Current rates of progress are insufficient, making it impossible to meet this goal without a technological paradigm shift. Dr. Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in the COVID-19 Era and Beyond. 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. The goal of this workshop is to offer an opportunity to appreciate the diversity in applications, to draw connections to inform decision optimization across different industries, and to discover new problems that are fundamental to marketplaces of different domains. Topics of interest in the biomedical space include: Topics of general interest to cyber-security include: Submission site:https://easychair.org/conferences/?conf=aics22, Tamara Broderick (MIT CSAIL, tamarab@mit.edu), James Holt (Laboratory for Physical Sciences, USA, holt@lps.umd.edu), Edward Raff (Booz Allen Hamilton, USA, Raff_Edward@bah.com), Ahmad Ridley (National Security Agency), Dennis Ross (MIT Lincoln Laboratory, USA, dennis.ross@ll.mit.edu), Arunesh Sinha (Singapore Management University, Singapore, aruneshs@smu.edu.sg), Diane Staheli (MIT Lincoln Laboratory, USA, diane.staheli@ll.mit.edu), William W. Streilein (MIT Lincoln Laboratory, USA, wws@ll.mit.edu), Milind Tambe (Harvard University, USA, milind_tambe@harvard.edu), Yevgeniy Vorobeychik (Washington University in Saint Louis, USA, eug.vorobey@gmail.com) Allan Wollaber (MIT Lincoln Laboratory, USA, Allan.Wollaber@ll.mit.edu), Supplemental workshop site:http://aics.site/. Jos Miguel Hernndez-Lobato, University of CambridgeProf. In the financial services industry particularly, a large amount of financial analysts work requires knowledge discovery and extraction from different data sources, such as SEC filings and industry reports, etc., before they can conduct any analysis. In recent months/years, major global shifts have occurred across the globe triggered by the Covid pandemic. It does not store any personal data. 11, 2022: We have posted the list of accepted Workshops at, Apr. Roco Mercado, Massachusetts Institute of Technology. Submission site:https://cmt3.research.microsoft.com/AAAI2022HCSSL/Submission/Index, Ali Etemad (Queens University, ali.etemad@queensu.ca), Ali Etemad (Queens University, ali.etemad@queensu.ca), Ahmad Beirami (Facebook AI, ahmad.beirami@gmail.com), Akane Sano (Rice University, akane.sano@rice.edu), Aaqib Saeed (Philips Research & University of Cambridge, aqibsaeed@protonmail.com), Alireza Sepas-Moghaddam (Socure, alireza.sepasm@socure.com), Mathilde Caron (Inria & Facebook AI, mathilde@fb.com), Pritam Sarkar (Queens University & Vector Institute, pritam.sarkar@queensu.ca), Huiyuan Yang (Rice University, hy48@rice.edu), Supplemental website:https://hcssl.github.io/AAAI-22/. The 21st Web Conference (WWW 2022), (Acceptance Rate: 17.7%), accepted. in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), applied data science track, accepted (acceptance rate: 19.9%), pp. Please refer to the KDD 2022 website for the policies of Conflict of Interest, Violations of Originality, and Dual Submission: A Best Paper Award will be presented to the best full paper as voted by the reviewers. Zishan Gu, Ke Zhang, Guangji Bai, Liang Chen, Liang Zhao, Carl Yang. Submission Site: See the webpagehttps://sites.google.com/view/gclr2022/submissions; for detailed instructions and submission link. The official dates for submitting an application are detailed below, but see the exact deadline posted on the Description Page for the program of study. We consider submissions that havent been published in any peer-reviewed venue (except those under review). (Depending on the volume of submissions, we may be able to accommodate only a subset of them.). The full-day workshop will start with an opening remark followed by long research paper presentations in the morning. System reports should also follow the AAAI 2022 formatting guidelines and have 4-6 pages including references. Extended abstracts should not exceed 2 pages, excluding references. [Best Paper Award]. Submit to: Papers are required to submit to:https://easychair.org/conferences/?conf=dlg22. Application fees are not refundable. The AAAI template https://aaai.org/Conferences/AAAI-22/aaai22call/ should be used for all submissions. Submissions will undergo double blind review. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022. Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. ACM, New York, NY, USA, 10 pages. We invite submissions of technical papers up to 7 pages excluding references and appendices. Neil T. Heffernan, Worcester Polytechnic Institute (Worcester, MA, USA), Andrew S. Lan, University of Massachusetts Amherst (Amherst, MA, USA), Anna N. Rafferty, Carleton College (Northfield, MN, USA), Adish Singla, Max Planck Institute for Software Systems (Saarbrucken, Germany). Your Style Your Identity: LeveragingWriting and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network, The Web Conference (WWW 2019), short paper, (acceptance rate: 20%), accepted, 2019. Representation Learning on Spatial Networks. Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao. Chen Ling, Carl Yang, and Liang Zhao. : Papers must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template. Moreover, to tackle and overcome several issues in personalized healthcare, information technology will need to evolve to improve communication, collaboration, and teamwork among patients, their families, healthcare communities, and care teams involving practitioners from different fields and specialties. Expected attendance is 40-50 people. Our intent is to facilitate new AI/ML advances for core engineering design, simulation, and manufacturing. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. July 21: Clarified that the workshop this year will be held in-person. 2022. Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng and Liang Zhao. Liang Gou, Bosch Research (IEEE VIS liaison), Claudia Plant, University of Vienna (KDD liaison), Alvitta Ottley, Washington University, St. Louis, Junming Shao, University of Electronic Science and Technology of China, Visualization in Data Science (VDS at ACM KDD and IEEE VIS), Visualization in Data Science (VDS at ACM KDD and IEEE VIS). A Systematic Survey on Deep Generative Models for Graph Generation. Computers & Electrical Engineering (impact factor: 2.189), vo. Videos have become an omnipresent source of knowledge: courses, presentations, conferences, documentaries, live streams, meeting recordings, vlogs. Deadline in your local America/New_York timezone: Deadline in timezone from conference website: DASFAA 2022. Please refer and submit through theLearning Network Architecture During Trainingworkshop website, which has more detailed information. 2999-3006, New Orleans, US, Feb 2018. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. The submission website ishttps://cmt3.research.microsoft.com/OTSDM2022. 5 (2014): 1447-1459. Interpretable Molecular Graph Generation via Monotonic Constraints. for causal estimation in behavioral science. Liang Zhao. Integration of logical inference in training deep models. Submissions including full papers (6-8 pages) and short papers (2-4 pages) should be anonymized and follow the AAAI-22 Formatting Instructions (two-column format) at https://www.aaai.org/Publications/Templates/AuthorKit22.zip. Papers will be peer-reviewed and selected for spotlight and/or poster presentation at the workshop. with other vehicles via vehicular communication systems (e.g., dedicated short range communication (DSRC), vehicular ad hoc networks (VANETs), long term evolution (LTE), and 5G/6G mobile networks) for cooperation. Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao. Knowledge Discovery and Data Mining is an interdisciplinary area focusing The workshop aims at bridging formalisms for learning and reasoning such as neural and symbolic approaches, probabilistic programming, differentiable programming, Statistical Relation Learning and using non-differentiable optimization in deep models. Xuchao Zhang, Xian Wu, Fanglan Chen, Liang Zhao, Chang-Tien Lu. In some programs, spots may be available after the deadlines. 1059-1072, May 1 2017. We also use third-party cookies that help us analyze and understand how you use this website. Although machine learning (ML) approaches have demonstrated impressive performance on various applications and made significant progress for AI, the potential vulnerabilities of ML models to malicious attacks (e.g., adversarial/poisoning attacks) have raised severe concerns in safety-critical applications. 2022. 9, no. The research contributions may discuss technical challenges of reading and interpreting business documents and present research results. Rabat, Morocco . We will end the workshop with a panel discussion by invited speakers from different fields to enlist future directions. KDD 2023 August 06-10, 2023. With this in mind, we welcome relevant contributions on the following (and related) topic areas: The submissions must be in PDF format, written in English, and formatted according to the AAAI camera-ready style. Saliency-regularized Deep Multi-task Learning. ), responsible development of human-centric SSL (e.g., safety, limitations, societal impacts, and unintended consequences), ethical and legal implications of using SSL on human-centric data, implications of SSL on robustness and fairness, implications of SSL on privacy and security, interpretability and explainability of human-centric SSL frameworks, if your work broadly addresses the use of unlabeled human-centric data with unsupervised or semi-supervised learning, if your work focuses on architectures and frameworks for SSL for sensory data beyond CV and NLP (but not necessarily human-centric data). Handwritten recognition in business documents. Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. Three specific roles are part of this format: session chairs, presenters and paper discussants. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. How can the financial services industry balance the regulatory compliance and model governance pressures with adaptive models, Methods to combine scientific knowledge and data to build accurate predictive models, Adaptive experiment design under resource constraints, Learning cheap surrogate models to accelerate simulations, Learning effective representations for structured data, Uncertainty quantification and reasoning tools for decision-making, Explainable AI for both prediction and decision-making, Integrating AI tools into existing workflows, Challenges in applying and deployment of AI in the real-world. Recently developed tools and cutting-edge methodologies coming from the theory of optimal transport have proved to be particularly successful for these tasks. Algorithms for secure and privacy-aware machine learning for AI. SUPERB is a benchmarking platform that allows the community to train, evaluate, and compare the speech representations on diverse downstream speech processing tasks. Deep Spatial Domain Generalization. Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu. The workshop combines several disciplines, including ML, software engineering (with emphasis on quality), security, and game theory. Disease Contact Network. Oral presentations: 10 minute presentation for oral papers. Innovation, Service, and Rising Star Awards. How to do good research, Get it published in SIGKDD and get it cited! the 33rd Annual Computer Security Applications Conference (ACSAC 2018), (acceptance rate: 20.1%), San Juan, Puerto Rico, USA, Dec 2018, accepted. SIAM International Conference on Data Mining (SDM 2023) (Acceptance Rate: 27.4%), accepted. In addition to the keynote and presentations of accepted works, the workshop will include both a general discussion session on defining and addressing the key challenges in this area , and a lightning tutorial session that will include brief overviews and demos of relevant tools, including open source frameworks such as Ecole. Molecules, (impact factor: 4.411), accepted. Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. P. 6205, succursale Centre-villeMontral, (Qubec) H3C 3T5Canada. Spatial Event Forecasting in Social Media with Geographically Hierarchical Regularization. We send a public call and we assume the workshop will be of interest to many AAAI main conference audiences; we expect 50 participants. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022. PDF suitable for ArXiv repository (4 to 8 pages). Some will be selected for spotlight talks, and some for the poster session. AAAI is pleased to present the AAAI-22 Workshop Program. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. Half day event featuring a panel, invited and keynote speakers and presentations selected through a CFP. Submissions of technical papers can be up to 7 pages excluding references and appendices. KDD 2022 is a dual-track conference that provides distinct programming in research and applied data science. Novel algorithmic solutions to causal inference or discovery problems using information-theoretic tools or assumptions. 11-13. The Institute for Operations Research and the Management Sciences, [Submission deadline extended, June 3] KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail, and Beyond, We are excited to announce our upcoming workshop at. Call for Participation The 3rd KDD Workshop on Data-driven Humanitarian Mapping and Policymaking solicits research papers, case studies, vision papers, software demos, and extended abstracts. 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. Deadline in . Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted. We welcome submissions of long (max. Novel approaches and works in progress are encouraged. Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye, Liang Zhao. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. We invite the submission of original and high-quality research papers in the topics related to biased or scarce data. Yuyang Gao, Giorgio Ascoli, Liang Zhao. Creative Commons Attribution-Share Alike 3.0 License, 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 25TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, Knowledge Discovery and Data Mining Conference, 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 18th ACM SIGKDD Knowledge Discovery and Data Mining, The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Note: The workshop is a collaboration between NASSMA organisation, Deepmind and UM6P. Everyone in the Top-10 leaderboard submissions will have a guaranteed opportunity for an in-person oral/poster presentation. Thank you for all your contributions, our, Paper submission deadline is now extended to. Submissions may consist of up to 7 pages of technical content plus up to two additional pages solely for references. The workshop is being organized by application area or other, panels, invited speakers, interactive, small groups, discussions, presentations. 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 . Please specify the length of the workshop (1-day, 1.5-day, 2-day, or half-day. Atlanta, Georgia, USA . Optimal transport-based analysis of structured data, such as networks, meshes, sequences, and so on; The applications of optimal transport in molecule analysis, network analysis, natural language processing, computer vision, and bioinformatics. Integration of Deep Learning and Relational Learning. By clicking Accept All, you consent to the use of ALL the cookies. Yuanqi du, George Mason University, USA; Jian Pei, Simon Fraser University, Canada; Charu Aggarwal, IBM Research AI, USA; Philip S. Yu, University of Illinois at Chicago, USA; Xuemin Lin, University of New South Wales, Australia; Jiebo Luo, University of Rochester, USA; Lingfei Wu, JD.Com Silicon Valley Research Center, USA; Yinglong Xia, Facebook AI, USA; Jiliang Tang, Michigan State University, USA; Peng Cui, Tsinghua University, China; William L. Hamilton, McGill University, Canada; Thomas Kipf, University of Amsterdam, Netherlands, Workshop URL:https://deep-learning-graphs.bitbucket.io/dlg-aaai22/.