Specialist Experts for Prediction with Side Information

Kalnishkan, Yuri; Adamskiy, Dmitry; Chernov, Alexey; Scarfe, Tim

doi:10.1109/icdmw.2015.161

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…The method of specialized experts was first proposed by and further developed by Chernov and Vovk (2009), Devaine et al (2013), Gaillard et al (2014), Kalnishkan et al (2015). With this approach, at each step t, a set of specialized experts E t ⊆ {1, .…”

Section: Aa For Experts With Confidencementioning

confidence: 99%

Online Aggregation of Probability Forecasts with Confidence

V’yugin¹,

Trunov²

2021

Preprint

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The paper presents numerical experiments and some theoretical developments in prediction with expert advice (PEA). One experiment deals with predicting electricity consumption depending on temperature and uses real data. As the pattern of dependence can change with season and time of the day, the domain naturally admits PEA formulation with experts having different "areas of expertise". We consider the case where several competing methods produce online predictions in the form of probability distribution functions. The dissimilarity between a probability forecast and an outcome is measured by a loss function (scoring rule). A popular example of scoring rule for continuous outcomes is Continuous Ranked Probability Score (CRPS). In this paper the problem of combining probabilistic forecasts is considered in the PEA framework. We show that CRPS is a mixable loss function and then the time-independent upper bound for the regret of the Vovk aggregating algorithm using CRPS as a loss function can be obtained. Also, we incorporate a "smooth" version of the method of specialized experts in this scheme which allows us to combine the probabilistic predictions of the specialized experts with overlapping domains of their competence.

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Section: Aa For Experts With Confidencementioning

confidence: 99%

Online Aggregation of Probability Forecasts with Confidence

V’yugin¹,

Trunov²

2021

Preprint

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“…The full data are available at the site, along with the schema. For the description of the data and how it was used in the competition, see [33,19,16].…”

Section: Data Availabilitymentioning

confidence: 99%

Model of cognitive dynamics predicts performance on standardized tests

et al. 2018

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In the modern knowledge economy, success demands sustained focus and high cognitive performance. Research suggests that human cognition is linked to a finite resource, and upon its depletion, cognitive functions such as self-control and decision-making may decline. While fatigue, among other factors, affects human activity, how cognitive performance evolves during extended periods of focus remains poorly understood. By analyzing performance of a large cohort answering practice standardized test questions online, we show that accuracy and learning decline as the test session progresses and recover following prolonged breaks. To explain these findings, we hypothesize that answering questions consumes some finite cognitive resources on which performance depends, but these resources recover during breaks between test questions. We propose a dynamic mechanism of the consumption and recovery of these resources and show that it explains empirical findings and predicts performance better than alternative hypotheses. While further controlled experiments are needed to identify the physiological origin of these phenomena, our work highlights the potential of empirical analysis of large-scale human behavior data to explore cognitive behavior.

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“…We carry out an empirical investigation on London and Ames house prices datasets. The experiments follow the approach of [KACS15]: prediction with expert advice can used to find relevant past information. Predictors trained on different sections of past data can be combined in the on-line mode so that prediction is carried out using relevant past data.…”

Section: Introductionmentioning

confidence: 99%

Aggregating Algorithm for prediction of packs

et al. 2019

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This paper formulates the protocol for prediction of packs, which a special case of prediction under delayed feedback. Under this protocol, the learner must make a few predictions without seeing the outcomes and then the outcomes are revealed. We develop the theory of prediction with expert advice for packs. By applying Vovk's Aggregating Algorithm to this problem we obtain a number of algorithms with tight upper bounds. We carry out empirical experiments on housing data.

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Specialist Experts for Prediction with Side Information

Cited by 7 publications

References 20 publications

Online Aggregation of Probability Forecasts with Confidence

Online Aggregation of Probability Forecasts with Confidence

Model of cognitive dynamics predicts performance on standardized tests

Aggregating Algorithm for prediction of packs

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