Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted. Yanfang Ye, Yiming Zhang, Yujie Fan, Chuan Shi and Liang Zhao. In light of these issues, and the ever-increasing pervasiveness of AI in the real world, we seek to provide a focused venue for academic and industry researchers and practitioners to discuss research challenges and solutions associated with building AI systems under data scarcity and/or bias. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. 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/. Frontiers in Neurorobotics, (impact factor: 2.574), accepted. For example, AI tools are built to ease the workload for teachers. SDU will be a one-day workshop. Typically, we receive around 40~60 submissions to each previous workshop. Programming Languages, Domain specific languages, Libraries and software tools for integration of various learning and reasoning paradigms. a tutorial on how to structure data mining papers by Prof. Xindong Wu (University of Louisiana at Lafayette). We are soliciting submissions of short papers in PDF format and formatted according to the Standard ACM Conference Proceedings Template. However, we will also accept anonymous submissions. Shiyu Wang, Xiaojie Guo, Liang Zhao. 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. 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. Long papers (up to 6 pages + references) and extended abstracts (2 pages + references) are welcome, including resubmissions of already accepted papers, work-in-progress, and position papers. Submission Guidelines Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. Yuyang Gao, Giorgio Ascoli, Liang Zhao. The 21st IEEE International Conference on Data Mining (ICDM 2021), (Acceptance Rate: 9.9%), accepted. Dynamic Tracking and Relative Ranking of Airport Threats from News and Social Media. The consideration and experience of adversarial ML from industry and policy making. Wang, Shiyu, Yuanqi Du, Xiaojie Guo, Bo Pan, and Liang Zhao. Representation learning, distributed representations learning and encoding in natural language processing for financial documents; Synthetic or genuine financial datasets and benchmarking baseline models; Transfer learning application on financial data, knowledge distillation as a method for compression of pre-trained models or adaptation to financial datasets; Search and question answering systems designed for financial corpora; Named-entity disambiguation, recognition, relationship discovery, ontology learning and extraction in financial documents; Knowledge alignment and integration from heterogeneous data; Using multi-modal data in knowledge discovery for financial applications; Data acquisition, augmentation, feature engineering, and analysis for investment and risk management; Automatic data extraction from financial fillings and quality verification; Event discovery from alternative data and impact on organization equity price; AI systems for relationship extraction and risk assessment from legal documents; Accounting for Black-Swan events in knowledge discovery methods. KDD 2022 2022. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted. 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. Papers will be peer-reviewed and selected for oral and/or poster presentations at the workshop. Short or position papers of up to 4 pages are also welcome. 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. It leverages many emerging privacy-preserving technologies (SMC, Homomorphic Encryption, differential privacy, etc.) In nearly all applications, reliability, safety, and security of such systems is a critical consideration. 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 ). Conference stats are visualized below for a straightforward comparison. Ting Hua, Chandan Reddy, Lijing Wang, Liang Zhao, Lei Zhang, Chang-Tien Lu, and Naren Ramakrishnan. The program consists of poster sessions for accepted papers, and invited and spotlight talks. We especially welcome research from fields including but not limited to AI, human-computer interaction, human-robot interaction, cognitive science, human factors, and philosophy. Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, Bill Wuest, Amarda Shehu, Liang Zhao. A 2-day workshop to share knowledge and research on five tracks of DSTC-10 and general related technical track. 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." The deep learning community must often confront serious time and hardware constraints from suboptimal architectural decisions. The submission website ishttps://cmt3.research.microsoft.com/OTSDM2022. in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019. text, images, and videos). This policy also applies to papers that overlap substantially in technical content with papers previously published, accepted, or under review. 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). Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework. ), Graduate (master's, specialized graduate diploma (DESS), etc. Pakdd 2022 Rabat, Morocco . Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye, Liang Zhao. Complex systems are often characterized by several components that interact in multiple ways among each other. Papers will be submitted to OpenReview system: Waiting for approval,https://openreview.net/forum?id=6uMNTvU-akO, Workshop Chair:Parisa Kordjamshidi, +1-2174187004, kordjams@msu.edu, Organizing Committee:Parisa Kordjamshidi (Michigan State University, kordjams@msu.edu), Behrouz Babaki (Mila/HEC Montreal, behrouz.babaki@mila.quebec), Sebastijan Dumani (KU Leuven, sebastijan.dumancic@cs.kuleuven.be), Alex Ratner (University of Washington, ajratner@cs.washington.edu), Hossein Rajaby Faghihi (Michigan State University, rajabyfa@msu.edu), Hamid Karimian (Michigan State University, karimian@msu.edu), Organizing Committee:Dan Roth (University of Pennsylvania, danroth@seas.upenn.edu) and Guy Van Den Broeck (University of California Los Angeles, guyvdb@cs.ucla.edu), Supplemental workshop site:https://clear-workshop.github.io. 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. The workshop page ishttps://sites.google.com/view/aaaiwfs2022, and it will include the most up-to-date information, including the exact schedule. The workshop will focus on both the theoretical and practical challenges related to the design of privacy-preserving AI systems and algorithms and will have strong multidisciplinary components, including soliciting contributions about policy, legal issues, and societal impact of privacy in AI. The AAAI-22 workshop program includes 39 workshops covering a [] With the rapid development of advanced techniques on the intersection between information theory and machine learning, such as neural network-based or matrix-based mutual information estimator, tighter generalization bounds by information theory, deep generative models and causal representation learning, information theoretic methods can provide new perspectives and methods to deep learning on the central issues of generalization, robustness, explainability, and offer new solutions to different deep learning related AI applications.This workshop aims to bring together both academic researchers and industrial practitioners to share visions on the intersection between information theory and deep learning, and their practical usages in different AI applications. We expect ~60 attendees. We hope this will help bring the communities of data mining and visualization more closely connected. Liming Zhang, Dieter Pfoser, Liang Zhao. PLOS ONE (impact factor: 3.534), vo. Data mining systems and platforms, and their efficiency, scalability, security and privacy. Examples of the datasets which may be considered are the DBTex Radiology Mammogram dataset and the Johns Hopkins COVID-19 case reports. As far as we know, we are the first workshop to focus on practical deep learning in the wild for AI, which is of great significance. We will accept the extended abstracts of the relevant and recently published work too. The theme of the hack-a-thon will be decided before submission is closed and will be focused around finding creative solutions to novel problems in health. Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, and Chang-Tien Lu. Our topics of interest span over prediction, planning, and decision problems for online marketplaces, including but not limited to. References will not count towards the page limit. . KDD is the premier Data Science conference. DeepGAR: Deep Graph Learning for Analogical Reasoning. 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. 14, 2022: The information of Keynote Speakers is available at, Apr. New theory and fundamentals of AI-aided design and manufacturing. We invite researchers to submit either full-length research papers (8 pages) or extended abstracts (2 pages) describing novel contributions and preliminary results, respectively, to the topics above; a more extensive list of topics is available on the Workshop website. Junxiang Wang, Yuyang Gao, Andreas Zufle, Jingyuan Yang, and Liang Zhao. Its capabilities have expanded from processing structured data (e.g. Microsoft's Conference Management Toolkit is a hosted academic conference management system. We welcome full paper submissions (up to 8 pages, excluding references or supplementary materials). All these changes require novel solutions, and the AI community is well-positioned to provide both theoretical- and application-based methods and frameworks. The submission website ishttps://cmt3.research.microsoft.com/TAIH2022. Eliminating the need to guess the right topology in advance of training is a prominent benefit of learning network architecture during training. Sathappan Muthiah, Patrick Butler, Rupinder Paul Khandpur, Parang Saraf, Nathan Self, Alla Rozovskaya, Liang Zhao, Jose Cadena et al. In the Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), (acceptance rate: 17.9%), accepted, Macao, China, Aug 2019. fact-checking. Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. Deadline in . DI@KDD2022 Call for Papers Organization Program Keynote Talk Accepted Papers Call for Papers Document Intelligence Workshop @ KDD 2022 UPDATES August 6: Final versions of the papersare posted! Finally, there is an increasing interest in AI in moving beyond traditional supervised learning approaches towards learning causal models, which can support the identification of targeted behavioral interventions. The topics for AIBSD 2022 include, but are not limited to: This one-day workshop will include invited talks from keynote speakers, and oral/spotlight presentations of the accepted papers. 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 ================== "SimNest: Social Media Nested Epidemic Simulation via Online Semi-supervised Deep Learning." 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. Motif-guided Heterogeneous Graph Deep Generation. for causal estimation in behavioral science. Jan 13, 2022: Notification. Xiaosheng Li, Jessica Lin, and Liang Zhao. "Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System." ", ACM Transactions on Spatial Algorithms and Systems (TSAS), (Acceptance Rate: 11%), Volume 2 Issue 4, Acticle No. We also use third-party cookies that help us analyze and understand how you use this website. [Call for papers] KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail, and Beyond, CFP: IJCAI 2021 Reinforcement Learning for Intelligent Transportation Systems Workshop, Second Workshop on Marketplace Innovation. Geoinformatica, (impact factor: 2.392), Volume 20, Issue 4, pp 765-795, Oct 2016. Detailed information could be found on the website of the workshop. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), (acceptance rate: 15.4%), accepted. 639-648, Nov 2015. Hence, there is a need for research and practical solutions to ML security problems.With these in mind, this workshop solicits original contributions addressing problems and solutions related to dependability, quality assurance and security of ML systems. Junxiang Wang, Hongyi Li, Zheng Chai, Yongchao Wang, Yue Cheng, Liang Zhao. December, 12-16, 2022. This workshop covers (but not limited to) the following topics: , It is a one day workshop and includes: invited talks, interactive discussions, paper presentations, shared task presentations, poster session etc. In the Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), (acceptance rate: 17.9%), accepted, Macao, China, Aug 2019. Please note that the KDD Cup workshop will haveno proceedingsand the authors retainfull rightsto submit or post the paper at any other venue. We invite participants to submit papers by the 12th of November, based on but not limited to, the following topics: RL in various formalisms: one-shot games, turn-based, and Markov games, partially-observable games, continuous games, cooperative games; deep RL in games; combining search and RL in games; inverse RL in games; foundations, theory, and game-theoretic algorithms for RL; opponent modeling; analyses of learning dynamics in games; evolutionary methods for RL in games; RL in games without the rules; search and planning; and online learning in games. At least one author of each accepted submission must present the paper at the workshop. Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen. 1-39, November 2016. Check the deadlines for submitting your application. Share. Journal of Biomedical Semantics, (impact factor: 1.845), 2018, accepted. Poster/short/position papers submission deadline: Nov 5, 2021Full paper submission deadline: Nov 5, 2021Paper notification: Dec 3, 2021. AI Conference Deadlines ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted. Knowledge representation for business documents. 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. 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. Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. In some programs, spots may be available after the deadlines. Jos Miguel Hernndez-Lobato, University of CambridgeProf. Some specific topics in the context of scientific discovery and engineering design include (but not limited to): This will be a one day workshop with a number of paper presentations and poster spotlights, a poster session, several invited talks, and a panel discussion. 2022. Disease Contact Network. 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 . 5, pp. Objectives of ADAM include outlining the main research challenges in this area, cross-pollinating collaborations between AI researchers and domain experts in engineering design and manufacturing, and sketching open problems of common interest. Washington DC, USA. Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is Identification of information-theoretic quantities relevant for causal inference and discovery. 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. We invite submission of papers describing innovative research on all aspects of knowledge discovery and data science, ranging from theoretical foundations to novel models and algorithms for data science problems in science, business, medicine, and engineering. International Journal of Digital Earth, (impact factor: 3.097), 25 Aug 2020, https://doi.org/10.1080/17538947.2020.1809723. Theoretical understanding of adversarial ML and its connection to other areas. Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad. Oct. 24, 2021: The KDD2022 website is LIVE! Online Flu Epidemiological Deep Modeling on This has created a strong demand for transcript understanding. Registration in each workshop is required by all active participants, and is also open to all interested individuals. SIGMOD 2022 adheres to the ACM Policy Against Harassment. Spatiotemporal Innovation Center Team. 1953-1970, Oct. 2017. AAAI-22 Workshop Program - AAAI 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. Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. Submissions should follow the AAAI 2022 formatting guidelines and the AAAI 2022 standards for double-blind review including anonymous submission. We invite novel contributions following the AAAI-22 formatting guidelines, camera-ready style. Full papers are allocated 20m presentation and 10m discussion. Out of these, around 20~30 papers are accepted. 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 . Videos have become an omnipresent source of knowledge: courses, presentations, conferences, documentaries, live streams, meeting recordings, vlogs. Kyoto . IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. Junxiang Wang and Liang Zhao. Held in conjunction with KDD'22 Aug 15, 2022 - Washington DC, USA. This date takes priority over those shown below and could be extended for some programs. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), accepted. AI is now shaping the way businesses, governments, and educational institutions do things and is making its way into classrooms, schools and districts across many countries. However, FL also faces multiple challenges that may potentially limit its applications in real-world use scenarios. KDD 2022 KDD . All accepted papers will be archived on the workshop website, but there will not be formal proceedings. KDD 2022. After the submission deadline, the names and order of authors cannot be changed. Workshop Date: Sunday August 14, 2022 EDT. The 11th International Conference on Learning Representations (ICLR 2023), accepted. Deadline: FSE 2023. 1, Sec. 1503-1512, Aug 2015. All submissions must be in PDF format and formatted according to the new Standard AAAI Conference Proceedings Template. In addition, any other work on dialog research is welcome to the general technical track. 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. In fact, the increasingly digitized education tools and the popularity of online learning have produced an unprecedented amount of data that provides us with invaluable opportunities for applying AI in education. 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.
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