Information extraction and information retrieval for scientific documents; Question answering and question generation for scholarly documents; Word sense disambiguation, acronym identification and expansion, and definition extraction; Document summarization, text mining, document topic classification, and machine reading comprehension for scientific documents; Graph analysis applications including knowledge graph construction and representation, graph reasoning and query knowledge graphs; Biomedical image processing, scientific image plagiarism detection, and data visualization; Code/Pseudo-code generation from text and im-age/diagram captioning, New language understanding resources such as new syn-tactic/semantic parsers, language models or techniques to encode scholarly text; Survey or analysis papers on scientific document under-standing and new tasks and challenges related to each scientific domain; Factuality, data verification, and anti-science detection. Semantic understanding of business documents. Continuous refinement of AI models using active/online learning. Papers will be peer-reviewed and selected for spotlight and/or poster presentation at the workshop. 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. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. SL-VAE: Variational Autoencoder for Source Localization in Graph Information Diffusion. Distributed Self-Paced Learning in Alternating Direction Method of Multipliers. Modern interface, high scalability, extensive features and outstanding support are the signatures of Microsoft CMT. Previously published work (or under-review) is acceptable. Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. Conference stats are visualized below for a straightforward comparison. For program deadlines, click on the Admissions and Regulations tab on the specific page of study. Deep Graph Learning for Circuit Deobfuscation. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu. Why did so many AI/ML models fail during the pandemic? Despite rapid recent progress, it has proven to be challenging for Artificial Intelligence (AI) algorithms to be integrated into real-world applications such as autonomous vehicles, industrial robotics, and healthcare. The post-launch session includes the invited talks, shared task winners presentations, and a panel discussion on the resources, findings, and upcoming challenges. 1923-1935, 1 Oct. 2020, doi: 10.1109/TKDE.2019.2912187. Two types of submissions will be considered: full papers (6-8 pages + references), and short papers (2-4 pages + references). 2022. Proceedings of the IEEE (impact factor: 9.237), vol. Yevgeniy Vorobeychik (Washington University in St. Louis), Bruno Sinopoli (Washington University in St. Louis), Jinghan Yang (Washington University in St. Louis), Bo Li (UIUC), Atul Prakash (University of Michigan), Supplemental Workshop site:https://jinghany.github.io/trase2022/. Information theory has demonstrated great potential to solve the above challenges. All papers will be peer reviewed, single-blinded. Roco Mercado, Massachusetts Institute of Technology. 4, Roosevelt Rd., Taipei, TaiwanAffiliation: National Taiwan UniversityPhone: +1-412-465-0130Email: yvchen@csie.ntu.edu.tw, Paul CrookAddress: 1 Hacker Way, Menlo Park, CA, USAAffiliation: FacebookPhone: +1-650-885-0094Email: pacrook@fb.com, DSTC 10 home:https://dstc10.dstc.community/homeDSTC 10 CFPs:https://dstc10.dstc.community/calls_1/call-for-workshop-papers. Full papers: Submissions must represent original material that has not appeared elsewhere for publication and that is not under review for another refereed publication. As Artificial Intelligence (AI) begins to impact our everyday lives, industry, government, and society with tangible consequences, it becomes increasingly important for a user to understand the reasons and models underlying an AI-enabled systems decisions and recommendations. Scientific documents such as research papers, patents, books, or technical reports are one of the most valuable resources of human knowledge. Ourprevious workshop at AAAI-21generated significant interest from the community. The paper submissions must be in pdf format and use the AAAI official templates. Short or position papers of up to 4 pages are also welcome. This workshop aims to explore and advance the current state of research and practice, including but not limited to the following topics: In addition to the invited talks and the panel discussion on topics related to Document Intelligence, the workshop program will include paper sessions which provides an opportunity to present peer-reviewed work on the topic related to Document Intelligence. While the research community is converging on robust solutions for individual AI models in specific scenarios, the problem of evaluating and assuring the robustness of an AI system across its entire life cycle is much more complex. The 19th International Conference on Data Mining (ICDM 2019), short paper, (acceptance rate: 18.05%), Beijing, China, accepted. 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). Despite the great success of deep neural networks (DNNs) in many artificial intelligence (AI) tasks, they still suffer from limitations, such as poor generalization behavior for out-of-distribution (OOD) data, vulnerability to adversarial examples, and the black-box nature of DNNs. The cookie is used to store the user consent for the cookies in the category "Performance". Declarative languages and differentiable programming. arXiv preprint arXiv:2002.11867 (2021), Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao. SDU is expected to host 50-60 attendees. This workshop aims to bring together researchers from industry and academia and from different disciplines in AI and surrounding areas to explore challenges and innovations in IML. Government day with NSF, NIH, DARPA, NIST, and IARPA, Local industries in the DC Metro Area, including the Amazons second headquarter, New initiatives at KDD 2022: undergraduate research and poster session, Early career research day with postdoctoral scholars and assistant professors in a mentoring workshop and panel, Workshops and hands-on tutorials on emerging topics. We allow papers that are concurrently submitted to or currently under review at other conferences or venues. Ranking, acceptance rate, deadline, and publication tips. Liang Zhao, Yuyang Gao, Jieping Ye, Feng Chen, Fanny Ye, Chang-tien Lu, and Naren Ramakrishnan. Introduction: SIGKDD aims to provide the premier forum for advancement and adoption of the "science" of knowledge discovery and data mining.SIGKDD will encourage: basic research in KDD (through annual research conferences, newsletter and other related activities . Authors of accepted papers will be invited to participate. . 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. See ICDM Acceptance Rates for more information. This workshop has no archival proceedings. Computer Science and Engineering, INESC-ID, IST Ulisboa, Lisbon, Portugal currently at Sorbonne University, Paris, France silvia.tulli@gaips.inesc-id.pt), Prashan Madumal (Science and Information Systems, University of Melbourne, Parkville, Australia pmathugama@student.unimelb.edu.au), Mark T. Keane (School of Computer Science, University College Dublin, Dublin, Ireland mark.keane@ucd.ie), David W. Aha (Navy Center for Applied Research in AI, Naval Research Laboratory, Washington, DC, USA david.aha@nrl.navy.mil), Adam Johns (Drexel University, Philadelphia, PA USA), Tathagata Chakraborti (IBM Research AI, Cambridge, MA USA), Kim Baraka (VU University Amsterdam, Netherlands), Isaac Lage (Harvard University, Cambridge, MA USA), David Martens (University of Antwerp, Belgium), Mohamed Chetouani (Sorbonne Universit, Paris, France), Peter Flach (University of Bristol, United Kingdom), Kacper Sokol (University of Bristol, United Kingdom), Ofra Amir (Technion, Haifa, Israel), Dimitrios Letsios (Kings College London, London, United Kingdom), Supplemental workshop site:https://sites.google.com/view/eaai-ws-2022/topic. "Spatiotemporal Event Forecasting in Social Media." The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2022 (ACM SIGSPATIAL 2022) (Acceptance Rate: 23.8%), full paper track, to appear, 2022. Share. Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen. Deep Graph Translation. As deep learning problems become increasingly complex, network sizes must increase and other architectural decisions become critical to success. A message will appear on your application form if there is a risk that the time required to process the application and to send the answer, in addition to the time you will need to acquire study permits, will be too long for you to arrive for the beginning of the session. Spatio-temporal Event Forecasting Using Incremental Multi-source Feature Learning. Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. The workshop attracted about 100 attendees. Would you like to mark this message as the new best answer? 2022. Taking the pulse of COVID-19: a spatiotemporal perspective. 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 reproducibility papers include a clarification phase: Deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone. Novel AI-based techniques to improve modeling of engineering systems. We also use third-party cookies that help us analyze and understand how you use this website. 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. Algorithms and theories for explainable and interpretable AI models. For each accepted paper, at least one author must attend the workshop and present the paper. 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. However, the performance and efficiency of these techniques are big challenges for performing real-time applications. Merge remote-tracking branch 'origin/master', 2. iCal Outlook robotics Submissions can be original research contributions, or abstracts of papers previously submitted to top-tier venues, but not currently under review in other venues and not yet published. Zhiqian Chen, Lei Zhang, Gaurav Kolhe, Hadi Mardani Kamali, Setareh Rafatirad, Sai Manoj Pudukotai Dinakarrao, Houman Homayoun, Chang-Tien Lu, Liang Zhao. Application fees are not refundable. Please note that foreign students must allow for 3 to 6 months to complete all the formalities required to study in Canada. Yuanqi Du*, Shiyu Wang* (co-first author), Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao. Federated learning (FL) is one promising machine learning approach that trains a collective machine learning model using sharing data owned by various parties. The workshop will include original contributions on theory, methods, systems, and applications of data mining, machine learning, databases, network theory, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based healthcare applications, with a focus on applications in population and personalized health. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. This cookie is set by GDPR Cookie Consent plugin. Balaraman Ravindran (Indian Institute of Technology Madras, India ravi@cse.iitm.ac.in), Balaraman Ravindran (Indian Institute of Technology Madras, India Primary contact (ravi@cse.iitm.ac.in), Kristian Kersting (TU Darmstadt, Germany, kersting@cs.tu-darmstadt.de), Sriraam Natarajan (Univ of Texas Dallas, USA, Sriraam.Natarajan@utdallas.edu), Ginestra Bianconi (Queen Mary University of London, UK, ginestra.bianconi@gmail.com), Philip S. Chodrow (University of California, Los Angeles, USA, phil@math.ucla.edu) Tarun Kumar (Indian Institute of Technology Madras, India, tkumar@cse.iitm.ac.in), Deepak Maurya (Purdue University, India, maurya@cse.iitm.ac.in), Shreya Goyal (Indian Institute of Technology Madras, India, Goyal.3@iitj.ac.in), Workshop URL:https://sites.google.com/view/gclr2022/. Submitting a short or long paper to VDS will give authors a chance to present at VDS events at both ACM KDD 2022(hybrid) and IEEE VIS 2022( hybrid). Long Beach, California, USA . However, most models and AI systems are built with conservative operating environment assumptions due to regulatory compliance concerns. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. We also invite papers that have been published at other venues to spark discussions and foster new collaborations. 2022. . Submissions of technical papers can be up to 7 pages excluding references and appendices. Workshop Date: Sunday August 14, 2022 EDT. 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. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), regular paper (acceptance rate: 8.9%), Singapore, Dec 2018, accepted. of London). 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. Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad. The main goal of the dialog system technology challenge (DSTC) workshop is to share the result of five main tracks of the tenth dialog system technology challenge (DSTC10). ML4OR will serve as an interdisciplinary forum for researchers in both fields to discuss technical issues at this interface and present ML approaches that apply to basic OR building blocks (e.g., integer programming solvers) or specific applications. algorithms applied to the above topics: deep learning, reinforcement learning, multi-armed bandits, causal inference, mathematical programming, and stochastic optimization. In addition to that, we propose a shared task on one of the challenging SDU tasks, i.e., acronym extraction and disambiguation in multiple languages text. . This AAAI workshop aims to bring together researchers from core AI/ML, robotics, sensing, cyber physical systems, agriculture engineering, plant sciences, genetics, and bioinformatics communities to facilitate the increasingly synergistic intersection of AI/ML with agriculture and food systems. We invite submissions on a wide range of topics, spanning both theoretical and practical research and applications. In other words, many existing FL solutions are still exposed to various security and privacy threats. This 1-day workshop will include a mixture of invited speakers, panels (including discussion with the audience), and presentations from authors of accepted submissions. Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth Loparo, and Liang Zhao. This proposed workshop will build upon successes and learnings from last years successful AI for Behavior Change workshop, and will focus on on advances in AI and ML that aim to (1) design and target optimal interventions; (2) explore bias and equity in the context of decision-making and (3) exploit datasets in domains spanning mobile health, social media use, electronic health records, college attendance records, fitness apps, etc. All the submissions must follow the AAAI-22 formatting guidelines, camera-ready style. Feature Constrained Multi-Task Learnings for Event Forecasting in Social Media." The AAAI Workshop on Machine Learning for Operations Research (ML4OR) builds on the momentum that has been directed over the past 5 years, in both the OR and ML communities, towards establishing modern ML methods as a first-class citizen at all levels of the OR toolkit. Winter. 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-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted. Submission instructions will be available at the workshop web page. Detailed information could be found on the website of the workshop. arXiv preprint arXiv:2212.03954 (2022). "A Topic-focused Trust Model for Twitter." Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. 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. Submissions are limited to 4 pages, not including references. Disentangled Spatiotemporal Graph Generative Model. The academic session will focus on most recent research developments on GNNs in various application domains. 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. This manual extraction process is usually inefficient, error-prone, and inconsistent. Explainable Agency captures the idea that AI systems will need to be trusted by human agents and, as autonomous agents themselves must be able to explain their decisions and the reasoning that produced their choices (Langley et al., 2017). The PAKDD is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, and Liang Zhao. in Proceedings of the SIAM International Conference on Data Mining (SDM 2015), (acceptance rate: 22%), Vancouver, BC, pp. Data mining systems and platforms, and their efficiency, scalability, security and privacy. Whats more, various AI based models are trained on massive student behavioral and exercise data to have the ability to take note of a students strengths and weaknesses, identifying where they may be struggling. 1145/3394486.3403221. Comparison or integration of self-supervised learning methods and other semi-supervised and transfer learning methods in speech and audio processing tasks. ), 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). Junxiang Wang, Fuxun Yu, Xiang Chen, and Liang Zhao. The AAAI-22 workshop program includes 39 workshops covering a [] We will end the workshop with a panel discussion by invited speakers from different fields to enlist future directions. 1503-1512, Aug 2015. Participants will be given access to publicly available datasets and will be asked to use tools from AI and ML to generate insight from the data. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted. This topic encompasses forms of Neural Architecture Search (NAS) in which the performance properties of each architecture, after some training, are used to guide the selection of the next architecture to be tried. Xiaojie Guo, Liang Zhao, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao. Scientists and engineers in diverse domains are increasingly relying on using AI tools to accelerate scientific discovery and engineering design. IEEE, 2014. 1-39, November 2016. Submissions are limited to a maximum of four (4) pages, including all content and references, and must be in PDF format. We will include a panel discussion to close the workshop, in which the audience can ask follow up questions and to identify the key AI challenges to push the frontiers in Chemistry. However, the use of rich data sets also raises significant privacy concerns: They often reveal personal sensitive information that can be exploited, without the knowledge and/or consent of the involved individuals, for various purposes including monitoring, discrimination, and illegal activities. Deadline: Fri Jun 09 2023 04:59:00 GMT-0700 Yahoo! Submissions will be accepted via the Easychair submission website. Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. These models can also generate instant feedback to instructors and help them to improve their teaching effectiveness. In general, AI techniques are still not widely adopted in the real world. The goal of this workshop is to bring together the optimal transport, artificial intelligence, and structured data modeling, gathering insights from each of these fields to facilitate collaboration and interactions. Topics of interest include, but are not limited to: One day, comprising keynote, paper presentations and panel sessions. 9, no. Checklist for Revising a SIGKDD Data Mining Paper: Our goal is to build a stronger community of researchers exploring these methods, and to find synergies among these related approaches and alternatives. of Graz), Cynthia Rudin (Duke Univ.) Methods for learning network architecture during training, including Incrementally building neural networks during training, new performance benchmarks for the above. To facilitate KDD related research, we create this repository with: *ICDM has two tracks (regular paper track and short paper track), but the exact statistic is not released, e.g., the split between these two tracks. 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. Microsoft's Conference Management Toolkit is a hosted academic conference management system. Hyperparameters such as the number of layers, the number of nodes in each layer, the pattern of connectivity, and the presence and placement of elements such as memory cells, recurrent connections, and convolutional elements are all manually selected. 2085-2094, Aug 2016. 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/. 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.