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Found 15 papers, 8 papers with code
Bayesian Optimization of Function Networks with Partial Evaluations
Recent work has considered Bayesian optimization of function networks (BOFN), where the objective function is computed via a network of functions, each taking as input the output of previous nodes in the network and additional parameters.
Unexpected Improvements to Expected Improvement for Bayesian Optimization
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often exceeded by that of more recent methods.
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
We prove that under suitable reparameterizations, the BO policy that maximizes the probabilistic objective is the same as that which maximizes the AF, and therefore, PR enjoys the same regret bounds as the original BO policy using the underlying AF.
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
We present a new kernel that allows for Gaussian process regression in $O((n+m)\log(n+m))$ time.
Robust Multi-Objective Bayesian Optimization Under Input Noise
In many manufacturing processes, the design parameters are subject to random input noise, resulting in a product that is often less performant than expected.
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces
Many real world scientific and industrial applications require optimizing multiple competing black-box objectives.
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy.
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
We argue that, even in the noiseless setting, generating multiple candidates in parallel is an incarnation of EHVI with uncertainty in the Pareto frontier and therefore can be addressed using the same underlying technique.
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
We propose a novel imitation-learning-based algorithm that distills a TS policy into an explicit policy representation by performing posterior inference and optimization offline.
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning
Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes.
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion.
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Real-world applications frequently have constraints with respect to a currently deployed policy.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings.
Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings.
