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Rui Castro

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4 papers

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NeurIPS Conference 2023 Conference Paper

Adaptive Selective Sampling for Online Prediction with Experts

Rui Castro
Fredrik Hellström
Tim van Erven

We consider online prediction of a binary sequence with expert advice. For this setting, we devise label-efficient forecasting algorithms, which use a selective sampling scheme that enables collecting much fewer labels than standard procedures. For the general case without a perfect expert, we prove best-of-both-worlds guarantees, demonstrating that the proposed forecasting algorithm always queries sufficiently many labels in the worst case to obtain optimal regret guarantees, while simultaneously querying much fewer labels in more benign settings. Specifically, for a scenario where one expert is strictly better than the others in expectation, we show that the label complexity of the label-efficient forecaster is roughly upper-bounded by the square root of the number of rounds. Finally, we present numerical experiments empirically showing that the normalized regret of the label-efficient forecaster can asymptotically match known minimax rates for pool-based active learning, suggesting it can optimally adapt to benign settings.

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EAAI Journal 2017 Journal Article

Offshore wind farm layout optimization regarding wake effects and electrical losses

Luís Amaral
Rui Castro

A major development of the offshore wind energy market is being witnessed. Since the implicated costs are considerably high, it is extremely important to ensure that the energy production is maximum, so that the costs per energy unit are minimized. Thus, the turbines should be strategically positioned to extract as much energy as possible from the wind, considering wake effect losses, as well as internal grid electrical losses. In order to avoid turbines to be placed in unrealistic positions, they should be distributed according to a grid of rectangular shaped cells; each of these is divided in multiple sub-cells. The problem of finding the turbines optimal position among the pre-defined sub-cells so that maximum annual energy is produced could be addressed using a deterministic approach. However, the problem becomes unfeasible when the number of turbines and/or the number of sub-cells increase. To overcome this difficulty, optimization techniques should be used. Genetic Algorithm and Particle Swarm Optimization are approached in this paper. This paper deals with the wind park layout optimization problem. A methodology to position the turbines inside a wind park so that the annual energy production is maximum is proposed. The results proved that the meta-heuristic method is much more CPU time efficient in providing the maximum annual year production as compared to the traditional deterministic approach.

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NeurIPS Conference 2008 Conference Paper

Human Active Learning

Rui Castro
Charles Kalish
Robert Nowak
Ruichen Qian
Tim Rogers
Jerry Zhu

We investigate a topic at the interface of machine learning and cognitive science. Human active learning, where learners can actively query the world for information, is contrasted with passive learning from random examples. Furthermore, we compare human active learning performance with predictions from statistical learning theory. We conduct a series of human category learning experiments inspired by a machine learning task for which active and passive learning error bounds are well understood, and dramatically distinct. Our results indicate that humans are capable of actively selecting informative queries, and in doing so learn better and faster than if they are given random training data, as predicted by learning theory. However, the improvement over passive learning is not as dramatic as that achieved by machine active learning algorithms. To the best of our knowledge, this is the first quantitative study comparing human category learning in active versus passive settings.

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NeurIPS Conference 2005 Conference Paper

Faster Rates in Regression via Active Learning

Rebecca Willett
Robert Nowak
Rui Castro

This paper presents a rigorous statistical analysis characterizing regimes in which active learning significantly outperforms classical passive learning. Active learning algorithms are able to make queries or select sample locations in an online fashion, depending on the results of the previous queries. In some regimes, this extra flexibility leads to significantly faster rates of error decay than those possible in classical passive learning settings. The nature of these regimes is explored by studying fundamental performance limits of active and passive learning in two illustrative nonparametric function classes. In addition to examining the theoretical potential of active learning, this paper describes a practical algorithm capable of exploiting the extra flexibility of the active setting and provably improving upon the classical passive techniques. Our active learning theory and methods show promise in a number of applications, including field estimation using wireless sensor networks and fault line detection.

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