Given the ever-increasing role of the World Wide Web as a source of information in many domains including healthcare, accessing, managing, and analyzing its content has brought new opportunities and challenges. Proceedings of the IEEE (impact factor: 9.237), vol. Frontiers in Big Data, accepted, 2021. Liang Zhao, Ting Hua, Chang-Tien Lu, and Ing-Ray Chen. In this 2nd instance of GCLR (Graphs and more Complex structures for Learning and Reasoning) workshop, we will focus on various complex structures along with inference and learning algorithms for these structures. Integration of logical inference in training deep models. Robust Regression via Online Feature Selection under Adversarial Data Corruption. Ferdinando Fioretto (Syracuse University), Emma Frejinger (Universit de Montral), Elias B. Khalil (University of Toronto), Pashootan Vaezipoor (University of Toronto). Hua, Ting, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. The annual ACM SIGMOD/PODS Conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and . This workshop seeks to explore new ideas on AI safety with particular focus on addressing the following questions: Contributions are sought in (but are not limited to) the following topics: To deliver a truly memorable event, we will follow a highly interactive format that will include invited talks and thematic sessions. Welcome to the home of the 2023 ACM SIGMOD/PODS Conference, to be held in the Seattle metropolitan area, Washington, USA, on June 18 - June 23, 2023. Their results will be submitted in either a short paper or poster format. Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest. Integration of non-differentiable optimization models in learning. The topics of interest include, but are not limited to: The papers will be presented in poster format and some will be selected for oral presentation. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2014), industrial track, pp. Yuyang Gao, Tong Sun, Sungsoo Hong, and Liang Zhao. ISPRS International Journal of Geo-Information (IJGI), (impact factor: 1.502), 5.10 (2016): 193. Yuanqi Du, Xiaojie Guo, Yinkai Wang, Amarda Shehu, Liang Zhao. Gabriel Pedroza (CEA LIST), Jos Hernndez-Orallo (Universitat Politcnica de Valncia, Spain), Xin Cynthia Chen (University of Hong Kong, China), Xiaowei Huang (University of Liverpool, UK), Huascar Espinoza (KDT JU, Belgium), Mauricio Castillo-Effen (Lockheed Martin, USA), Sen higeartaigh (University of Cambridge, UK), Richard Mallah (Future of Life Institute, USA), John McDermid (University of York, UK), Supplemental workshop site:http://safeaiw.org/. We will end the workshop with a panel discussion by top researchers in the field. July 21: Clarified that the workshop this year will be held in-person. At least three research trends are informing insights in this field. 4 pages) papers describing research at the intersection of AI and science/engineering domains including chemistry, physics, power systems, materials, catalysis, health sciences, computing systems design and optimization, epidemiology, agriculture, transportation, earth and environmental sciences, genomics and bioinformatics, civil and mechanical engineering etc. 4 pages), and position (max. Data Mining Conferences - GitHub "Online Spatial Event Forecasting in Microblogs. What techniques and approaches can be used to detect and effectively manage similar scenarios in the future? International Journal of Digital Earth, (impact factor: 3.097), 25 Aug 2020, https://doi.org/10.1080/17538947.2020.1809723. Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features. We have invited several distinguished speakers with their research interests spanning from the theoretical to experimental aspects of complex networks. Causality has received significant interest in ML in recent years in part due to its utility for generalization and robustness. We invite the submission of original and high-quality research papers in the topics related to biased or scarce data. For questions on submission and the workshop, please send email through the following link: Track 1: Tony Qin (Lyft), Rui Song (NC State & Amazon), Hongtu Zhu (UNC), Michael Jordan (Berkeley), Track 2: Liangjie Hong (LinkedIn), Mohammed Korayem (CareerBuilder), Haiyan Luo (Indeed). The workshop will be a one-day workshop, featuring speakers, panelists, and poster presenters from machine learning, biomedical informatics, natural language processing, statistics, behavior science. [paper] Business documents are central to the operation of all organizations, and they come in all shapes and sizes: project reports, planning documents, technical specifications, financial statements, meeting minutes, legal agreements, contracts, resumes, purchase orders, invoices, and many more. Attendance is open to all registered participants. Online marketplace is a digital platform that connects buyers (demand) and sellers (supply) and provides exposure opportunities that individual participants would not otherwise have access to. There will be about 60~85 people to participate, including the program committee, invited speakers, panelists, authors of accepted papers, winners of the competition and other interested people. The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in artificial intelligence and, in particular, machine learning and optimization. 2085-2094, Aug 2016. Finally, the workshop will welcome papers that describe the release of privacy-preserving benchmarks and data sets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples. SDU is expected to host 50-60 attendees. Self-supervised learning utilizes proxy supervised learning tasks, for example, distinguishing parts of the input signal from distractors, or generating masked input segments conditioned on the unmasked ones, to obtain training data from unlabeled corpora. System reports should also follow the AAAI 2022 formatting guidelines and have 4-6 pages including references. Videos have become an omnipresent source of knowledge: courses, presentations, conferences, documentaries, live streams, meeting recordings, vlogs. 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). 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. Xuchao Zhang, Xian Wu, Fanglan Chen, Liang Zhao, Chang-Tien Lu. It highlights the importance of declarative languages that enable such integration for covering multiple formalisms at a high-level and points to the need for building a new generation of ML tools to help domain experts in designing complex models where they can declare their knowledge about the domain and use data-driven learning models based on various underlying formalisms. Yuyang Gao, Tong Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Hong, and Liang Zhao. robust and interpretable natural language processing for healthcare. 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/. Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. AI Conference Deadlines - Hyunwoo Kim All papers must be submitted in PDF format using the AAAI-22 author kit. KDD 2022 : 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Conference Series : Knowledge Discovery and Data Mining Link: https://kdd.org/kdd2022/ Call For Papers [Empty] Related Resources KDD 2023 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING Generative Adversarial Learning of Protein Tertiary Structures. Realizing the vision of Document Intelligence remains a research challenge that requires a multi-disciplinary perspective spanning not only natural language processing and understanding, but also computer vision, layout understanding, knowledge representation and reasoning, data mining, knowledge discovery, information retrieval, and more all of which have been profoundly impacted and advanced by deep learning in the last few years. 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. Conference stats are visualized below for a straightforward comparison. This workshop aims to bring together FL researchers and practitioners to address the additional security and privacy threats and challenges in FL to make its mass adoption and widespread acceptance in the community. Benchmarks to reliably evaluate attacks/defenses and measure the real progress of the field. Information-theoretic approaches provide a novel set of tools that can expand the scope of classical approaches to causal inference and discovery problems in a variety of applications. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. Liming Zhang, Liang Zhao, Dieter Pfoser, Shan Qin and Chen Ling. Papers must be between 4-8 pages in the AAAI submission format, with the eighth page containing only references. . There is now a great deal of interest in finding better alternatives to this scheme. ADMM for Efficient Deep Learning with Global Convergence. "STED: semi-supervised targeted-interest event detectionin in twitter." Note: This is the inaugural event of a conference dedicated to Graph Machine Learning. Please specify the length of the workshop (1-day, 1.5-day, 2-day, or half-day. Three categories of contributions are sought: full-research papers up to 8 pages; short papers up to 4 pages; and posters and demos up to 2 pages. We welcome the submissions in the following two formats: The submissions should adhere to theAAAI paper guidelines. Connor Coley, Massachusetts Institute of TechnologyProf. The industry session will emphasize practical industrial product developments using GNNs. Knowledge Discovery and Data Mining. It is valuable to bring together researchers and practitioners from different application domains to discuss their experiences, challenges, and opportunities to leverage cross-domain knowledge. Yanfang Ye, Yiming Zhang, Yujie Fan, Chuan Shi and Liang Zhao. "Robust Regression via Heuristic Hard Thresholding". The workshop will be co-located with the KDD 2022 conference at Washington DC Convention Center,Washington D.C., USA onAugust 17th, 2022 at1PM5PM (Eastern Standard Time). This is a one-day workshop, planned with a 10-minute opening, 6 invited keynotes, ~6 contributed talks, 2 poster sessions, and 2 panel discussions. Modeling Health Stage Development of Patients with Dynamic Attributed Graphs in Online Health Communities. 40 attendees including: invited speakers, authors of accepted papers and shared task participants. We plan to invite 2-4 keynote speakers from prestigious universities and leading industrial companies. 20, 2022: We have announced Call for Nominations: , Jan. 25, 2022: Sponsorship Opportunities is available at, Jan. 6, 2022: Call for KDD Cup Proposals is available at, Dec. 26, 2021: Call for Workshop Proposals is available at, Dec. 26, 2021: Call for Tutorials is available at, Nov. 24, 2021: Those who are interested in serving as a PC, please feel free to fill in this, Nov. 12, 2021: Call for Research Track Papers is available at, Nov. 12, 2021: Call for Applied Data Science Track Papers is available at. upon methodologies and applications for extracting useful knowledge from data [1]. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. The positive/negative social impacts and ethical issues related to adversarial ML. Handwritten recognition in business documents. The discussion in the workshop can lead to implementing FL solutions that are more accurate, robust and interpretable, and gain the trust of the FL participants. "Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design", 28th International Conference on Field Programmable Logic and Applications (FPL 2019), (acceptance rate: 18%), Barcelona, Spain, accepted. Some good examples include recommender systems, clustering, graph mining, Why did so many AI/ML models fail during the pandemic? 2020. Rex Ying's Personal Website Data science is the practice of deriving insights from data, enabled by statistical modeling, computational methods, interactive visual analysis, and domain-driven problem solving. Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features. A Report on the First Workshop on Document Intelligence (DI) at NeurIPS 2019. 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. Long Beach, California, USA . Online . in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), regular paper (acceptance rate: 8.9%), Singapore, Dec 2018, accepted. It is also central for tackling decision-making problems such as reinforcement learning, policy or experimental design. Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. KDD 2022. 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 ). Junxiang Wang, Junji Jiang, Liang Zhao. Jan 13, 2022: Notification. SIGSPATIAL Special (invited paper), vo. Detailed information could be found on the website of the workshop. 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. In recent years, we have seen examples of general approaches that learn to play these games via self-play reinforcement learning (RL), as first demonstrated in Backgammon. 29, no. TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints. 1953-1970, Oct. 2017. 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. GNES: Learning to Explain Graph Neural Networks. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13.
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