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Found 8 papers, 6 papers with code
Generative Adversarial Bayesian Optimization for Surrogate Objectives
To address this limitation, we propose generative adversarial Bayesian optimization (GABO) using adaptive source critic regularization, a task-agnostic framework for Bayesian optimization that employs a Lipschitz-bounded source critic model to constrain the optimization trajectory to regions where the surrogate function is reliable.
Learning Performance-Improving Code Edits
Next, we propose a broad range of adaptation strategies for code optimization; for prompting, these include retrieval-based few-shot prompting and chain-of-thought, and for finetuning, these include performance-conditioned generation and synthetic data augmentation based on self-play.
Learning to Select Pivotal Samples for Meta Re-weighting
In this paper, we study how to learn to identify such a meta sample set from a large, imperfect training set, that is subsequently cleaned and used to optimize performance in the meta re-weighting setting.
Black Box Adversarial Prompting for Foundation Models
Prompting interfaces allow users to quickly adjust the output of generative models in both vision and language.
Discovering Many Diverse Solutions with Bayesian Optimization
Bayesian optimization (BO) is a popular approach for sample-efficient optimization of black-box objective functions.
Neural Likelihoods for Multi-Output Gaussian Processes
We construct flexible likelihoods for multi-output Gaussian process models that leverage neural networks as components.
Deep Feature Interpolation for Image Content Changes
We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation.
Bayesian Active Model Selection with an Application to Automated Audiometry
Using this and a previously published model for healthy responses, the proposed method is shown to be capable of diagnosing the presence or absence of NIHL with drastically fewer samples than existing approaches.
