Many tasks in explainable machine learning, such as data valuation and
feature attribution, perform expensive computation for each data point and can
be intractable for large datasets. These methods require efficient
approximations, and learning a network that directly predicts the desired
output, which is commonly known as amortization, is a promising solution.
However, training such models with exact labels is often intractable; we
therefore explore training with noisy labels and find that this is inexpensive
and surprisingly effective. Through theoretical analysis of the label noise and
experiments with various models and datasets, we show that this approach
significantly accelerates several feature attribution and data valuation
methods, often yielding an order of magnitude speedup over existing approaches.

该研究论文通过训练具有噪声标签的模型，通过理论分析和实验研究，证明了这种方法在特征归因和数据估值等任务中能够显著加速，并且通常比现有方法快一个数量级。

随机分摊：加速特征和数据归因的统一方法

Stochastic Amortization: A Unified Approach to Accelerate Feature and  Data Attribution

We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of
adaptive Bayesian experimental design that allows experiments to be run in
real-time. Traditional sequential Bayesian optimal experimental design
approaches require substantial computation at each stage of the experiment.
This makes them unsuitable for most real-world applications, where decisions
must typically be made quickly. DAD addresses this restriction by learning an
amortized design network upfront and then using this to rapidly run (multiple)
adaptive experiments at deployment time. This network represents a design
policy which takes as input the data from previous steps, and outputs the next
design using a single forward pass; these design decisions can be made in
milliseconds during the live experiment. To train the network, we introduce
contrastive information bounds that are suitable objectives for the sequential
setting, and propose a customized network architecture that exploits key
symmetries. We demonstrate that DAD successfully amortizes the process of
experimental design, outperforming alternative strategies on a number of
problems.

本文介绍了深度自适应设计（DAD）方法，通过一次前期学习建立自适应贝叶斯实验设计的成本分摊，实现了实时运行实验的功能。