CVPR 11 deadline . If these formalities are not completed in time, you will have to file a new application at a later date. Researchers from related fields are invited to submit papers on the recent advances, resources, tools, and upcoming challenges for SDU. 2022. We expect 50-65 people in the workshop. 1, 2022: Call For Paper: The Undergraduate Consortium at SIGKDD 2022 is available at, Mar. 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. Chen Ling, Hengning Cao, Liang Zhao. 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. Well also host a competition on adversarial ML along with this workshop. Knowledge and Information Systems (KAIS), (impact factor: 2.936), accepted. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. 2022. Analytical cookies are used to understand how visitors interact with the website. However, most models and AI systems are built with conservative operating environment assumptions due to regulatory compliance concerns. Liang Zhao, Jiangzhuo Chen, Feng Chen, Fang Jin, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. We have invited several distinguished speakers with their research interests spanning from the theoretical to experimental aspects of complex networks. and facilitate discussions and collaborations in developing trustworthy AI methods that are reliable and more acceptable to physicians. Molecules, (impact factor: 4.411), accepted. Knowledge discovery from various data sources has gained the attention of many practitioners in recent decades. The workshop is a full day. Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior. 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. Application-specific designs for explainable AI, e.g., healthcare, autonomous driving, etc. While classical security vulnerabilities are relevant, ML techniques have additional weaknesses, some already known (e.g., sensitivity to training data manipulation), and some yet to be discovered. This workshop aims to bring together researchers and practitioners working on different facets of these problems, from diverse backgrounds to share challenges, new directions, recent research results, and lessons from applications. Moreover, to tackle and overcome several issues in personalized healthcare, information technology will need to evolve to improve communication, collaboration, and teamwork among patients, their families, healthcare communities, and care teams involving practitioners from different fields and specialties. Submissions should follow the AAAI-2022https://aaai.org/Conferences/AAAI-22/aaai22call/. Please refer tohttps://rl4ed.org/aaai2022/index.htmlfor additional information. It leverages many emerging privacy-preserving technologies (SMC, Homomorphic Encryption, differential privacy, etc.) Brave new ideas to learn AI models under bias and scarcity. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, Chang-Tien Lu, and Naren Ramakrishnan. to protect data owner privacy in FL. Large-scale Cost-aware Classification Using Feature Computational Dependency Graph. Integration of probabilistic inference in training deep models. Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. Novel methods to learn from scarce/sparse, or heterogenous, or multimodal data. Deadline: Fri Jun 09 2023 04:59:00 GMT-0700 Yahoo! Integration of Deep learning and Constraint programming. Zishan Gu, Ke Zhang, Guangji Bai, Liang Chen, Liang Zhao, Carl Yang. Submission at:https://easychair.org/my/conference?conf=edsmls2022. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. ^All accepted WSDM papers are associated with an interactive poster presentation in addition to oral presentations. Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. About 7-8 invited speakers who are distinguished professional in Deep learning on graph will present the frontier research topics. Modern interface, high scalability, extensive features and outstanding support are the signatures of Microsoft CMT. Share. 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. Self-supervised learning (SSL) has shown great promise in problems involving natural language and vision modalities. The automated processing of unstructured data to discover knowledge from complex financial documents requires a series of techniques such as linguistic processing, semantic analysis, and knowledge representation & reasoning. Submission Guidelines Workshop registration is available to AAAI-22 technical registrants at a discounted rate, or separately to workshop only registrants. 4. Attendance is open to all; at least one author of each accepted submission must be physically/virtually present at the workshop. Disentangled Spatiotemporal Graph Generative Model. The goal of this workshop is to focus on creating and refining AI-based approaches that (1) process personalized data, (2) help patients (and families) participate in the care process, (3) improve patient participation, (4) help physicians utilize this participation to provide high quality and efficient personalized care, and (5) connect patients with information beyond that available within their care setting. These approaches make it possible to use a tremendous amount of unlabeled data available on the web to train large networks and solve complicated tasks. We will use double-blind reviewing. Liang Zhao, Feng Chen, and Yanfang Ye. Welcome to DLG-KDD'22! - Bitbucket The final schedule will be available in November. At least one author of each accepted submission must be present at the workshop. 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). Graph Neural Networks: Foundations, Frontiers, and Applications. Liang Zhao, Yuyang Gao, Jieping Ye, Feng Chen, Fanny Ye, Chang-tien Lu, and Naren Ramakrishnan. What approaches emerge in building fundamentally robust and adaptive AI/ML systems? Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), New Orleans, US, Feb 2018, pp. Integrated syntax and semantic approaches for document understanding. 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. Algorithms and theories for learning AI models under bias and scarcity. "GA-based principal component selection for production performance estimation in mineral processing." It will include multiple keynote speakers, invited talks, a panel discussion, and two poster sessions for the accepted papers. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), vol. As deep learning problems become increasingly complex, network sizes must increase and other architectural decisions become critical to success. Junxiang Wang, Hongyi Li, Liang Zhao. We invite submissions of technical papers up to 7 pages excluding references and appendices. We cordially welcome researchers, practitioners, and students from academia and industry who are interested in understanding and discussing how data scarcity and bias can be addressed in AI to participate. Submission site:https://cmt3.research.microsoft.com/ITCI2022, Murat Kocaoglu, Chair (Purdue University, mkocaoglu@purdue.edu), Negar Kiyavash (EPFL, negar.kiyavash@epfl.ch), Todd Coleman (UCSD, tpcoleman@ucsd.edu), Supplemental workshop site:https://sites.google.com/view/itci22. KDD: Knowledge Discovery and Data Mining 2024 2023 2022 - WikiCFP Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. I recommend highly motivated students to reach out to me way earlier than the admission deadline, and figure out a research project project with me, with the goal of a publication. Papers will be peer-reviewed by the Program Committee (2-3 reviewers per paper). The program consists of poster sessions for accepted papers, and invited and spotlight talks. 27, 2022: Please check out Speical Days at, Apr. DeepGAR: Deep Graph Learning for Analogical Reasoning. Wang, Shiyu, Yuanqi Du, Xiaojie Guo, Bo Pan, and Liang Zhao. All papers must be submitted in PDF format, using the AAAI-22 author kit. The first AAAI Workshop on AI for Design and Manufacturing, ADAM, aims to bring together researchers from core AI/ML, design, manufacturing, scientific computing, and geometric modeling. Highlights: Government day with NSF, NIH, DARPA, NIST, and IARPA Local industries in the DC Metro Area, including the Amazon's 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 KDD 2022. We invite submission of papers describing innovative research and applications around the following topics. Research track papers reporting the results of ongoing or new research, which have not been published before. 40 attendees including: invited speakers, authors of accepted papers and shared task participants. All papers will be peer reviewed, single-blinded. Rupinder Khandpur, Taoran Ji, Yue Ning, Liang Zhao, Chang-Tien Lu, Erik Smith, Christopher Adams and Naren Ramakrishnan. Virtual . Xiaojie Guo, Lingfei Wu, Liang Zhao. Frontiers in Big Data, accepted, 2021. Deadlines are shown in America/Los_Angeles time. 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. [Best Paper Award]. GNES: Learning to Explain Graph Neural Networks. We accept two types of submissions full research papers no longer than 8 pages (including references) and short/poster papers with 2-4 pages. As a result, many AI/ML systems faced serious performance challenges and failures. Our goal is to build a stronger community of researchers exploring these methods, and to find synergies among these related approaches and alternatives. Submit to:https://cmt3.research.microsoft.com/AIBSD2022, Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories, kp388@cornell.edu), Ziyan Wu (UII America, Inc., wuzy.buaa@gmail.com), Supplemental workshop site:https://aibsdworkshop.github.io/2022/index.html. The scope of the workshop includes, but is not limited to, the following areas: We also invite participants to an interactive hack-a-thon. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted. SIGKDD Explorations, Vol. Proceedings of the IEEE (impact factor: 9.237), vol. The research contributions may discuss technical challenges of reading and interpreting business documents and present research results. Application fees are not refundable. Novel mechanisms for eliciting and consuming user feedback, recommender, structured and generative models, concept acquisition, data processing, optimization; HCI and visualization challenges; Analysis of human factors/cognition and user modelling; Design, testing and assessment of IML systems; Studies on risks of interaction mechanisms, e.g., information leakage and bias; Business use cases and applications. sup-port vector machine (SVM), decision tree, random forest, etc.) Track 2 focuses on the state of the art advances in the computational jobs marketplace. Deep learning and statistical methods for data mining. How can we develop solid technical visions and new paradigms about AI Safety? It is anticipated that this will be an in-person workshop, subject to changing travel restrictions and health measures. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 20%=174/870), short paper, to appear, 2022. 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/. 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. IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 24.31), accepted. Participants in the hack-a-thon will be asked to either register as a team or be randomly assigned to a team after registration. Sigcomm 2022! - 2022. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2016), regular paper, (acceptance rate: 8.5%), pp. To provide proper alerts and timely response, public health officials and researchers systematically gather news and other reports about suspected disease outbreaks, bioterrorism, and other events of potential international public health concern, from a wide range of formal and informal sources. These submissions would benefit from additional exposure and discussion that can shape a better future publication. Autonomous vehicles can share their detected information (e.g., traffic signs, collision events, etc.) Generative Deep Learning for Macromolecular Structure and Dynamics, Current Opinion in Structural Biology, (impact factor: 7.108), Section on Theory and Simulation/Computational Methods 67: 170-177, 2021 accepted. The submitted contributions will be peer-reviewed by the Program Committee, and preference will be given to high-quality original and relevant work to the Document Intelligence topics. IEEE, 2014. Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji and Charu Aggarwal. However, the quality of audio and video content shared online and the nature of speech and video transcripts pose many challenges to the existing natural language processing. The AAAI author kit can be downloaded from:https://www.aaai.org/Publications/Templates/AuthorKit22.zip. 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). Methods for learning network architecture during training, including Incrementally building neural networks during training, new performance benchmarks for the above. The Conference. Merge remote-tracking branch 'origin/master', 2. Submission link:https://easychair.org/cfp/raisa-2022, William Streilein, MIT Lincoln Laboratory, 244 Wood St., Lexington, MA, 02420, (781) 981-7200, wws@ll.mit.edu, Olivia Brown (MIT Lincoln Laboratory, Olivia.Brown@ll.mit.edu), Rajmonda Caceres (MIT Lincoln Laboratory, Rajmonda.Caceres@ll.mit.edu), Tina Eliassi-Rad (Northeastern University, teliassirad@northeastern.edu), David Martinez (MIT Lincoln Laboratory, dmartinez@ll.mit.edu), Sanjeev Mohindra (MIT Lincoln Laboratory, smohindra@ll.mit.edu), Elham Tabassi (National Institute of Standards and Technology, elham.tabassi@nist.gov), Workshop URL:https://sites.google.com/view/raisa-2022/. Keynotes and invited talks: Several keynotes and invited talks by leading researchers in the area will be presented. FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers. Scientists and engineers in diverse domains are increasingly relying on using AI tools to accelerate scientific discovery and engineering design. KDD 2022 | Washington DC, U.S. Examples of the datasets which may be considered are the DBTex Radiology Mammogram dataset and the Johns Hopkins COVID-19 case reports. 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. Representation Learning on Spatial Networks. We invite submissions on a wide range of topics, spanning both theoretical and practical research and applications. It is important to learn how to use AI effectively in these areas in order to be able to motivate and help people to take actions that maximize their welfare. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), 2022. Additionally, adversaries continue to develop new attacks. December, 09-12, 2022. This is especially the case for non-traditional online resources such as social networks, blogs, news feed, twitter posts, and online communities with the sheer size and ever-increasing growth and change rate of their data. Accepted contributions will be made publicly available as non-archival reports, allowing future submissions to archival conferences or journals. 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. 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 recent months/years, major global shifts have occurred across the globe triggered by the Covid pandemic. This website uses cookies to improve your experience while you navigate through the website. Recent years have witnessed growing efforts from the AI research community devoted to advancing our education and promising results have been obtained in solving various critical problems in education. 3, pp. Junxiang Wang and Liang Zhao. If it turns out that the architecture is not appropriate for the task, the user must repeatedly adjust the architecture and retrain the network until an acceptable architecture has been obtained. Submissions must be formatted in the AAAI submission format (https://www.aaai.org/Publications/Templates/AuthorKit22.zip) All submissions should be done electronically via EasyChair. Data mining systems and platforms, and their efficiency, scalability, security and privacy. In addition, several invited speakers with distinguished professional background will give talks related the frontier topics of GNN. This will include invited talks, poster sessions and a panel to discuss the achievements of past DSTC series, and future direction. "EMBERS at 4 years:Experiences operating an Open Source Indicators Forecasting System." 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. Some examples of the success of information theory in causal inference are: the use of directed information, minimum entropy couplings and common entropy for bivariate causal discovery; the use of the information bottleneck principle with applications in the generalization of machine learning models; analyzing causal structures of deep neural networks with information theory; among others. Accepted submissions will have the option of being posted online on the workshop website. Paper Final Version Due: Monday August 1, 2022. 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. Liang Zhao, Olga Gkountouna, and Dieter Pfoser. Registration Opens: Feb 02 '22 02:00 PM UTC: Registration Cancellation Refund Deadline: Apr 18 '22(Anywhere on Earth) Paper Submissions Abstract Submission Deadline: Sep 29 '21 12:00 AM UTC: Paper Submission deadline: Oct 06 '21 12:00 AM . Spatiotemporal Innovation Center Team. We will also select some of the best posters for spotlight talks (2 minutes each). Submissions will go through a double-blind review process. Please use ACM Conference templates (two column format). 9, no. However, these real-world applications typically translate to problem domains where it is extremely challenging to even obtain raw data, let alone annotated data. Papers will be peer-reviewed and selected for spotlight and/or poster presentation at the workshop. Linguistic analysis of business documents. Conference Management Toolkit - Login A primary reason for this is the inherent long-tailed nature of our world, and the need for algorithms to be trained with large amounts of data that includes as many rare events as possible. 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. "Online Spatial Event Forecasting in Microblogs. [materials]. The cookie is used to store the user consent for the cookies in the category "Other. Oct 14, 2021: Abstract Deadline. 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. Note: Mandatory abstract deadline on May 16, 2022 Deadline: ISMIR 2022 ISMIR '22 ​ . 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.
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