December, 09-12, 2022. robust and interpretable natural language processing for healthcare. By clicking Accept All, you consent to the use of ALL the cookies. 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. 105, no. At least one author of each accepted submission must be present at the workshop. Pengtao Xie (main contact), Assistant Professor, University of California, San Diego, pengtaoxie2008@gmail.com Engineer Ln, San Diego, CA 92161 (Tel)4123206230, Marinka Zitnik, Assistant Professor, Harvard University, marinka@hms.harvard.edu 10 Shattuck Street, Boston, MA 02115 (Tel)6503086763, Byron Wallace, Assistant Professor, Northeastern University, byron@ccs.neu.edu 177 Huntington Ave, Boston, MA 02115 (Tel)4135120352, Eric P. Xing, Professor, Carnegie Mellon University, epxing@cs.cmu.edu 5000 Forbes Ave, Pittsburgh, PA 15213 (Tel)4122682559, Ramtin Hosseini, PhD Student, University of California, San Diego, rhossein@eng.ucsd.edu (Tel) 3104293825, Ethics and fairness in autonomous systems, Robust robotic design, particularly of autonomous drones and/or vehicles. 2022. Checklist for Revising a SIGKDD Data Mining Paper, How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering, https://researcher.watson.ibm.com/researcher/view_group.php?id=144, IEEE International Conference on Big Data (, AAAI Conference on Artificial Intelligence (, IEEE International Conference on Data Engineering (, SIAM International Conference on Data Mining (, Pacific-Asia Conference on Knowledge Discovery and Data Mining (, ACM SIGKDD International Conference on Knowledge discovery and data mining (, European Conference on Machine learning and knowledge discovery in databases (, ACM International Conference on Information and Knowledge Management (, IEEE International Conference on Data Mining (, ACM International Conference on Web Search and Data Mining (, 18.4% (181/983, research track), 22.5% (112/497, applied data science track), 59.1% (107/181, research track), 35.7% (40/112, applied data science track), 17.4% (130/748, research track), 22.0% (86/390, applied data science track), 49.2% (64/130, research track), 41.9% (36/86, applied data science track), 18.1% (142/784, research track), 19.9% (66/331, applied data science track), 49.3% (70/142, research track), 60.1% (40/66, applied data science track), 18.5% (194/1046, overall), 9.1% (95/?, regular paper), ?% (99/?, short paper), 19.8% (188/948, overall), 8.9% (84/?, regular paper), ?% (104/?, short paper), 19.9% (155/778, overall), 9.3% (72/?, regular paper), ?% (83/?, short paper), 19.6% (178/904, overall), 8.6% (78/?, regular paper), ?% (100/?, short paper), 19.6% (202/1031, long paper), 22.7% (107/471, short paper), 21.8% (38/174m applied research), 17% (147/826, long paper), 23% (96/413, short paper), 25% (demo), 34% (industry paper), Short papers are presented at poster sessions, 20% (171/855, long paper), 28% (119/419, short paper), 38% (30/80, demo paper), 23% (160/701, long paper), 24% (55/234, short paper), 54 extended short papers (6 pages), 26% (94/354, research track), 26% (37/143, applied ds track), 15% (23/151, journal track), 27.8% (164/592, overall), 9.8% (58/592, long presentation), 18.1% (107/592, regular), 28.2% (129/458, overall), 9.8% (45/458, long presentation), 18.3% (84/458, regular), 29.6% (91/307, overall), 12.7% (39/307, long presentation), 16.9% (52/307, regular), 40.4% (34/84, long presentation), 59.5% (50/84, short presentation)^, 16.3% (84/514 in which 3 papers are withdrawn/rejected after the acceptance), 28.4% (23/81, long presentation), 71.6% (58/81, short presentation)^, 30% (24/80, long presentation), 70% (56/80, short presentation)^, 29.8% (20/67, long presentation), 70.2% (47/67, short presentation)^, 53.8% (21/39, long presentation), 46.2% (18/39, short presentation)^. The advances in web science and technology for data management, integration, mining, classification, filtering, and visualization has given rise to a variety of applications representing real-time data on epidemics. This workshop aims to provide a premier interdisciplinary forum for researchers in different communities to discuss the most recent trends, innovations, applications, and challenges of optimal transport and structured data modeling. Thirty-First AAAI Conference on Artificial Intelligence, pp. It is also central for tackling decision-making problems such as reinforcement learning, policy or experimental design. Because of the time needed to complete the formalities for entering Canada and Quebec, the admission period for international applicants ends several weeks before the session begins. We plan to invite 2-4 keynote speakers from prestigious universities and leading industrial companies. ML-guided rare event modeling and system uncertainty quantification, Development of software, libraries, or benchmark datasets, and. In addition, broad deployment of ML software in networked systems inevitably exposes ML software to attacks. For example, AI tools are built to ease the workload for teachers. The research contributions may discuss technical challenges of reading and interpreting business documents and present research results. Jos Miguel Hernndez-Lobato, University of CambridgeProf. [slides] 2020. Zirui Xu, Fuxun Xu, Liang Zhao, and Xiang Chen. The post-launch session includes the invited talks, shared task winners presentations, and a panel discussion on the resources, findings, and upcoming challenges. Papers will be submitted electronically using Easychair. AI is now shaping the way businesses, governments, and educational institutions do things and is making its way into classrooms, schools and districts across many countries. Accepted papers will be given the opportunity to present at the spotlight sessions during the workshop. Yanfang Ye, Yiming Zhang, Yujie Fan, Chuan Shi and Liang Zhao. California, United Stes. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye, Liang Zhao. It will include multiple keynote speakers, invited talks, a panel discussion, and two poster sessions for the accepted papers. Some existing research also presents that there is a trade-off between the robustness and accuracy of deep learning models. Submissions must be formatted in the AAAI submission format (https://www.aaai.org/Publications/Templates/AuthorKit22.zip) All submissions should be done electronically via EasyChair. You also have the option to opt-out of these cookies. 2022. About 7-8 invited speakers who are distinguished professional in Deep learning on graph will present the frontier research topics. 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). Dialog systems and related technologies, including natural language processing, audio and speech processing, and vision information processing. Saliency-regularized Deep Multi-task Learning. ), The workshop will be organized as half-day event with 2 invited speakers, follow by presentation from accepted papers (both ordinary papers, and shared task paper). 17th International Workshop on Mining and Learning with Graphs. We invite workshop participants to submit their original contributions following the AAAI format through EasyChair. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. The program consists of poster sessions for accepted papers, and invited and spotlight talks. "Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning." The 11th International Conference on Learning Representations (ICLR 2023), accepted. We expect ~60 attendees. In Proceedings of the IEEE International Conference on Big Data (BigData 2014), pp. "Spatiotemporal Event Forecasting in Social Media." Videos have become an omnipresent source of knowledge: courses, presentations, conferences, documentaries, live streams, meeting recordings, vlogs. All submissions will be peer-reviewed. This cookie is set by GDPR Cookie Consent plugin. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 20%=174/870), short paper, to appear, 2022. The challenge requires participants to build competitive models for diverse downstream tasks with limited labeled data and trainable parameters, by reusing self-supervised pre-trained networks. Ourprevious workshop at AAAI-21generated significant interest from the community. Automatic fact/claim verification has recently become a topic of interest among diverse research communities. Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. Scientific documents such as research papers, patents, books, or technical reports are one of the most valuable resources of human knowledge. Advances in complex engineering systems such as manufacturing and materials synthesis increasingly seek artificial intelligence/machine learning (AI/ML) solutions to enhance their design, development, and production processes. The aim of this workshop is to focus on both original research and review articles on various disciplines of ITS applications, including particularly AI techniques for ITS time-series data analyses, ITS spatio-temporal data analyses, advanced traffic management systems, advanced traveler information systems, commercial vehicle operation systems, advanced vehicle control and safety systems, advanced public transportation services, advanced information management services, etc. The workshop will focus on two thrusts: 1) Exploring how we can leverage recent advances in RL methods to improve state-of-the-art technology for ED; 2) Identifying unique challenges in ED that can help nurture technical innovations and next breakthroughs in RL. Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. and deep learning techniques (e.g. Research track papers reporting the results of ongoing or new research, which have not been published before. Papers must be in PDF format, in English, and formatted according to the AAAI template. Papers more suited for a poster, rather than a presentation, would be invited for a poster session. OARS-KDD2022: KDD 2022 Workshop on Online and Adaptive Recommender Systems Washington DC, DC, United States, August 15, 2022 Topics: data science artificial intelligence recommender system recommendation KDD 2022 Workshop on Online and Adaptive Recommender Systems (OARS) Call For Papers ================== Public health authorities and researchers collect data from many sources and analyze these data together to estimate the incidence and prevalence of different health conditions, as well as related risk factors. As deep learning problems become increasingly complex, network sizes must increase and other architectural decisions become critical to success. Submissions will be collected via the OpenReview platform; URL forthcoming on the Workshop website. We also use third-party cookies that help us analyze and understand how you use this website. Paper Submission:November 12, 2021, 11:59 pm (anywhere on earth) Author Notification: December 3, 2021Full conference:February 22 March 1, 2022Workshop:February 28 March 1, 2022. Submissions that do not meet the formatting requirements will be rejected without review. Multi-objective Deep Data Generation with Correlated Property Control. This website uses cookies to improve your experience while you navigate through the website. Xiaojie Guo, Yuanqi Du, Liang Zhao. Dynamic Tracking and Relative Ranking of Airport Threats from News and Social Media. Our preliminary plan for the schedule is as following , DEFACTIFY@AAAI-22 Program [tentative]9:00AM-9:15AMInaugurationA brief summary of the shared tasks number of participants, best results, Session 1 multimodal fact checkingWorkshop papers 9:30AM 10:30AM, 11:00AM 12:00pmInvited talk 1 Prof. Rada Mihalcea, University of Michigan, Session 2 Best 4/5 papers from FACTIFY & MEMOTION shared taskWorkshop papers 1:00PM 2:00PM, 2:00PM 3:30PMInvited talk 2 Prof. LOUIS-PHILIPPE MORENCY, CMU, Session 2 multimodal hate speechWorkshop papers 4:00PM 5:00PM. The submission website ishttps://cmt3.research.microsoft.com/TAIH2022. Deep Geometric Neural Networks for Spatial Interpolation. Optimal transport theory, including statistical and geometric aspects; Gromov-Wasserstein distance and its variants; Bayesian inference for/with optimal transport; Gromovization of machine learning methods; Optimal transport-based generative modeling. [Best Poster Runner-Up Award]. Important Dates. You signed in with another tab or window. Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, and Chang-TIen Lu. Submissions are limited to a total of 5 pages for initial submission (up to 6 pages for final camera-ready submission), excluding references or supplementary materials, and authors should only rely on the supplementary material to include minor details that do not fit in the 5 pages. Position papers (4 pages in length for main content + 2 pages for references in AAAI format): we are seeking position papers that advocate for a particular approach or set of approaches, or present an overview of a promising relevant research area. We are interested in a broad range of topics, both foundational and applied. Novel AI-based techniques to improve modeling of engineering systems. Some will be selected for spotlight talks, and some for the poster session. The workshop also welcomes participants of SUPERB and Zero Speech challenge to submit their results. All papers must be submitted in PDF format, using the AAAI-22 author kit. VDS@VIS Submission Deadline:Thur., July 14th, 2022, 5:00 pm PDT, VDS@VIS Author Notification:Thur., August 25th, 2022, 5:00 pm PDT, VDS@KDD Submission Deadline:Thur., May 26th June 2nd, 2022, 5:00 pm PDT, VDS@KDD Author Notification:Mon., June 20th, 2022, 5:00 pm PDT. Besides academia, many companies and institutions are researching on topics specific to their particular domains. Please use vds@ieeevis.org to get in touch with us, or follow us on Twitter at @VisualDataSci. Please submit the papers and system reports toEasyChair, Thien Huu Nguyen (University of Oregon, thien@cs.uoregon.edu), Walter Chang (Adobe Research, wachang@adobe.com), Amir Pouran Ben Veyseh (University of Oregon, apouranb@uoregon.edu), Viet Dac Lai (University of Oregon, viet@uoregon.edu), Franck Dernoncourt (Adobe Research, franck.dernoncourt@adobe.com), Workshop URL:https://sites.google.com/view/sdu-aaai22/home. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. Shi, Y., Deng, M., Yang, X., Liu, Q., Zhao, L., & Lu, C. T. "A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter." The 39th IEEE International Conference on Data Engineering (ICDE 2023), accepted. Papers will be peer-reviewed and selected for spotlight and/or poster presentation at the workshop. Fang Jin, Wei Wang, Liang Zhao, Edward Dougherty, Yang Cao, Chang-Tien Lu, and Naren Ramakrishnan. These research trends inform the need to explore the intersection of AI with behavioral science and causal inference, and how they can come together for applications in the social and health sciences. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), New Orleans, US, Feb 2018, pp. Online and Distributed Robust Regressions with Extremely
Noisy Labels. https://doi.org/10. Held in conjunction with KDD'22 Aug 15, 2022 - Washington DC, USA. Topics of interest include, but are not limited to: One day, comprising keynote, paper presentations and panel sessions. Qingzhe Li, Amir A. Fanid, Martin Slawski, Yanfang Ye, Lingfei Wu, Kai Zeng, and Liang Zhao. The workshop organizers invite paper submissions on the following (and related) topics: This workshop will be a one-day workshop, featuring invited speakers, poster presentations, and short oral presentations of selected accepted papers. DI-2022 accepted papers will not be archived in the main KDD 2022 proceedings. Novel ML-accelerated optimization for conceptual/detailed system design. Attendance is open to all prior registration to the workshop/conference. Graph Neural Networks: Foundations, Frontiers, and Applications. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [1] . We especially welcome research from fields including but not limited to AI, human-computer interaction, human-robot interaction, cognitive science, human factors, and philosophy. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 9.77%), to appear, 2022. and Simone Stumpf (Univ. Authors of accepted papers will be invited to participate. We have the following keynote speakers confirmed: Andreas Holzinger (Medical Univ. Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2021), (acceptance rate: 21.0%), accepted. arXiv preprint arXiv:2302.02093 (2023). The goal of the inaugural HC-SSL workshop is to highlight and facilitate discussions in this area and expose the attendees to emerging potentials of SSL for human-centric representation learning, and promote responsible AI within the context of SSL. Submit to: Submissions should be made via EasyChair athttps://easychair.org/conferences/?conf=it4dl, Jose C. Principe (University of Florida, principe@cnel.ufl.edu), Robert Jenssen (UiT The Arctic University of Norway, robert.jenssen@uit.no), Badong Chen (Xian Jiaotong University, chenbd@mail.xjtu.edu.cn), Shujian Yu (UiT The Arctic University of Norway, yusj9011@gmail.com), Supplemental workshop site:https://www.it4dl.org/. Submissions should follow the AAAI-2022https://aaai.org/Conferences/AAAI-22/aaai22call/. No supplement is allowed for extended abstracts. 1799-1808. Zero Speech challenge is to build language models only based on audio or audio-visual information, without using any textual input. Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. Winter. Submission URL:https://easychair.org/conferences/?conf=rl4edaaai22. Workshop registration is available to AAAI-22 technical registrants at a discounted rate, or separately to workshop only registrants. 12 (2014): 90-94. Xiaojie Guo and Liang Zhao. While most work on XAI has focused on opaque learned models, this workshop also highlights the need for interactive AI-enabled agents to explain their decisions and models. "Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework",The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China, accepted. Topics of interest include but are not limited to: Acronyms, i.e., short forms of long phrases, are common in scientific writing. Knowledge representation for business documents. in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019. ASPLOS 2023 will be moving to three submission deadlines. 15, pp. We will accept both original papers up to 8 pages in length (including references) as well as position papers and papers covering work in progress up to 4 pages in length (not including references).Submission will be through Easychair at the AAAI-22 Workshop AI4DO submission site, Professor Bistra Dilkina (dilkina@usc.edu), USC and Dr. Segev Wasserkrug, (segevw@il.ibm.com), IBM Research, Prof. Andrea Lodi (andrea.lodi@cornell.edu), Jacobs Technion-Cornell Institute IIT and Dr. Dharmashankar Subrmanian (dharmash@us.ibm.com), IBM Research. [materials][data]. ADMM for Efficient Deep Learning with Global Convergence. the 56th Design Automation Conference (DAC 2019), accepted, (acceptance rate: 20%), Las Vegas, US, 2019. "Automatic Targeted-Domain Spatiotemporal Event Detection in Twitter."