We introduce AlphaRank, an artificial intelligence approach to address the fixed-budget ranking and selection (R&S) problems. We formulate the sequential sampling decision as a Markov decision process and propose a Monte Carlo simulation-based rollout policy that utilizes classic R&S procedures as base policies for efficiently learning the value function of stochastic dynamic programming. We accelerate online sample-allocation by using deep reinforcement learning to pre-train a neural network model offline based on a given prior. We also propose a parallelizable computing framework for large-scale problems, effectively combining "divide and conquer" and "recursion" for enhanced scalability and efficiency. Numerical experiments demonstrate that the performance of AlphaRank is significantly improved over the base policies, which could be attributed to AlphaRank's superior capability on the trade-off among mean, variance, and induced correlation overlooked by many existing policies.
IBM Watson AI XPRIZE（IBM沃森人工智能X大奖）；Responsible AI Collaborative（负责任人工智能协作组织）
Emerging artificial intelligence (AI) applications often balance the preferences and impacts among diverse and contentious stakeholder groups. Accommodating these stakeholder groups during system design, development, and deployment requires tools for the elicitation of disparate system interests and collaboration interfaces supporting negotiation balancing those interests. This paper introduces interactive visual "participation interfaces" for Markov Decision Processes (MDPs) and collaborative ranking problems as examples restoring a human-centered locus of control.
We propose and experimentally motivate a new methodology to support decision-making processes in healthcare with artificial intelligence based on personal rankings of care decision making steps that can be identified with our methodology, questionnaire data and its statistical patterns. Our longitudinal quantitative cross-sectional three-stage study gathered self-ratings for 437 expression statements concerning healthcare situations on Likert scales in respect to "the need for help", "the advancement of health", "the hopefulness", "the indication of compassion" and "the health condition", and …
RealAI（瑞莱智慧）；Tsinghua University（清华大学）；Beijing Institute of Technology（北京理工大学）；Shanghai Jiao Tong University（上海交通大学）；China Nanhu Academy of Electronics and Information Technology（中国电子科技南湖研究院）；Xi’an Jiaotong University（西安交通大学）；Tencent YouTu Lab（腾讯优图实验室）；China Construction Bank Fintech（中国建设银行金融科技公司）；RippleInfo（涟漪信息科技）
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competi…
Ivey Business School, Western University（西安大略大学毅伟商学院）；Virginia Tech（弗吉尼亚理工大学）
Increasingly, modern Artificial Intelligence (AI) research has become more computationally intensive. However, a growing concern is that due to unequal access to computing power, only certain firms and elite universities have advantages in modern AI research. Using a novel dataset of 171394 papers from 57 prestigious computer science conferences, we document that firms, in particular, large technology firms and elite universities have increased participation in major AI conferences since deep learning's unanticipated rise in 2012. The effect is concentrated among elite universities, which are …
School of Mechanical Engineering, Federal University of Uberlândia（Uberlândia联邦大学机械工程学院）；University of Padova（帕多瓦大学）；Federal University of São João del Rei（圣若昂德尔雷伊联邦大学）
The monitoring of rotating machinery is an essential task in today's production processes. Currently, several machine learning and deep learning-based modules have achieved excellent results in fault detection and diagnosis. Nevertheless, to further increase user adoption and diffusion of such technologies, users and human experts must be provided with explanations and insights by the modules. Another issue is related, in most cases, with the unavailability of labeled historical data that makes the use of supervised models unfeasible. Therefore, a new approach for fault detection and diagnosis…
Pontifical Catholic University of Parana (PUCPR)（巴拉那天主教大学）；Federal Technological University of Parana (UTFPR)（巴拉那联邦理工大学）；Federal University of Parana (UFPR)（巴拉那联邦大学）
The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 10th, 2020, more than 7.1 million people were infected, and more than 400 thousand have died worldwide. In the current scenario, the Brazil and the United States of America present a high daily incidence of new cases and deaths. It is important to forecast the number of new cases in a time window of one week, once this can help the public health system developing strategic planning to deals with the COVID-19. In this paper, Bayesian regression neural network, cubist re…
Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study
McGill University（麦吉尔大学）；Aifred Health（Aifred Health公司）；Queens College, CUNY（纽约市立大学皇后学院）；University of Michigan（密歇根大学）；University of Arizona（亚利桑那大学）；Google（谷歌公司）
An Experimental Investigation into the Evaluation of Explainability Methods
University of Mons（蒙斯大学）；UCLouvain（鲁汶大学）；Cliniques Universitaires Saint-Luc（圣卢克大学诊所）；IREC（IREC研究所）；University of Namur（那慕尔大学）；Interuniversity Institute for Bioinformatics in Brussels（布鲁塞尔跨大学生物信息学研究所）；Université Libre de Bruxelles（布鲁塞尔自由大学）；Multitel（Multitel研究中心）
PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models
University of Catania, Department of Economics and Business（卡塔尼亚大学经济与商业系）；Poznan University of Technology, Institute of Computing Science（波兹南技术大学计算科学研究所）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）
