开课单位--德克萨斯大学

51
New Approaches to the Study of Machiavelli[马基雅维利研究的新方法]
  Marco Paoli(德克萨斯大学) Niccolò di Bernardo dei Machiavelli (3 May 1469 – 21 June 1527) was an Italian philosopher, writer, and politician and is considered one ...
热度：60

52
Semi-supervised Graph Clustering: A Kernel Approach[半监督图聚类：一种核方法]
  Brian Kulis(德克萨斯大学) 半监督图聚类：一种核方法
热度：32

53
Catching the Drift: Learning Broad Matches from Clickthrough Data[捕捉漂移：点击数据学习广泛的比赛]
  Sonal Gupta(德克萨斯大学) Identifying similar keywords, known as broad matches, is an important task in online advertising that has become a standard feature on all major keywo...
热度：34

54
Learning RoboCup-Keepaway with Kernels[学习机器人世界杯足球锦标赛方法的要点]
  Tobias Jung(德克萨斯大学) We give another success story of using kernel-based methods to solve a dificult reinforcement learning problem, namely that of 3vs2 keepaway in RoboCu...
热度：38

55
Lower Bounds and Hardness Amplification for Learning Shallow Monotone Formulas[学习浅单调公式的下限和硬度放大]
  Homin K. Lee(德克萨斯大学) Much work has been done on learning various classes of “simple” monotone functions under the uniform distribution. In this paper we give t...
热度：55

56
Bottom-Up Search and Transfer Learning in SRL[公司转让中自底向上的搜索和学习]
  Mooney Raymond J(德克萨斯大学) This talk addresses two important issues motivated by of our recent research in SRL. First, is the value of data-driven, "bottom-up" search ...
热度：36

57
A Novel Framework for Locating Software Faults Using Latent Divergences[一种利用潜在分歧软件故障定位的框架]
  Sarfraz Khurshid(德克萨斯大学) Fault localization, i.e., identifying erroneous lines of code in a buggy program, is a tedious process, which often requires considerable manual effor...
热度：41

58
An Empirical Comparison of Abstraction in Models of Markov Decision Processes[马尔可夫决策过程模型抽象的实证比较]
  Todd Hester(德克萨斯大学) Reinforcement learning studies the problem of solving sequential decision making problems. Model-based methods learn an effective policy in few action...
热度：29

59
Feature Selection for Value Function Approximation Using Bayesian Model Selection[使用贝叶斯模型选择的值函数近似的特征选择]
  Tobias Jung(德克萨斯大学) Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be ob...
热度：43

60
Max-Margin Weight Learning for Markov Logic Networks[马尔科夫逻辑网络的最大边缘权重学习]
  Tuyen Ngoc Huynh(德克萨斯大学) Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both first-order logic and graphica...
热度：37