图 书 馆开课单位--会议
1
2
3
4
5
6
7
8
9
10
1Hoeffding and Bernstein Races for Selecting Policies in Evolutionary Direct Policy Search[Hoeffding和Bernstein的策略选择竞赛]
Christian Igel(会议) Uncertainty arises in reinforcement learning from various sources, and therefore it is necessary to consider statistics based on several roll-outs for...
热度：6
Christian Igel(会议) Uncertainty arises in reinforcement learning from various sources, and therefore it is necessary to consider statistics based on several roll-outs for...
热度：6
2Grammatical Inference as a Principal Component Analysis Problem[语法推理作为主成分分析问题]
Raphaël Bailly(会议) One of the main problems in probabilistic grammatical inference consists in inferring a stochastic language, i.e. a probability distribu- tion, in som...
热度：8
Raphaël Bailly(会议) One of the main problems in probabilistic grammatical inference consists in inferring a stochastic language, i.e. a probability distribu- tion, in som...
热度：8
3Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties[具有单调不可约性和相关性的非循环关系特征的分块构造]
Ondřej Kuželka(会议) We describe an algorithm for constructing a set of acyclic conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional...
热度：8
Ondřej Kuželka(会议) We describe an algorithm for constructing a set of acyclic conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional...
热度：8
4Trajectory Prediction: Learning to Map Situations to Robot Trajectories[轨迹预测：学习将情况映射到机器人轨迹]
Nikolay Jetchev(会议) Trajectory planning and optimization is a fundamental problem in articulated robotics. Algorithms used typically for this problem compute optimal traj...
热度：5
Nikolay Jetchev(会议) Trajectory planning and optimization is a fundamental problem in articulated robotics. Algorithms used typically for this problem compute optimal traj...
热度：5
5Robot Trajectory Optimization Using Approximate Inference[基于近似推理的机器人轨迹优化]
Marc Toussaint(会议) The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classica...
热度：7
Marc Toussaint(会议) The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classica...
热度：7
6Learning Nonlinear Dynamic Models[学习非线性动态模型]
Ruslan Salakhutdinov(会议) We present a novel approach for learning nonlinear dynamic models, which leads to a new set of tools capable of solving problems that are otherwise di...
热度：9
Ruslan Salakhutdinov(会议) We present a novel approach for learning nonlinear dynamic models, which leads to a new set of tools capable of solving problems that are otherwise di...
热度：9
7Generalization Analysis of Listwise Learning-to-Rank Algorithms[列表学习对排名算法的泛化分析]
Hang Li(会议) This paper presents a theoretical framework for ranking, and demonstrates how to perform generalization analysis of listwise ranking algorithms using ...
热度：8
Hang Li(会议) This paper presents a theoretical framework for ranking, and demonstrates how to perform generalization analysis of listwise ranking algorithms using ...
热度：8
8Ranking Interesting Subgroups[对感兴趣的子组进行排名]
Stefan Rüping(会议) Subgroup discovery is the task of identifying the top k patterns in a database with most significant deviation in the distribution of a target attribu...
热度：6
Stefan Rüping(会议) Subgroup discovery is the task of identifying the top k patterns in a database with most significant deviation in the distribution of a target attribu...
热度：6
9Decision Tree and Instance-Based Learning for Label Ranking[基于决策树和实例的标签排序学习]
Weiwei Cheng(会议) The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduc...
热度：8
Weiwei Cheng(会议) The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduc...
热度：8
10Optimistic Initialization and Greediness Lead to Polynomial Time Learning in Factored MDPs[因子MDP中的乐观初始化和贪婪导致多项式时间学习]
Istvan Szita(会议) In this paper we propose an algorithm for polynomial-time reinforcement learning in factored Markov decision processes (FMDPs). The factored optimisti...
热度：8
Istvan Szita(会议) In this paper we propose an algorithm for polynomial-time reinforcement learning in factored Markov decision processes (FMDPs). The factored optimisti...
热度：8