Active Learning for Accurate Estimation ...
Type de document :
Communication dans un congrès avec actes
Titre :
Active Learning for Accurate Estimation of Linear Models
Auteur(s) :
Riquelme, Carlos [Auteur]
Stanford University
Ghavamzadeh, Mohammad [Auteur]
Sequential Learning [SEQUEL]
Adobe Systems Inc. [Adobe Advanced Technology Labs]
Lazaric, Alessandro [Auteur]
Sequential Learning [SEQUEL]
Stanford University
Ghavamzadeh, Mohammad [Auteur]
Sequential Learning [SEQUEL]
Adobe Systems Inc. [Adobe Advanced Technology Labs]
Lazaric, Alessandro [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Sequential Learning [SEQUEL]
Titre de la manifestation scientifique :
ICML 2017 - 34th International Conference on Machine Learning
Ville :
Sydney
Pays :
Australie
Date de début de la manifestation scientifique :
2017-08-06
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
We explore the sequential decision-making problem where the goal is to estimate a number of linear models uniformly well, given a shared budget of random contexts independently sampled from a known distribution. For each ...
Lire la suite >We explore the sequential decision-making problem where the goal is to estimate a number of linear models uniformly well, given a shared budget of random contexts independently sampled from a known distribution. For each incoming context, the decision-maker selects one of the linear models and receives an observation that is corrupted by the unknown noise level of that model. We present Trace-UCB, an adaptive allocation algorithm that learns the models' noise levels while balancing contexts accordingly across them, and prove bounds for its simple regret in both expectation and high-probability. We extend the algorithm and its bounds to the high dimensional setting , where the number of linear models times the dimension of the contexts is more than the total budget of samples. Simulations with real data suggest that Trace-UCB is remarkably robust , outperforming a number of baselines even when its assumptions are violated.Lire moins >
Lire la suite >We explore the sequential decision-making problem where the goal is to estimate a number of linear models uniformly well, given a shared budget of random contexts independently sampled from a known distribution. For each incoming context, the decision-maker selects one of the linear models and receives an observation that is corrupted by the unknown noise level of that model. We present Trace-UCB, an adaptive allocation algorithm that learns the models' noise levels while balancing contexts accordingly across them, and prove bounds for its simple regret in both expectation and high-probability. We extend the algorithm and its bounds to the high dimensional setting , where the number of linear models times the dimension of the contexts is more than the total budget of samples. Simulations with real data suggest that Trace-UCB is remarkably robust , outperforming a number of baselines even when its assumptions are violated.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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- active_learning_accurate_estimation_linear_models_supplementary.pdf
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