Automated Machine Learning (AutoML) has been used successfully in settings
where the learning task is assumed to be static. In many real-world scenarios,
however, the data distribution will evolve over time, and it is yet to be shown
whether AutoML techniques can effectively design online pipelines in dynamic
environments. This study aims to automate pipeline design for online learning
while continuously adapting to data drift. For this purpose, we design an
adaptive Online Automated Machine Learning (OAML) system, searching the
complete pipeline configuration space of online learners, including
preprocessing algorithms and ensembling techniques. This system combines the
inherent adaptation capabilities of online learners with the fast automated
pipeline (re)optimization capabilities of AutoML. Focusing on optimization
techniques that can adapt to evolving objectives, we evaluate asynchronous
genetic programming and asynchronous successive halving to optimize these
pipelines continually. We experiment on real and artificial data streams with
varying types of concept drift to test the performance and adaptation
capabilities of the proposed system. The results confirm the utility of OAML
over popular online learning algorithms and underscore the benefits of
continuous pipeline redesign in the presence of data drift.

本研究旨在针对数据漂移自动设计在线学习的管道，将在线学习器的固有适应能力与自动化管道的快速优化能力结合起来，我们基于异步遗传编程和异步连续缩减思路优化管道，实验证明了 OAML 系统在数据漂移情况下相比流行的在线学习算法具有更好的性能和适应能力，连续管道重新设计在处理数据漂移问题时效果显著。

在线自动机器学习：一种适用于在线学习的自适应自动机器学习框架

Online AutoML: An adaptive AutoML framework for online learning

Data scientists seeking a good supervised learning model on a new dataset
have many choices to make: they must preprocess the data, select features,
possibly reduce the dimension, select an estimation algorithm, and choose
hyperparameters for each of these pipeline components. With new pipeline
components comes a combinatorial explosion in the number of choices! In this
work, we design a new AutoML system to address this challenge: an automated
system to design a supervised learning pipeline. Our system uses matrix and
tensor factorization as surrogate models to model the combinatorial pipeline
search space. Under these models, we develop greedy experiment design protocols
to efficiently gather information about a new dataset. Experiments on large
corpora of real-world classification problems demonstrate the effectiveness of
our approach.

本文提出了一种新的 AutoML 系统，利用矩阵和张量分解作为代理模型，通过贪心实验设计协议来高效收集有关新数据集的信息，从而解决数据科学家在选择适当的监督式学习模型时面临的挑战。经过对大型实际分类问题的实验，证明了这种方法的有效性。

基于矩阵和张量分解的高效自动机器学习流水线搜索

Efficient AutoML Pipeline Search with Matrix and Tensor Factorization

Spoken language understanding system is traditionally designed as a pipeline
of a number of components. First, the audio signal is processed by an automatic
speech recognizer for transcription or n-best hypotheses. With the recognition
results, a natural language understanding system classifies the text to
structured data as domain, intent and slots for down-streaming consumers, such
as dialog system, hands-free applications. These components are usually
developed and optimized independently. In this paper, we present our study on
an end-to-end learning system for spoken language understanding. With this
unified approach, we can infer the semantic meaning directly from audio
features without the intermediate text representation. This study showed that
the trained model can achieve reasonable good result and demonstrated that the
model can capture the semantic attention directly from the audio features.

本文研究了口语理解系统的端到端学习方法，能够直接从音频特征中推断语义意义，而不需要中间文本表示。研究表明，该模型能够获得合理好的结果，并能直接从音频特征中捕捉语义注意力。