论文地址:Meta-learners for Estimating HeterogeneousTreatment Effects using Machine Learning 摘要 在实验和观察性研究中, 异质化的treatment的评估和分析受到了人们越来越多的关注。我们描述一些元算法,他们可以利用机器学习和统计学中的任何监督学习或者回归方法来评估条件处理效应( Conditional Average Treatment E...
在两个说服领域实验中,X-learner展示了如何被用于定位治疗方案并阐明潜在机制。 标题:Metalearners for estimating heterogeneous treatment effects using machine learning 链接:https://www.pnas.org/doi/epdf/10.1073/pnas.1804597116 广告 因果论:模型、推理和推断(原书第2版) 京东 ¥144.50 去购买 一、简介...
Motivated by a generalized formulation of gradient-based meta-learning, we propose a formulation that uses Transformers as hypernetworks for INRs, where it can directly build the whole set of INR weights with Transformers specialized as set-to-set mapping. We demonstrate the effectiveness of our ...
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We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-...
Solution of Meta_Learners for 2019 NeurIPS autoDL competition Usage: This repository is the code folder of the competition. Please: Follow the instructions from official website to setup the experimental environment. Download 2nd_General_TinyBERT_4L_312D.zip and thin_resnet34.h5 from the releases...
Meta-learning with implicit gradients--nips19 论文思想 Few-shot case formula Implicit MAML Algorithm Practical Algorithm 论文思想 原始的MAML算法一个很大的挑战是外循环(元更新)需要通过对内循环(梯度自适应)过程进行求导,一般就要求存储和计算高阶导数。这篇论文的核心是利用隐微分方法,... ...
For a tutorial of these metalearners with mathematical underpinning, please refer to the Chapter namedTreatment heterogeneity for survival outcomesin theHandbook of Matching and Weighting Adjustments for Causal Inference. In addition, this chapter includes a benchmarking study of the five metalearners via...
Metalearners for estimating heterogeneous treatment effects using machine learning 文档格式: .pdf 文档大小: 827.73K 文档页数: 10页 顶/踩数: 0/0 收藏人数: 0 评论次数: 0 文档热度: 文档分类: 论文--自然科学论文 文档标签: Metalearnersforestimatingheterogeneoustreatmenteffectsusingmachinelearning ...
Transformers are Meta-Reinforcement Learnersarxiv.org/abs/2206.06614 背景介绍 元强化学习需要从一些源任务里学习知识,再利用这些知识帮助在目标任务中快速获得较好的策略,一般有两种方法,一种是Context-based的,根据源任务轨迹 τi ,学习一个 zi=E(τi) 编码不同任务轨迹的特征,那么最终要学习的策略就变成了...