Data Mining Conference Acceptance Rate. We accept two types of submissions full research paper no longer than 8 pages (including references) and short/poster paper with 2-4 pages. We are excited to continue promoting innovation in self-supervision for the speech/audio processing fields and inspiring the fields to contribute to the general machine learning community. Keynotes and invited talks: Several keynotes and invited talks by leading researchers in the area will be presented. 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. The goal of this workshop is to connect researchers in self-supervision inside and outside the speech and audio fields to discuss cutting-edge technology, inspire ideas and collaborations, and drive the research frontier. Proceedings of the ACM on Human-Computer Interaction (CSCW 2022), to appear, 2022. 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. Deep Graph Spectral Evolution Networks for Graph Topological Evolution. 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. ISPRS International Journal of Geo-Information (IJGI), (impact factor: 1.502), 5.10 (2016): 193. At least one author of each accepted submission must present the paper at the workshop. In other words, many existing FL solutions are still exposed to various security and privacy threats. 625-634, New Orleans, US, Dec 2017. Functional Connectivity Prediction with Deep Learning for Graph Transformation. 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. Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted. Panel discussion: Interactive Q&A session with a panel of leading researchers. Games provide an abstract and formal model of environments in which multiple agents interact: each player has a well-defined goal and rules to describe the effects of interactions among the players. Wang, Shiyu, Yuanqi Du, Xiaojie Guo, Bo Pan, and Liang Zhao. AAAI is pleased to present the AAAI-22 Workshop Program. All the submissions should be anonymous. Submission site:https://easychair.org/conferences/?conf=kdf22, Chair:Xiaomo Liu (J.P. Morgan Chase AI Research, xiaomo.liu@jpmchase.com), Zhiqiang Ma (J.P. Morgan Chase AI Research), Armineh Nourbakhsh (J.P. Morgan Chase AI Research), Sameena Shah (J.P. Morgan Chase AI Research), Gerard de Melo (Hasso Plattner Institute), Le Song (Mohamed bin Zayed University of Artificial Intelligence), Workshop URL:https://aaai-kdf.github.io/kdf2022/. This calls for novel methods and new methodologies and tools to address quality and reliability challenges of ML systems. KDD 2022. Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2021), (acceptance rate: 21.0%), accepted. Submissions of technical papers can be up to 7 pages excluding references and appendices. However, you may visit "Cookie Settings" to provide a controlled consent. "Knowledge-enhanced Neural Machine Reasoning: A Review." Martin Michalowski, PhD, FAMIA (Co-chair), University of Minnesota; Arash Shaban-Nejad, PhD, MPH (Co-chair), The University of Tennessee Health Science Center Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics; Simone Bianco, PhD (Co-chair), IBM Almaden Research Center; Szymon Wilk, PhD, Poznan University of Technology; David L. Buckeridge, MD, PhD, McGill University; John S. Brownstein, PhD, Boston Childrens Hospital, Workshop URL:http://w3phiai2022.w3phi.com/. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. Like other systems, ML systems must meet quality requirements. All deadlines are at 11:59 PM anytime in the world. 47, no. In addition, authors can provide an optional one page supplement at the end of their submitted paper (it needs to be in the same PDF file) focused on reproducibility. There is a need for the research community to develop novel solutions for these practical issues. If you are interested, please send a short email to rl4edorg@gmail.com and we can add you to the invitee list. Xuchao Zhang, Liang Zhao, Zhiqian Chen, and Chang-Tien Lu. Workshop registration is available to AAAI-22 technical registrants at a discounted rate, or separately to workshop only registrants. July 22: The workshop Programis up! The 19th International Conference on Data Mining (ICDM 2019), short paper, (acceptance rate: 18.05%), Beijing, China, accepted. Merge remote-tracking branch 'origin/master', 2. Consequently, standard notions of software quality and reliability such as deterministic functional correctness, black box testing, code coverage, and traditional software debugging become practically irrelevant for ML systems. Yuanqi Du*, Shiyu Wang* (co-first author), Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao. The workshop invites contribution to novel methods, innovations, applications, and broader implications of SSL for processing human-related data, including (but not limited to): In addition to the above, papers that consider the following are also invited: Manuscripts that fit only certain aspects of the workshop are also invited. Paper Submission Deadline: May 26, 2022 Author Notification: June 20, 2022 Camera Ready: July 9, 2022 Workshop: August . Combating fake news is one of the burning societal crises. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. KDD 2023 KDD '23 ​ ​ ​ August 6-10, 2023. We encourage all the teams who participated in the challenge to join the workshop. In recent years, various information theoretic principles have also been applied to different deep learning related AI applications in fruitful and unorthodox ways. Two types of submissions will be considered: full papers (6-8 pages + references), and short papers (2-4 pages + references). "SimNest: Social Media Nested Epidemic Simulation via Online Semi-supervised Deep Learning." The workshop is being organized by application area or other, panels, invited speakers, interactive, small groups, discussions, presentations. Checklist for Revising a SIGKDD Data Mining Paper: Submission URL:https://easychair.org/my/conference?conf=vtuaaai2022. IEEE Transactions on Information Forensics and Security (TIFS), (impact factor: 7.178), accepted. Modern interface, high scalability, extensive features and outstanding support are the signatures of Microsoft CMT. In particular, we encourage papers covering late-breaking results and work-in-progress research. The post-lunch session will feature one long talk, two short talks, and a poster session. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2016), regular paper, (acceptance rate: 8.5%), pp. Zishan Gu, Ke Zhang, Guangji Bai, Liang Chen, Liang Zhao, Carl Yang. The submissions need to be anonymized. Identification of information-theoretic quantities relevant for causal inference and discovery. As deep learning problems become increasingly complex, network sizes must increase and other architectural decisions become critical to success. The PAKDD is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. Fine tuning a neural network is very time consuming and far from optimal. It is anticipated that this will be an in-person workshop, subject to changing travel restrictions and health measures. For example, FL is still at the risk of various kinds of attacks that may result in leakage of individual data source privacy or degraded joint model accuracy. The goal of this workshop is to bring together the causal inference, artificial intelligence, and behavior science communities, gathering insights from each of these fields to facilitate collaboration and adaptation of theoretical and domain-specific knowledge amongst them. [Best Paper Candidate]. 1953-1970, Oct. 2017. The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China, accepted. In addition, several invited speakers with distinguished professional background will give talks related the frontier topics of GNN. We invite workshop participants to submit their original contributions following the AAAI format through EasyChair. Papers will be peer-reviewed and selected for oral and/or poster presentations at the workshop. Application-specific designs for explainable AI, e.g., healthcare, autonomous driving, etc. Interactive Machine Learning (IML) is concerned with the development of algorithms for enabling machines to cooperate with human agents. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), accepted. Deadline: AI4science NASSMA 2022 2022 AI4science NASSMA 2022 '22 . A 2-day workshop to share knowledge and research on five tracks of DSTC-10 and general related technical track. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted. 8 pages), short (max. A Report on the First Workshop on Document Intelligence (DI) at NeurIPS 2019. The 21st IEEE International Conference on Data Mining (ICDM 2021), (Acceptance Rate: 9.9%), accepted. 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 papers may consist of up to seven pages of technical content plus up to two additional pages for references. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. By clicking Accept All, you consent to the use of ALL the cookies. Deep Graph Translation. The Thirty-Sixth AAAI Conference on Artificial IntelligenceFebruary 28 and March 1, 2022Vancouver Convention CentreVancouver, BC, Canada AAAI is pleased to present the AAAI-22 Workshop Program. 2022. Submissions will be collected via the OpenReview platform; URL forthcoming on the Workshop website. Integration of logical inference in training deep models. . Self-supervised learning (SSL) has shown great promise in problems involving natural language and vision modalities. This will include invited talks, poster sessions and a panel to discuss the achievements of past DSTC series, and future direction. Virtual . Xiaosheng Li, Jessica Lin, Liang Zhao. We are in a conversation with some publishers once they confirm, we will announce accordingly. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. IBM Research, 2018. Submissions introducing interesting experimental phenomena and open problems of optimal transport and structured data modeling are welcome as well. ), Learning with algebraic or combinatorial structure, Link analysis/prediction, node classification, graph classification, clustering for complex graph structures, Theoretical analysis of graph algorithms or models, Optimization methods for graphs/manifolds, Probabilistic and graphical models for structured data, Unsupervised graph/manifold embedding methods. The final schedule will be available in November. Please note that the KDD Cup workshop will have no proceedings and the authors retain full rights to submit or post the paper at any other venue. Data science is the practice of deriving insights from data, enabled by statistical modeling, computational methods, interactive visual analysis, and domain-driven problem solving. Association for the Advancement of Artificial Intelligence, The Thirty-Sixth AAAI Conference on Artificial IntelligenceFebruary 28 and March 1, 2022Vancouver Convention CentreVancouver, BC, Canada. 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. Onn Shehory, Bar Ilan University (onn.shehory@biu.ac.il), Eitan Farchi, IBM Research Haifa (farchi@il.ibm.com), Guy Barash, Western Digital (Guy.Barash@wdc.com), Supplemental workshop site:https://sites.google.com/view/edsmls-2022/home. 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). AAAI, specifically, is a great venue for our workshop because its audience spans many ML and AI communities. Researchers from related fields are invited to submit papers on the recent advances, resources, tools, and upcoming challenges for SDU. Naftali Cohen (JP Morgan Chase & New York University), Eren Kurshan (Bank of America & Columbia University), Senthil Kumar (Capital One), Susan Tibbs (Financial Institutions Regulatory Authority, FINRA), Tucker Balch (JP Morgan Chase & Georgia Institute of Technology), and Kevin Compher (Securities Exchange Commission). Distributed Self-Paced Learning in Alternating Direction Method of Multipliers. Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [1] . We invite submissions of technical papers up to 7 pages excluding references and appendices. Advances in IML promise to make AIs more accessible and controllable, more compatible with the values of their human partners and more trustworthy. Hierarchical Incomplete Multisource Feature Learning for Spatiotemporal Event Forecasting. in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), research track (acceptance rate: 18.2%), San Francisco, California, pp. 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. Guangji Bai and Liang Zhao. Algorithms for secure and privacy-aware machine learning for AI. KDD 2022 : Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. The third AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22) builds on the success of previous years PPAI-20 and PPAI-21 to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. Novel ML methods in the computational material and physical sciences. A tag already exists with the provided branch name. We hope to build upon that success. URL: https://sites.google.com/view/kdd22onlinemarketplaces Call For Papers (Submission deadline: June3, 2022) Identification of key challenges and opportunities for future research. Everyone in the Top-10 leaderboard submissions will have a guaranteed opportunity for an in-person oral/poster presentation. Are you sure you want to create this branch? Previous healthcare-related workshops focus on how to develop AI methods to improve the accuracy and efficiency of clinical decision-making, including diagnosis, treatment, triage. Winter. of Graz), Cynthia Rudin (Duke Univ.) Recently developed tools and cutting-edge methodologies coming from the theory of optimal transport have proved to be particularly successful for these tasks. You can optionally export all deadlines to Google Calendar or .ics . 1059-1072, May 1 2017. Topics of interest include but are not limited to: (1) Survey papers summarizing recent advances in RL with applicability to ED; (2) Developing toolkits and datasets for applying RL methods to ED; (3) Using RL for online evaluation and A/B testing of different intervention strategies in ED; (4) Novel applications of RL for ED problem settings; (5) Using pedagogical theories to narrow the policy space of RL methods; (6) Using RL methodology as a computational model of students in open-ended domains; (7) Developing novel offline RL methods that can efficiently leverage historical student data; (8) Combining statistical power of RL with symbolic reasoning to ensure the robustness for ED. ), 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). ; (2) Deep Learning (DL) approaches that can exploit large datasets, particularly Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL); (3) End-to-end learning methodologies that mend the gap between ML model training and downstream optimization problems that use ML predictions as inputs; (4) Datasets and benchmark libraries that enable ML approaches for a particular OR application or challenging combinatorial problems. We invite thought-provoking submissions on a range of topics in fields including, but not limited to, the following areas: The full-day workshop will start with a keynote talk, followed by an invited talk and contributed paper presentations in the morning. Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, et al.. "'Beating the news' with EMBERS:forecasting civil unrest using open source indicators." 17th International Workshop on Mining and Learning with Graphs. Time Series Clustering in Linear Time Complexity. Extended abstract up to 2 pages are also welcome. These cookies ensure basic functionalities and security features of the website, anonymously. All submissions will be peer-reviewed. To adapt SSL frameworks to build effective human-centric deep learning solutions for human-centric data, a number of key challenges and opportunities need to be explored. These complex demands have brought profound implications and an explosion of interest for research into the topic of this workshop, namely building practical AI with efficient and robust deep learning models. 1-11, Feb 2016. In some programs, spots may be available after the deadlines. ACM RecSys 2022 will be held in Seattle, USA, from September 18 - 23, 2022. Disentangled Spatiotemporal Graph Generative Model. Knowledge and Information Systems (KAIS), (Impact Factor: 2.531), to appear, 2022. Full (8 pages) and short (4 pages, work in progress) papers, AAAI style. Performance characterization of AI algorithms and systems under bias and scarcity. 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 ================== 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.