The PAC-Bayesian approach is a powerful set of techniques to derive nonasymptotic risk
bounds for random estimators. The corresponding optimal distribution of estimators, usually …

PAC-Bayesian learning bounds are of the utmost interest to the learning community. Their
role is to connect the generalization ability of an aggregation distribution ρ ρ to its empirical …

We establish rates of convergences in statistical learning for time series forecasting. Using
the PAC-Bayesian approach, slow rates of convergence√ d/n for the Gibbs estimator under …

We propose a vector auto-regressive model with a low-rank constraint on the transition
matrix. This model is well suited to predict high-dimensional series that are highly correlated …

Exponential inequalities are main tools in machine learning theory. To prove exponential
inequalities for non iid random variables allows to extend many learning techniques to these …

The PAC-Bayesian approach is a powerful set of techniques to derive non-asymptotic risk
bounds for random estimators. The corresponding optimal distribution of estimators, usually …

We address the problem of forecasting a time series meeting the Causal Bernoulli Shift
model, using a parametric set of predictors. The aggregation technique provides a predictor …

摘要我国正处于城镇化的快速发展阶段. 然而, 在城镇化的过程中, 决策者常常面临这样的问题:
一个城市的资源能支撑多大的人口规模? 对产业结构进行怎样的调整才能最大化释放人口承载 …

The aim of this thesis is to apply new methodologies to public transportation data. Indeed,
we are more and more surrounded by sensors and computers generating huge amount of …

Gross Domestic Product (GDP) per capita is a critical degree of a nation's monetary growth
that records for its number of people. A balanced participation ratio of both males and …