An approximation approach to the problem of the acquisition of phonotactics in Optimality Theory

The Error-driven Ranking Model of the Acquisition of Phonotactics: How to Keep the Faithfulness Constraints at Bay

Optimal Data Acquisition for Reinforcement Learning: A Large Deviations Perspective

Data acquisition efficiency is a central challenge in deploying reinforcement learning in business and healthcare operations, where interactions are costly, slow, and often involve humans in the loop. This paper develops…

Optimality Theory as a Framework for Lexical Acquisition

This paper re-investigates a lexical acquisition system initially developed for French.We show that, interestingly, the architecture of the system reproduces and implements the main components of Optimality Theory. Howev…

Batch Acquisition Function Evaluations and Decouple Optimizer Updates for Faster Bayesian Optimization

Bayesian optimization (BO) efficiently finds high-performing parameters by maximizing an acquisition function, which models the promise of parameters. A major computational bottleneck arises in acquisition function optim…

On Kernel Derivative Approximation with Random Fourier Features

Random Fourier features (RFF) represent one of the most popular and wide-spread techniques in machine learning to scale up kernel algorithms. Despite the numerous successful applications of RFFs, unfortunately, quite lit…