572 citations
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25 papers · 1 filter
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Felipe Petroski Such, Aditya Rawal, Joel Lehman +2
This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in orde…
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Sumanth Dathathri, Andrea Madotto, Janice Lan +5
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated languag…
Multi-Attribute Bayesian Optimization With Interactive Preference Learning
Raul Astudillo, Peter I. Frazier
We consider black-box global optimization of time-consuming-to-evaluate functions on behalf of a decision-maker (DM) whose preferences must be learned. Each feasible design is asso…
Parameter elimination in particle Gibbs sampling
Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray +1
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories. A viable approach is particle Markov chain Monte Carlo, combining MCMC and seque…
Parametric Gaussian Process Regressors
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
The combination of inducing point methods with stochastic variational inference has enabled approximate Gaussian Process (GP) inference on large datasets. Unfortunately, the result…
An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue
Norman Packard, Mark A. Bedau, Alastair Channon +4
Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongl…