PAC-Bayes 的上界可以理解为是经验误差和模型复杂度之间的均衡，经验误差反映了模型拟合数据的程度，而复...
PAC-Bayes 的上界可以理解为是经验误差和模型复杂度之间的均衡，经验误差反映了模型拟合数据的程度，而复...
The application of PAC-Bayesian theory to Meta-RL poses a challenge due to the existence of dependencies in the training data, which renders the independent and identically distributed (i.i.d.) assumption invalid. To address this challenge, we propose a dependency graph-based offline decomposition...
(2019) provide the first generalisation bounds for CURL, relying on a Rademacher complexity. We extend their framework to the flexible PAC-Bayes setting, allowing us to deal with the non-iid setting. We present PAC-Bayesian generalisation bounds for CURL, which are then used to derive a new ...
Repository files navigation README PAC_bayesian_online_clustering Implementation of the PACO algorithm (online clustering algorithm in which the number of cluster is found automatically) developped in Le Li, Benjamin Guedj, Sebastien Loustau. PAC-Bayesian Online Clustering. 2016. About...
B. Guedj and S. Robbiano. PAC-Bayesian High Dimensional Bipartite Rank- ing. arXiv preprint, 2015. URL http://arxiv.org/abs/1511.02729. 3, 9B. Guedj and S. Robbiano. PAC-Bayesian High Dimensional Bipartite Rank- ing. Preprint arXiv:1511.02729, 2015. 3, 11...
(2011). PAC-Bayesian bounds for sparse regression estimation with exponential weights. Electronic Journal of Statistics, 5, 127- 145.Alquier, P. and Lounici, K. (2011). PAC-Bayesian bounds for sparse regression esti- mation with exponential weights. Electron. J. Stat. 5 127-145. MR...
主要考虑基于Bayes分类的PAC-Bayesian定理的证明,以便有效运用到统计问题中.在此,最简单地来讲,Bayes分类是利用贝叶斯变换公式的分类算法. 1.1 PAC模型 X为输入空间,Y为输出空间,X={x1,x2,...,xm},Y={y1,y2,...,yn},Y=f(x),Θ为所有可能的假设空间.在Θ中选取可行的f去估计Y,在假设空间Θ中的任何...
概率近似正确(PAC)是研究"可学习"的理论框架.近年来,研究人员融合贝叶斯方法与不依赖分布的PAC性能度量提出了所谓的PAC-Bayesian学习理论.该理论因其对于任意概念空间任意测度的先验均能给出泛化误差界而在人工智能不同领域的相关算法分析中得到广泛应用.文章综述了PAC-Bayesian学习理论的由来及其核心思想,进而结合大数据的...
We study a two-level multiview learning with more than two views under the PAC-Bayesian framework. This approach, sometimes referred as late fusion, consists in learning sequentially multiple view-specific classifiers at the first level, and then combining these view-specific classifiers at the ...