图 书 馆开课单位--普林斯顿大学
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31Galaxy Structure and Formation[星系结构与形成]
Jeremiah P. Ostriker;Peter O. Vandervoort(普林斯顿大学) 星系结构与形成
热度：27
Jeremiah P. Ostriker;Peter O. Vandervoort(普林斯顿大学) 星系结构与形成
热度：27
32Predicting Abnormal Returns From News Using Text Classification[使用文本分类新闻预测异常收益率]
Ronny Luss(普林斯顿大学) 使用文本分类新闻预测异常收益率
热度：81
Ronny Luss(普林斯顿大学) 使用文本分类新闻预测异常收益率
热度：81
33A Tensor Spectral Approach to Learning Mixed Membership Community Models[学习混合隶属度社区模型的张量谱方法]
Rong Ge(普林斯顿大学) Modeling community formation and detecting hidden communities in networks is a well studied problem. However, theoretical analysis of community detect...
热度：40
Rong Ge(普林斯顿大学) Modeling community formation and detecting hidden communities in networks is a well studied problem. However, theoretical analysis of community detect...
热度：40
34Magnetohydrodynamics in Astrophysical Contexts[在天体物理学中磁流体力学]
Eugene N. Parker; James M. Stone(普林斯顿大学) 在天体物理学中磁流体力学
热度：38
Eugene N. Parker; James M. Stone(普林斯顿大学) 在天体物理学中磁流体力学
热度：38
35Minimax Policies for Combinatorial Prediction Games[组合预测游戏的极大极小策略]
Sébastien Bubeck(普林斯顿大学) We address the online linear optimization problem when the actions of the forecaster are represented by binary vectors. Our goal is to understand the ...
热度：36
Sébastien Bubeck(普林斯顿大学) We address the online linear optimization problem when the actions of the forecaster are represented by binary vectors. Our goal is to understand the ...
热度：36
36A Theory of Multiclass Boosting[多类的一个推进理论]
Indraneel Mukherjee(普林斯顿大学) Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclas...
热度：32
Indraneel Mukherjee(普林斯顿大学) Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclas...
热度：32
37Dirichlet process mixtures of generalised linear models[狄利克雷过程广义线性模型的混合物]
Lauren A. Hannah(普林斯顿大学) We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLMs), a new method of nonparametric regression that accommodates continuous an...
热度：51
Lauren A. Hannah(普林斯顿大学) We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLMs), a new method of nonparametric regression that accommodates continuous an...
热度：51
38Convex Relaxation and Estimation of High-Dimensional Matrices[高维矩阵的凸松弛与估计]
Martin J. Wainwright(普林斯顿大学) Problems that require estimating high-dimensional matrices from noisy observations arise frequently in statistics and machine learning. Examples incl...
热度：31
Martin J. Wainwright(普林斯顿大学) Problems that require estimating high-dimensional matrices from noisy observations arise frequently in statistics and machine learning. Examples incl...
热度：31
39Online Variational Inference for the Hierarchical Dirichlet Process[层次狄利克雷过程的在线变分推理]
Chong Wang(普林斯顿大学) The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric model that can be used to model mixed-membership data with a potentially infinite...
热度：181
Chong Wang(普林斯顿大学) The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric model that can be used to model mixed-membership data with a potentially infinite...
热度：181
40Bounded regret in stochastic multi-armed bandits[multi-armed bandits的有界后悔问题]
Sébastien Bubeck(普林斯顿大学) We study the stochastic multi-armed bandit problem when one knows the value μ(⋆) of an optimal arm, as a well as a positive lower bound on the sma...
热度：79
Sébastien Bubeck(普林斯顿大学) We study the stochastic multi-armed bandit problem when one knows the value μ(⋆) of an optimal arm, as a well as a positive lower bound on the sma...
热度：79