在广泛的模拟研究中,尽管没有一个元学习器是一致最好的,X-learner的性能是令人满意的。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:pnas.org/doi/epdf/10.10 广告 因果论:模型、推理...
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,... ...
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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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...
MarcoGorelli deleted the ci/add-metalearners branch March 14, 2025 12:01 Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment Reviewers MarcoGorelli Assignees No one assigned Labels documentation Projects None yet Milestone No milestone Devel...
How Sensitive are Meta-Learners to Dataset Imbalance? Meta-Learning (ML) has proven to be a useful tool for training Few-Shot Learning (FSL) algorithms by exposure to batches of tasks sampled from a meta-dataset. However, the standard training procedure overlooks the dynamic nature of the ...
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-...
Meta-learners for few-shot weakly-supervised medical image segmentation Most uses of Meta-Learning in visual recognition are very often applied to image classification, with a relative lack of work in other tasks such as segmen... H Oliveira,PHT Gama,I Bloch,... - 《Pattern Recognition》 被...
(MxML), where mixing parameters are determined by the weight prediction network (WPN) optimized to improve the few-shot classification performance. Experiments on various datasets demonstrate that MxML significantly outperforms state-of-the-art meta-learners, or their naive ensemble in the case of ...