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Found 19 papers, 10 papers with code
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.
Joint Composite Latent Space Bayesian Optimization
To effectively tackle these challenges, we introduce Joint Composite Latent Space Bayesian Optimization (JoCo), a novel framework that jointly trains neural network encoders and probabilistic models to adaptively compress high-dimensional input and output spaces into manageable latent representations.
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 High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
We use Bayesian Optimization (BO) and propose a novel surrogate modeling approach for efficiently handling a large number of binary and categorical parameters.
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.
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-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
To overcome the shortcomings of existing approaches, we propose the budgeted multi-step expected improvement, a non-myopic acquisition function that generalizes classical expected improvement to the setting of heterogeneous and unknown evaluation costs.
Sustainable AI: Environmental Implications, Challenges and Opportunities
This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware.
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces
Many real world scientific and industrial applications require optimizing multiple competing black-box objectives.
Bayesian Optimization with High-Dimensional Outputs
However, the Gaussian Process (GP) models typically used as probabilistic surrogates for multi-task Bayesian Optimization scale poorly with the number of outcomes, greatly limiting applicability.
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.
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.
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree.
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.
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.
Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games
We study a general adversarial online learning problem, in which we are given a decision set X' in a reflexive Banach space X and a sequence of reward vectors in the dual space of X.
Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games
Under the assumption of uniformly continuous rewards, we obtain explicit anytime regret bounds in a setting where the decision set is the set of probability distributions on a compact metric space $S$ whose Radon-Nikodym derivatives are elements of $L^p(S)$ for some $p > 1$.
