soft Tensegrity Robots
• Improving Oil Slick Trajectory Simulations with Bayesian Optimization
• Approximating meta-heuristics with homotopic recurrent neural networks
• Democratizing Production-Scale Distributed Deep Learning
• Active Learning for Matching Problems
• Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology
• Multipoint-BAX: A New Approach for Efficiently Tuning Particle Accelerator Emittance via Virtual Objectives
• Efficient Cysteine Conformer Search with Bayesian Optimization
• No Free Lunch Theorem and Bayesian probability theory: two sides of the same coin. Some implications for black-box optimization and metaheuristics
• Faster Improvement Rate Population Based Training
• Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization
• Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes
• Bayesian Optimization Algorithms for Accelerator Physics
• Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes
• Towards semi-episodic learning for robot damage recovery
• Bayesian Optimization with Directionally Constrained Search
• Regret Bounds for Deterministic Gaussian Process Bandits
• Towards Automatic Bayesian Optimization: A first step involving acquisition functions
• Modeling an Augmented Lagrangian for Blackbox Constrained Optimization
• Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection
• Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm
• Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
• No-regret Algorithms for Multi-task Bayesian Optimization
• AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning
• Active Contextual Entropy Search
• Supervised Domain Adaptation using Graph Embedding
• Practical Batch Bayesian Optimization for Less Expensive Functions
• Combination of multiple Deep Learning architectures for Offensive Language Detection in Tweets
• Active preference learning based on radial basis functions
• Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization
• Piecewise linear regression and classification
• Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
• Optimization of process parameters in additive manufacturing based on the finite element method
• Bayesian optimization for robust robotic grasping using a sensorized compliant hand
• Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning
• Jet energy calibration with deep learning as a Kubeflow pipeline
• Bayesian Optimisation for Machine Translation
• No-Regret Bayesian Optimization with Unknown Hyperparameters
• Bayesian Optimization for Iterative Learning
• Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL
• Learned Indexes for Dynamic Workloads
• Learning to Generate Synthetic 3D Training Data through Hybrid Gradient
• Bayesian active learning for choice models with deep Gaussian processes
• New Hybrid Neuro-Evolutionary Algorithms for Renewable Energy and Facilities Management Problems
• Non-smooth Bayesian Optimization in Tuning Problems
• Adaptive Bayesian Linear Regression for Automated Machine Learning
• Practical Bayesian Optimization for Transportation Simulators
• Bayesian Optimization for Parameter Tuning of the XOR Neural Network
• Model order reduction with neural networks: Application to laminar and turbulent flows
• Occupancy Map Building through Bayesian Exploration
• A General Framework for Constrained Bayesian Optimization using Information-based Search
• FixNorm: Dissecting Weight Decay for Training Deep Neural Networks
• Learning Space Partitions for Path Planning
• A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function
• BORE: Bayesian Optimization by Density-Ratio Estimation
• Adaptive Compression-based Lifelong Learning
• Good practices for Bayesian Optimization of high dimensional structured spaces
• Sparse Spectrum Gaussian Process for Bayesian Optimization
• Bayesian Optimization with Approximate Set Kernels
• Approximate Computational Approaches for Bayesian Sensor Placement in High Dimensions
• Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs
• Decentralized, Adaptive, Look-Ahead Particle Filtering
• Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization
• Parameter Optimization using high-dimensional Bayesian Optimization
• Neural Model-based Optimization with Right-Censored Observations
• A Bi-Objective Optimization Based Acquisition Strategy for Batch Bayesian Global Optimization
• Efficient Transfer Bayesian Optimization with Auxiliary Information
• DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning
• Surrogate-based Optimization using Mutual Information for Computer Experiments (optim-MICE)
• Evolving Gaussian Process kernels from elementary mathematical expressions
• Independent Component Analysis via Energy-based and Kernel-based Mutual Dependence Measures
• Multi-Step Bayesian Optimization for One-Dimensional Feasibility Determination
• Using Distance Correlation for Efficient Bayesian Optimization
• Generalized State-Feedback Controller Synthesis for Underactuated Systems through Bayesian Optimization
• Muddling Labels for Regularization, a novel approach to generalization
• Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments
• LSTM recurrent neural network assisted aircraft stall prediction for enhanced situational awareness
• Efficient Sampling for Predictor-Based Neural Architecture Search
• End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization
• Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization
• Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control
• Steganography GAN: Cracking Steganography with Cycle Generative Adversarial Networks
• Detection and classification of vocal productions in large scale audio recordings
• Designing MacPherson Suspension Architectures using Bayesian Optimization
• Polymer Sequence Design via Active Learning
• Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds
• Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications
• BO-DBA: Query-Efficient Decision-Based Adversarial Attacks via Bayesian Optimization
• On Preference Learning Based on Sequential Bayesian Optimization with Pairwise Comparison
• Bayesian optimization of non-classical optomechanical correlations
• Bayesian Optimization using Pseudo-Points
• Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization
• Computationally efficient surrogate-based optimization of coastal storm waves heights and run-ups
• Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
• Bayesian optimization with local search
• Noncooperative dynamics in election interference
• Derivative-Free Global Optimization Algorithms: Bayesian Method and Lipschitzian Approaches
• Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction
• Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches
• Funneled Bayesian Optimization for Design, Tuning and Control of Autonomous Systems
• Speed-Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization
• apsis - Framework for Automated Optimization of Machine Learning Hyper Parameters
• A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization
• Accelerating Experimental Design by Incorporating Experimenter Hunches
• Accelerated Bayesian Optimization throughWeight-Prior Tuning
• Empirical Evaluation of Contextual Policy Search with a Comparison-based Surrogate Model and Active Covariance Matrix Adaptation
• Topological Bayesian Optimization with Persistence Diagrams
• Economically Efficient Combined Plant and Controller Design Using Batch Bayesian Optimization: Mathematical Framework and Airborne Wind Energy Case Study
• Stable Bayesian Optimisation via Direct Stability Quantification
• Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization
• Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization
• Evaluation Function Approximation for Scrabble
• Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
• Automatic Calibration of Dynamic and Heterogeneous Parameters in Agent-based Model
• Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning
• Reconfigurable-intelligent-surface-assisted Downlink Transmission Design via Bayesian Optimization
• Online Parameter Estimation for Safety-Critical Systems with Gaussian Processes
• On Wind Speed Sensor Configurations and Altitude Control in Airborne Wind Energy Systems
• Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems
• Weighting NTBEA for Game AI Optimisation
• Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design
• AutoBSS: An Efficient Algorithm for Block Stacking Style Search
• Utility-Optimized Synthesis of Differentially Private Location Traces
• Multi-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion
• Personalized Optimization for Computer Experiments with Environmental Inputs
• Mining Scientific Workflows for Anomalous Data Transfers
• Scalable Bayesian Optimization with Sparse Gaussian Process Models
• Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition
• Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients
• Explaining Inference Queries with Bayesian Optimization
• One-parameter family of acquisition functions for efficient global optimization
• Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop
• Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction
• Quality and Computation Time in Optimization Problems
• Bayesian optimization for automatic design of face stimuli
• Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network
• Gaussian Process Bandits for Tree Search: Theory and Application to Planning in Discounted MDPs
• Simple and Scalable Parallelized Bayesian Optimization
• High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
• Learning-Based Modular Indirect Adaptive Control for a Class of Nonlinear Systems
• Optimizing generalization on the train set: a novel gradient-based framework to train parameters and hyperparameters simultaneously
• Hierarchical Surrogate Modeling for Illumination Algorithms
• Bayesian optimization approach for tracking the location and orientation of a moving target using far-field data
• ThRIve: Thermally Robust CNN Inference via Low-Rank Adaptation in Heterogeneous PIM Architectures
• Bayesian Policy Reuse
• DistTune: Distributed Fine-Grained Adaptive Traffic Speed Prediction for Growing Transportation Networks
• Targeted Active Learning for Bayesian Decision-Making
• DynamiQ: Planning for Dynamics in Network Streaming Analytics Systems
• Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling
• Fast and optimal nonparametric sequential design for astronomical observations
• Classification and Bayesian Optimization for Likelihood-Free Inference
• Intrinsic Exploration as Multi-Objective RL
• Nonmyopic Multifidelity Active Search
• A New Bayesian Optimization Algorithm for Complex High-Dimensional Disease Epidemic Systems
• LinEasyBO: Scalable Bayesian Optimization Approach for Analog Circuit Synthesis via One-Dimensional Subspaces
• BayesOpt: A Library for Bayesian optimization with Robotics Applications
• Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization
• Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect
• Optimized Look-Ahead Tree Policies: A Bridge Between Look-Ahead Tree Policies and Direct Policy Search
• Study of Signal Temporal Logic Robustness Metrics for Robotic Tasks Optimization
• PixelTransformer: Sample Conditioned Signal Generation
• Scaling Bayesian Optimization With Game Theory
• Seeking for low thermal conductivity atomic configurations in $\mathrm{S}{\mathrm{i}}_{0.5}\mathrm{G}{\mathrm{e}}_{0.5}$ alloys with Bayesian Optimization
• The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface
• Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation
• Gaussian Processes for Individualized Continuous Treatment Rule Estimation
• Capturing Structure Implicitly from Time-Series having Limited Data