Ferdinando Fioretto (Syracuse University), Emma Frejinger (Universit de Montral), Elias B. Khalil (University of Toronto), Pashootan Vaezipoor (University of Toronto). The 21st Web Conference (WWW 2022), (Acceptance Rate: 17.7%), accepted. Jan 13, 2022: Notification. For general inquiries about AI2ASE, please write to the lead organizer aryan.deshwal@wsu.edu or jana.doppa@wsu.edu. Are you sure you want to create this branch? Hence, this workshop will focus on introducing research progress on applying AI to education and discussing recent advances of handling challenges encountered in AI educational practice. We welcome full research papers, position papers, and extended abstracts. Adverse event detection by integrating Twitter data and VAERS. At least three research trends are informing insights in this field. Any participant who experiences unacceptable behavior may contact any current member of the SIGMOD Executive Committee, the PODS Executive Committee, DBCares, or this year's D&I co-chairs Pnar Tzn (pito@itu.dk) and Renata Borovica-Gajic (renata.borovica@unimelb.edu.au). Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. 22, Issue 2. We expect ~60 attendees. PLOS ONE (impact factor: 3.534), vo. Submit to:https://openreview.net/group?id=AAAI.org/2022/Workshop/AdvML, Yinpeng Dong (dyp17@mails.tsinghua.edu.cn, 30 Shuangqing Road, Haidian District, Tsinghua University, Beijing, China, 100084, Phone: +86 18603303421), Yinpeng Dong (Tsinghua University, dyp17@mail.tsinghua.edu.cn), Tianyu Pang (Tsinghua University, pty17@mails.tsinghua.edu.cn), Xiao Yang (Tsinghua University, yangxiao19@mails.tsinghua.edu.cn), Eric Wong (MIT, wongeric@mit.edu), Zico Kolter (CMU, zkolter@cs.cmu.edu), Yuan He (Alibaba, heyuan.hy@alibaba-inc.com ). Papers will be submitted to OpenReview system: Waiting for approval,https://openreview.net/forum?id=6uMNTvU-akO, Workshop Chair:Parisa Kordjamshidi, +1-2174187004, kordjams@msu.edu, Organizing Committee:Parisa Kordjamshidi (Michigan State University, kordjams@msu.edu), Behrouz Babaki (Mila/HEC Montreal, behrouz.babaki@mila.quebec), Sebastijan Dumani (KU Leuven, sebastijan.dumancic@cs.kuleuven.be), Alex Ratner (University of Washington, ajratner@cs.washington.edu), Hossein Rajaby Faghihi (Michigan State University, rajabyfa@msu.edu), Hamid Karimian (Michigan State University, karimian@msu.edu), Organizing Committee:Dan Roth (University of Pennsylvania, danroth@seas.upenn.edu) and Guy Van Den Broeck (University of California Los Angeles, guyvdb@cs.ucla.edu), Supplemental workshop site:https://clear-workshop.github.io. 47, no. Liang Zhao, Jiangzhuo Chen, Feng Chen, Fang Jin, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. Liming Zhang, Liang Zhao, Dieter Pfoser, Shan Qin and Chen Ling. Full (8 pages) and short (4 pages, work in progress) papers, AAAI style. However, FL also faces multiple challenges that may potentially limit its applications in real-world use scenarios. Knowledge Discovery and Data Mining. SIAM International Conference on Data Mining (SDM 2023) (Acceptance Rate: 27.4%), accepted. Accepted papers will not be archived but will be hosted on the workshop website. Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors. 2020. Transformations in many fields are enabled by rapid advances in our ability to acquire and generate data. Submissions of technical papers can be up to 7 pages excluding references and appendices. Liang Zhao, Junxiang Wang, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. Papers more suited for a poster, rather than a presentation, would be invited for a poster session. For program deadlines, click on the Admissions and Regulations tab on the specific page of study. Award for Artificial Intelligence for the Benefit of Humanity, Patrick Henry Winston Outstanding Educator Award, A Report to ARPA on Twenty-First Century Intelligent Systems, The Role of Intelligent Systems in the National Information Infrastructure, Code of Conduct for Conferences and Events, Request to Reproduce Copyrighted Materials, AAAI Conference on Artificial Intelligence, W1: Adversarial Machine Learning and Beyond, W2: AI for Agriculture and Food Systems (AIAFS), W6: AI in Financial Services: Adaptiveness, Resilience & Governance, W7: AI to Accelerate Science and Engineering (AI2ASE), W8: AI-Based Design and Manufacturing (ADAM) (Half-Day), W9: Artificial Intelligence for Cyber Security (AICS)(2-Day), W10: Artificial Intelligence for Education (AI4EDU), W11: Artificial Intelligence Safety (SafeAI 2022)(1.5-Day), W12: Artificial Intelligence with Biased or Scarce Data, W13: Combining Learning and Reasoning: Programming Languages, Formalisms, and Representations (CLeaR), W14: Deep Learning on Graphs: Methods and Applications (DLG-AAAI22), W15: DE-FACTIFY :Multi-Modal Fake News and Hate-Speech Detection, W16: Dialog System Technology Challenge (DSTC10), W17: Engineering Dependable and Secure Machine Learning Systems (EDSMLS 2022) (Half-Day), W18: Explainable Agency in Artificial Intelligence, W19: Graphs and More Complex Structures for Learning and Reasoning (GCLR), W21: Human-Centric Self-Supervised Learning (HC-SSL), W22: Information-Theoretic Methods for Causal Inference and Discovery (ITCI22), W23: Information Theory for Deep Learning (IT4DL), W25: Knowledge Discovery from Unstructured Data in Financial Services (Half-Day), W26: Learning Network Architecture during Training, W27: Machine Learning for Operations Research (ML4OR) (Half-Day), W28: Optimal Transport and Structured Data Modeling (OTSDM), W29: Practical Deep Learning in the Wild (PracticalDL2022), W30: Privacy-Preserving Artificial Intelligence, W31: Reinforcement Learning for Education: Opportunities and Challenges, W32: Reinforcement Learning in Games (RLG), W33: Robust Artificial Intelligence System Assurance (RAISA) (Half-Day), W34: Scientific Document Understanding (SDU) (Half-Day), W35: Self-Supervised Learning for Audio and Speech Processing, W36: Trustable, Verifiable and Auditable Federated Learning, W38: Trustworthy Autonomous Systems Engineering (TRASE-22), W39: Video Transcript Understanding (Half-Day), https://openreview.net/group?id=AAAI.org/2022/Workshop/AdvML, https://openreview.net/group?id=AAAI.org/2022/Workshop/AIAFS, https://easychair.org/conferences/?conf=aaai-2022-workshop, https://rail.fzu.edu.cn/info/1014/1064.htm, https://aaai.org/Conferences/AAAI-22/aaai22call/, https://sites.google.com/view/aaaiwfs2022, https://www.aaai.org/Publications/Templates/AuthorKit22.zip, https://openreview.net/group?id=AAAI.org/2022/Workshop/ADAM, https://easychair.org/conferences/?conf=aics22, https://cmt3.research.microsoft.com/AIBSD2022, https://aibsdworkshop.github.io/2022/index.html, https://openreview.net/forum?id=6uMNTvU-akO, https://easychair.org/conferences/?conf=dlg22, https://deep-learning-graphs.bitbucket.io/dlg-aaai22/, https://cmt3.research.microsoft.com/DSTC102022, https://dstc10.dstc.community/calls_1/call-for-workshop-papers, https://easychair.org/my/conference?conf=edsmls2022, https://sites.google.com/view/edsmls-2022/home, https://sites.google.com/view/eaai-ws-2022/call, https://sites.google.com/view/eaai-ws-2022/topic, https://sites.google.com/view/gclr2022/submissions, https://cmt3.research.microsoft.com/AAAI2022HCSSL/Submission/Index, https://cmt3.research.microsoft.com/ITCI2022, https://easychair.org/conferences/?conf=it4dl, https://easychair.org/conferences/?conf=imlaaai22, https://sites.google.com/view/aaai22-imlw, https://easychair.org/conferences/?conf=kdf22, Learning Network Architecture During Training, https://cmt3.research.microsoft.com/OTSDM2022, https://cmt3.research.microsoft.com/PracticalDL2022, https://cmt3.research.microsoft.com/PPAI2022, https://easychair.org/conferences/?conf=rl4edaaai22, https://sites.google.com/view/raisa-2022/, https://sites.google.com/view/sdu-aaai22/home, https://cmt3.research.microsoft.com/SAS2022, https://easychair.org/conferences/?conf=fl-aaai-22, http://federated-learning.org/fl-aaai-2022/, https://cmt3.research.microsoft.com/TAIH2022, https://easychair.org/conferences/?conf=trase2022, https://easychair.org/my/conference?conf=vtuaaai2022, Symposium on Educational Advances in Artificial Intelligence (EAAI-22), Conference on Innovative Applications of Artificial Intelligence (IAAI-22). Workshops are one day unless otherwise noted in the individual descriptions. The goal of this workshop is to bring together the causal inference, artificial intelligence, and behavior science communities, gathering insights from each of these fields to facilitate collaboration and adaptation of theoretical and domain-specific knowledge amongst them. We will end the workshop with a panel discussion by top researchers in the field. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), vol. The AAAI author kit can be downloaded from:https://www.aaai.org/Publications/Templates/AuthorKit22.zip. Geoinformatica, (impact factor: 2.392), Volume 20, Issue 4, pp 765-795, Oct 2016. : Papers must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template. Papers can be submitted here as an extended abstract (4 pages limit excluding references) or a short paper (6 pages limit excluding references). The submission website ishttps://cmt3.research.microsoft.com/PPAI2022. Graph neural networks on node-level, graph-level embedding, Joint learning of graph neural networks and graph structure, Learning representation on heterogeneous networks, knowledge graphs, Deep generative models for graph generation/semantic-preserving transformation, Graph2seq, graph2tree, and graph2graph models, Spatial and temporal graph prediction and generation, Learning and reasoning (machine reasoning, inductive logic programming, theory proving), Natural language processing (information extraction, semantic parsing, text generation), Bioinformatics (drug discovery, protein generation, protein structure prediction), Reinforcement learning (multi-agent learning, compositional imitation learning), Financial security (anti-money laundering), Cybersecurity (authentication graph, Internet of Things, malware propagation), Geographical network modeling and prediction (Transportation and mobility networks, social networks), Computer vision (object relation, graph-based 3D representations like mesh), Lingfei Wu (JD.Com Silicon Valley Research Center),lwu@email.wm.edu, 757-634-5455, https://sites.google.com/a/email.wm.edu/teddy-lfwu/, Jian Pei (Simon Fraser University), jian_pei@sfu.ca, 778-782-6851, https://sites.google.com/view/jpei/jian-peis-homepage, Jiliang Tang (Michigan State University), tangjili@msu.edu, 408-744-2053, https://www.cse.msu.edu/~tangjili/, Yinglong Xia (Facebook AI), yinglongxia@gmail.com, 213-309-9908, https://sites.google.com/site/yinglongxia/, Xiaojie Guo (JD.Com Silicon Valley Research Center), Xguo7@gmu.edu, 571-224-5527, https://sites.google.com/view/xiaojie-guo-personal-site, Sutanay Choudhury (Pacific Northwest National Lab), Stephan Gnnemann (Technical University of Munich), Shen Wang, (University of Illinois at Chicago), Yizhou Sun (University of California, Los Angeles), Lingfei Wu (JD.Com Silicon Valley Research Center), Zhan Zheng (Washington University in St. Louis), Feng Chen (University at Albany State University of New York), Development of corpora and annotation guidelines for multimodal fact checking, Computational models for multimodal fact checking, Development of corpora and annotation guidelines for multimodal hate speech detection and classification, Computational models for multimodal hate speech detection and classification, Analysis of diffusion of Multimodal fake news and hate speech in social networks, Understanding the impact of the hate content on specific groups (like targeted groups), Fake news and hate speech detection in low resourced languages, Vulnerability, sensitivity and attacks against ML, Adversarial ML and adversary-based learning models, Case studies of successful and unsuccessful applications of ML techniques, Correctness of data abstraction, data trust, Choice of ML techniques to meet security and quality, Size of the training data, implied guaranties, Application of classical statistics to ML systems quality, Sensitivity to data distribution diversity and distribution drift, The effect of labeling costs on solution quality (semi-supervised learning), Software engineering aspects of ML systems and quality implications, Testing of the quality of ML systems over time, Quality implication of ML algorithms on large-scale software systems, Explainable/Interpretable Machine Learning, Fairness, Accountability and Transparency, Interactive Teaching Strategies and Explainability, Novel Research Contribution describing original methods and/or results (6 pages plus references), Surveys summarizing and organizing recent research results (up to 8 pages plus references), Demonstrations detailing applications of research findings, and/or debating relevant challenges and issues in the field (4 pages plus references), Constraint satisfaction and programming (CP), (inductive) logic programming (LP and ILP), Learning with Multi-relational graphs (alignment, knowledge graph construction, completion, reasoning with knowledge graphs, etc. KDD 2022 : Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. An Invertible Graph Diffusion Model for Source Localization. As far as we know, we are the first workshop to focus on practical deep learning in the wild for AI, which is of great significance. The workshop welcomes the submission of work on, but not limited to, the following research directions. Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, and Yanfang Ye. It is one of the key bottlenecks for financial services companies to improve their operating productivity. PDF suitable for ArXiv repository (4 to 8 pages). Topics include but not limited to: Large-scale and novel targeting technologies, Fraud, fairness, explainability and privacy, Intelligent assistants in job hunting and hiring automation, Large-scale and high performing data infrastructure, data analysis and tooling, Economics and causal inference in online jobs marketplace, Large-scale analytics of user behaviors in online jobs marketplace. "Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling." "Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning." Registration Opens: Feb 02 '22 02:00 PM UTC: Registration Cancellation Refund Deadline: Apr 18 '22(Anywhere on Earth) Paper Submissions Abstract Submission Deadline: Sep 29 '21 12:00 AM UTC: Paper Submission deadline: Oct 06 '21 12:00 AM . The objective of this workshop is to discuss the winning submissions of the Submissions to the Amazon KDD Cup 2022 issingle-blind (author names and affiliations should be listed). What approaches emerge in building fundamentally robust and adaptive AI/ML systems? Industry-wide reports highlight large-scale remediation efforts to fix the failures and performance issues. It leverages many emerging privacy-preserving technologies (SMC, Homomorphic Encryption, differential privacy, etc.) "How events unfold: spatiotemporal mining in social media." Track 1 covers the issues and algorithms pertinent to general online marketplaces as well as specific problems and applications arising from those diverse domains, such as ridesharing, online retail, food delivery, house rental, real estate, and more. The financial services industry relies heavily on AI and Machine Learning solutions across all business functions and services. Panel discussion: Interactive Q&A session with a panel of leading researchers. Scott E. Fahlman, School of Computer Science, Carnegie Mellon University (sef@cs.cmu.edu), Edouard Oyallon, Sorbonne Universit LIP6 (Edouard.oyallon@lip6.fr), Dean Alderucci, School of Computer Science, Carnegie Mellon University, (dalderuc@cs.cmu.edu). The workshop will include several technical sessions, a virtual poster session where presenters can discuss their work, to further foster collaborations, multiple invited speakers covering crucial aspects for the practical deep learning in the wild, especially the efficient and robust deep learning, some tutorial talks, the challenge for efficient deep learning and solution presentations, and will conclude with a panel discussion. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. How can we develop solid technical visions and new paradigms about AI Safety? Accepted submissions will be notified latest by August 7th, 2022. Dazhou Yu, Guangji Bai, Yun Li, and Liang Zhao. Junxiang Wang, Junji Jiang, Liang Zhao. We consider submissions that havent been published in any peer-reviewed venue (except those under review). Ting Hua, Chandan Reddy, Lijing Wang, Liang Zhao, Lei Zhang, Chang-Tien Lu, and Naren Ramakrishnan. All these changes require novel solutions, and the AI community is well-positioned to provide both theoretical- and application-based methods and frameworks. The following paper categories are welcome: Submission site:https://sites.google.com/view/eaai-ws-2022/call, Silvia Tulli (Dept. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), regular paper (acceptance rate: 8.9%), Singapore, Dec 2018, accepted. We collaborate with Saudi Aramco to use machine learning for simulating oil and water flows, .