Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL)
ability, where the model learns to do an unseen task simply by conditioning on a prompt …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

The determinantal point process (DPP) is an elegant probabilistic model of repulsion with
applications in various machine learning tasks including summarization and search …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

Many applications require optimizing an unknown, noisy function that is expensive to
evaluate. We formalize this task as a multi-armed bandit problem, where the payoff function …

Many applications require optimizing an unknown, noisy function that is expensive to
evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is …

Assembling a collection of very prominent researchers in the field, the Handbook of Spatial
Statistics presents a comprehensive treatment of both classical and state-of-the-art aspects …

When monitoring spatial phenomena, which can often be modeled as Gaussian processes
(GPs), choosing sensor locations is a fundamental task. There are several common …

A note on maximizing a submodular set function subject to a knapsack constraint -
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We design new approximation algorithms for the problems of optimizing submodular and
supermodular functions subject to a single matroid constraint. Specifically, we consider the …