Uncertain changes in data streams present challenges for machine learning
models to dynamically adapt and uphold performance in real-time. Particularly,
classification boundary change, also known as real concept drift, is the major
cause of classification performance deterioration. However, accurately
detecting real concept drift remains challenging because the theoretical
foundations of existing drift detection methods - two-sample distribution tests
and monitoring classification error rate, both suffer from inherent limitations
such as the inability to distinguish virtual drift (changes not affecting the
classification boundary, will introduce unnecessary model maintenance), limited
statistical power, or high computational cost. Furthermore, no existing
detection method can provide information on the trend of the drift, which could
be invaluable for model maintenance. This work presents a novel real concept
drift detection method based on Neighbor-Searching Discrepancy, a new statistic
that measures the classification boundary difference between two samples. The
proposed method is able to detect real concept drift with high accuracy while
ignoring virtual drift. It can also indicate the direction of the
classification boundary change by identifying the invasion or retreat of a
certain class, which is also an indicator of separability change between
classes. A comprehensive evaluation of 11 experiments is conducted, including
empirical verification of the proposed theory using artificial datasets, and
experimental comparisons with commonly used drift handling methods on
real-world datasets. The results show that the proposed theory is robust
against a range of distributions and dimensions, and the drift detection method
outperforms state-of-the-art alternative methods.

基于 Neighbor-Searching Discrepancy 的新统计量，提出了一种能够高准确率检测真实概念漂移并忽略虚假漂移的方法，同时还能指示分类边界变化的方法。实验结果表明，该方法在不同分布和维度下具有鲁棒性，并且在漂移检测方面优于现有方法。

一种基于邻居搜索的差异性漂移检测方案，用于学习进化数据

A Neighbor-Searching Discrepancy-based Drift Detection Scheme for  Learning Evolving Data

As the use of machine learning (ML) models is becoming increasingly popular
in many real-world applications, there are practical challenges that need to be
addressed for model maintenance. One such challenge is to 'undo' the effect of
a specific subset of dataset used for training a model. This specific subset
may contain malicious or adversarial data injected by an attacker, which
affects the model performance. Another reason may be the need for a service
provider to remove data pertaining to a specific user to respect the user's
privacy. In both cases, the problem is to 'unlearn' a specific subset of the
training data from a trained model without incurring the costly procedure of
retraining the whole model from scratch. Towards this goal, this paper presents
a Markov chain Monte Carlo-based machine unlearning (MCU) algorithm. MCU helps
to effectively and efficiently unlearn a trained model from subsets of training
dataset. Furthermore, we show that with MCU, we are able to explain the effect
of a subset of a training dataset on the model prediction. Thus, MCU is useful
for examining subsets of data to identify the adversarial data to be removed.
Similarly, MCU can be used to erase the lineage of a user's personal data from
trained ML models, thus upholding a user's "right to be forgotten". We
empirically evaluate the performance of our proposed MCU algorithm on
real-world phishing and diabetes datasets. Results show that MCU can achieve a
desirable performance by efficiently removing the effect of a subset of
training dataset and outperform an existing algorithm that utilizes the
remaining dataset.

本文提出了一种基于 MCU 算法的机器学习模型快速且高效地去除数据子集的方案，并利用 MCU 鉴别和标志对模型预测产生影响的对抗或隐私数据。实验表明，MCU 可以从训练数据集的子集中有效地去除模型的影响，并优于现有的算法。

基于马尔可夫链蒙特卡罗的机器遗忘：遗忘需要被遗忘的内容

Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs  to be Forgotten

With the recent explosion in popularity of voice assistant devices, there is
a growing interest in making them available to user populations in additional
countries and languages. However, to provide the highest accuracy and best
performance for specific user populations, most existing voice assistant models
are developed individually for each region or language, which requires linear
investment of effort. In this paper, we propose a general multilingual model
framework for Natural Language Understanding (NLU) models, which can help
bootstrap new language models faster and reduce the amount of effort required
to develop each language separately. We explore how different deep learning
architectures affect multilingual NLU model performance. Our experimental
results show that these multilingual models can reach same or better
performance compared to monolingual models across language-specific test data
while require less effort in creating features and model maintenance.

本文研究提出了一种通用多语言模型的框架，该框架利用深度学习架构实现语音助手设备的自然语言理解，显著减少了单独开发每种语言模型所需要的努力和维护成本，并在多语种环境下取得了比单语种环境下更好的研究成果。