Bayesian Meta-Learning Approaches 第一个想到的方法是让模型直接输出关于$y\^{ts}$分布的参数值。 好处是简单、能够结合其他的多种方法。 坏处是不能得到模型函数的不确定性原因,如确定数据点之间的不确定性如何关联。只能够表达有限的相对于目标ytsy^{ts}yts的分布类别。倾向于产生低校准的不确定性估计。
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsMassimiliano PatacchiolaJack TurnerElliot J. CrowleyMichael F. P. O'BoyleAmos J. StorkeyCurran Associates IncNeural Information Processing Systems
2. 主动学习和持续学习（Active and Continual Learning）：BDL可以提升深度学习在主动学习和持续学习任务...
-分类器-输出预测值 auto部分如下图绿色方框:在ML framework 左边新增 meta-learning,在右边新增 build-ensemble,对于调超参数,用的是贝叶斯优化。自动学习... auto-sklearn 里,一直出现的bayesianoptimizer就是答案。是利用贝叶斯优化进行自动调参的。 具体的贝叶斯优化原理链接 http://codewithzhangyi.com ...
Paper tables with annotated results for Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
We introduce a new framework for studying meta-learning methods using PAC-Bayesian theory. Its main advantage over previous work is that it allows for more flexibility in how the transfer of knowledge between tasks is realized. For previous approaches, this could only happen indirectly, by means ...
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning Luisa Zintgraf, Sam Devlin, Kamil Ciosek, Shimon Whiteson, Katja Hofmann 20th International Conference on Autonomous Agents and Multiagent Systems|May 2021 PDF 下载BibTex Reinforcement learning in Minecraft: Challenges and o...
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 5 - Bayesian Meta-Learning斯坦福CS330:多项任务和元学习,2019 |讲座5 - 贝叶斯元学习 Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/ To get the latest news on
之前RL课上讲到Q Learning 的一些缺陷,其中一个很大的问题就是overestimation,因此有了double Q learning以及TD3 一定程度上解决这个问题,我觉得主要原因在于Q function estimation的时候,对于一些没遇到过的state,预测的Q function可能会和实际相差很大,而在Bellman update的时候又是去了max,就会导致overestimate, 使得 ...
Overview.We introduce a Bayesian meta-learning method based onGaussian Processes (GPs)to tackle the problem of few-shot learning. We propose a simple, yet effective variant of deep kernel learning in which the kernel is transferred across tasks, which we callDeep Kernel Transfer (DKT). This ap...