图 书 馆开课单位--加州大学
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1Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery[矩阵配置文件 V：将领域知识纳入主题发现的通用技术]
Hoang Anh Dau(加州大学) Time series motif discovery has emerged as perhaps the most used primitive for time series data mining, and has seen applications to domains as divers...
热度：13
Hoang Anh Dau(加州大学) Time series motif discovery has emerged as perhaps the most used primitive for time series data mining, and has seen applications to domains as divers...
热度：13
2Convex Risk Minimization and Conditional Probability Estimation[凸风险最小化与条件概率估计]
Matus Telgarsky(加州大学) This manuscript strengthens the link between convex risk minimization and conditional probability estimation, a connection already notable for establi...
热度：35
Matus Telgarsky(加州大学) This manuscript strengthens the link between convex risk minimization and conditional probability estimation, a connection already notable for establi...
热度：35
3Detecting the Presence and Absence of Causal Relationships Between Expression of Yeast Genes with Very Few Samples[用很少的样品检测酵母基因表达之间因果关系的存在与否]
Eun Yong Kang(加州大学) Detecting the Presence and Absence of Causal Relationships Between Expression of Yeast Genes with Very Few Samples
热度：24
Eun Yong Kang(加州大学) Detecting the Presence and Absence of Causal Relationships Between Expression of Yeast Genes with Very Few Samples
热度：24
4Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions[概率分布RKHS嵌入的核选择和可分类性]
Bharath K.Sriperumbudur(加州大学) Embeddings of probability measures into reproducing kernel Hilbert spaces have been proposed as a straightforward and practical means of representing ...
热度：42
Bharath K.Sriperumbudur(加州大学) Embeddings of probability measures into reproducing kernel Hilbert spaces have been proposed as a straightforward and practical means of representing ...
热度：42
5Boosting with Structural Sparsity[结构稀疏的增压]
John Duchi(加州大学) We derive generalizations of AdaBoost and related gradient-based coordinate descent methods that incorporate sparsity-promoting penalties for the norm...
热度：29
John Duchi(加州大学) We derive generalizations of AdaBoost and related gradient-based coordinate descent methods that incorporate sparsity-promoting penalties for the norm...
热度：29
6Accounting for Burstiness in Topic Models[主题模型中突发事件的解释]
Gabriel Doyle(加州大学) Many different topic models have been used successfully for a variety of applications. However, even state-of-the-art topic models suffer from the imp...
热度：21
Gabriel Doyle(加州大学) Many different topic models have been used successfully for a variety of applications. However, even state-of-the-art topic models suffer from the imp...
热度：21
7Lower bounds on the performance of polynomial-time algorithms for sparse linear regression[稀疏线性回归多项式时间算法性能的下界]
Yuchen Zhang(加州大学) Under a standard assumption in complexity theory (NP not in P/poly), we demonstrate a gap between the minimax prediction risk for sparse linear regres...
热度：24
Yuchen Zhang(加州大学) Under a standard assumption in complexity theory (NP not in P/poly), we demonstrate a gap between the minimax prediction risk for sparse linear regres...
热度：24
8Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing[具有KL控制成本的大规模Markov决策问题及其在众包中的应用]
Alan Malek(加州大学) We study average and total cost Markov decision problems with large state spaces. Since the computational and statistical costs of finding the optimal...
热度：29
Alan Malek(加州大学) We study average and total cost Markov decision problems with large state spaces. Since the computational and statistical costs of finding the optimal...
热度：29
9Causal Inference as Computational Learning[作为计算学习的因果推理]
Judea Pearl,(加州大学) Causal Inference as Computational Learning
热度：22
Judea Pearl,(加州大学) Causal Inference as Computational Learning
热度：22
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