2013
COLT
COLT 2013
General Oracle Inequalities for Gibbs Posterior with Application to Ranking
Abstract
In this paper, we summarize some recent results in Li et al. (2012), which can be used to extend an important PAC-Bayesian approach, namely the Gibbs posterior, to study the nonadditive ranking risk. The methodology is based on assumption-free risk bounds and nonasymptotic oracle inequalities, which leads to nearly optimal convergence rates and optimal model selection to balance the approximation errors and the stochastic errors.
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Trend Setter
— Supervised Learning
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Keyword Pioneer
— gibbs posterior
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
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Hot Topic Early Bird
— model selection
Authors
Topics
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Learning Types > Supervised Learning
Machine Learning > Core Methods > Ranking
Machine Learning > Learning Types > Ranking