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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Prediction When Factors are Weak
因素较弱 预测 宏观经济预测
2023/5/5
In macroeconomic forecasting, principal component analysis (PCA) has been the most prevalent approach to the recovery of factors, which summarize information in a large set of macro predictors. Nevert...
Integrative approaches for microRNA target prediction: combining sequence information and the paired mRNA and miRNA expression profiles
target prediction expression profile integrative analysis
2016/1/20
Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a novel class of non-coding RNA, has recently been found to be one crucial class of post-transac...
Learning Policies for Contextual Submodular Prediction
Learning Policies Contextual Submodular Prediction
2013/6/14
Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options. Such lists are often evaluated using subm...
Minimax Policies for Combinatorial Prediction Games
Minimax Policies Combinatorial Prediction Games
2011/6/20
We address the online linear optimization problem when the actions of the forecaster are represented by
binary vectors. Our goal is to understand the magnitude of the minimax regret for the worst pos...
An Introduction to Artificial Prediction Markets for Classification
online learning supervised learning random forest implicit online learning
2011/3/18
Prediction markets are used in real life to predict outcomes of interest such as presidential elections. This paper presents a mathematical theory of artificial prediction markets for supervised learn...
No-Regret Reductions for Imitation Learning and Structured Prediction
No-Regret Reductions for Imitation Learning Structured Prediction
2010/11/9
Sequential prediction problems such as imitation learning, where future observations depend on
previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning.
Optimising prediction error among completely monotone covariance sequences
Gaussian time series prediction error covariance sequences
2009/3/19
We provide a characterisation of Gaussian time series which optimise the one-step prediction error subject to the covariance sequence being completely monotone with the first m covariances specified. ...