Data poisoning aims to compromise a machine learning based software component
by contaminating its training set to change its prediction results for test
inputs. Existing methods for deciding data-poisoning robustness have either
poor accuracy or long running time and, more importantly, they can only certify
some of the truly-robust cases, but remain inconclusive when certification
fails. In other words, they cannot falsify the truly-non-robust cases. To
overcome this limitation, we propose a systematic testing based method, which
can falsify as well as certify data-poisoning robustness for a widely used
supervised-learning technique named k-nearest neighbors (KNN). Our method is
faster and more accurate than the baseline enumeration method, due to a novel
over-approximate analysis in the abstract domain, to quickly narrow down the
search space, and systematic testing in the concrete domain, to find the actual
violations. We have evaluated our method on a set of supervised-learning
datasets. Our results show that the method significantly outperforms
state-of-the-art techniques, and can decide data-poisoning robustness of KNN
prediction results for most of the test inputs.

提出了一种基于系统化测试的方法来证明和证伪 k - 最近邻（KNN）算法的数据污染鲁棒性，该方法在抽象域中进行超逼近分析，缩小搜索空间，在具体域中进行系统化测试，查找实际违规情况，并显著优于现有技术。

KNN 数据毒化鲁棒性的系统化测试

Systematic Testing of the Data-Poisoning Robustness of KNN

We present JueWu-SL, the first supervised-learning-based artificial
intelligence (AI) program that achieves human-level performance in playing
multiplayer online battle arena (MOBA) games. Unlike prior attempts, we
integrate the macro-strategy and the micromanagement of MOBA-game-playing into
neural networks in a supervised and end-to-end manner. Tested on Honor of
Kings, the most popular MOBA at present, our AI performs competitively at the
level of High King players in standard 5v5 games.

JueWu-SL 是第一个在多人在线战略游戏中通过监督学习实现人类水平表现的 AI 程序，通过对宏观战略和微管理进行端到端的神经网络整合，经过 Honor of Kings 的测试，在标准的 5v5 游戏中表现出和高王段位选手的竞争水平。

监督学习在 MOBA 游戏中达到人类水平表现：《王者荣耀》案例研究

Supervised Learning Achieves Human-Level Performance in MOBA Games: A  Case Study of Honor of Kings

Machine-learning tasks frequently involve problems of manipulating and
classifying large numbers of vectors in high-dimensional spaces. Classical
algorithms for solving such problems typically take time polynomial in the
number of vectors and the dimension of the space. Quantum computers are good at
manipulating high-dimensional vectors in large tensor product spaces. This
paper provides supervised and unsupervised quantum machine learning algorithms
for cluster assignment and cluster finding. Quantum machine learning can take
time logarithmic in both the number of vectors and their dimension, an
exponential speed-up over classical algorithms.

本文提出了适用于聚类分配和聚类查找的有监督和无监督的量子机器学习算法，利用量子计算能够处理高维向量的优势，实现了对于大量向量的分类问题的指数级加速。