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 …
optimization and has found countless successful applications, but its performance is often …
Pfns4bo: In-context learning for bayesian optimization
In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian
Optimization (BO). PFNs are neural processes that are trained to approximate the posterior …
Optimization (BO). PFNs are neural processes that are trained to approximate the posterior …
Survival of the most influential prompts: Efficient black-box prompt search via clustering and pruning
Prompt-based learning has been an effective paradigm for large pretrained language
models (LLM), enabling few-shot or even zero-shot learning. Black-box prompt search has …
models (LLM), enabling few-shot or even zero-shot learning. Black-box prompt search has …
Efficient federated prompt tuning for black-box large pre-trained models
With the blowout development of pre-trained models (PTMs), the efficient tuning of these
models for diverse downstream applications has emerged as a pivotal research concern …
models for diverse downstream applications has emerged as a pivotal research concern …
Hypervolume knowledge gradient: a lookahead approach for multi-objective bayesian optimization with partial information
S Daulton, M Balandat… - … Conference on Machine …, 2023 - proceedings.mlr.press
Bayesian optimization is a popular method for sample efficient multi-objective optimization.
However, existing Bayesian optimization techniques fail to effectively exploit common and …
However, existing Bayesian optimization techniques fail to effectively exploit common and …
Bayesian active causal discovery with multi-fidelity experiments
This paper studies the problem of active causal discovery when the experiments can be
done based on multi-fidelity oracles, where higher fidelity experiments are more precise and …
done based on multi-fidelity oracles, where higher fidelity experiments are more precise and …
Propertydag: Multi-objective bayesian optimization of partially ordered, mixed-variable properties for biological sequence design
Bayesian optimization offers a sample-efficient framework for navigating the exploration-
exploitation trade-off in the vast design space of biological sequences. Whereas it is …
exploitation trade-off in the vast design space of biological sequences. Whereas it is …
Bayesian optimization over high-dimensional combinatorial spaces via dictionary-based embeddings
We consider the problem of optimizing expensive black-box functions over high-dimensional
combinatorial spaces which arises in many science, engineering, and ML applications. We …
combinatorial spaces which arises in many science, engineering, and ML applications. We …
Generalizing Bayesian optimization with decision-theoretic entropies
Bayesian optimization (BO) is a popular method for efficiently inferring optima of an
expensive black-box function via a sequence of queries. Existing information-theoretic BO …
expensive black-box function via a sequence of queries. Existing information-theoretic BO …
SOBER: Highly parallel Bayesian optimization and Bayesian quadrature over discrete and mixed spaces
Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-
efficient methods of performing optimisation and quadrature where expensive-to-evaluate …
efficient methods of performing optimisation and quadrature where expensive-to-evaluate …