Supplemental Workshop site:https://rl4ed.org/aaai2022/index.html. 1466-1469. Aug 14-18. The workshop will focus on the application of AI to problems in cyber-security. convolutional neural network (CNN), recurrent neural network (RNN), etc.) Attendance is expected to be 150-200 participants (estimated), including organizers and speakers. In this workshop we would like to focus on a contrasting approach, to learn the architecture during training. 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. By the end of this century, the earths population is projected to increase by 45% with available arable land decreasing by 20% coupled with changes in what crops these arable lands can best support; this creates the urgent need to enhance agricultural productivity by 70% before 2050. "The EMBERS architecture for streaming predictive analytics." job seekers, employers, recruiters and job agents. Typically, we receive around 40~60 submissions to each previous workshop. Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye, Liang Zhao. KDD 2023 KDD '23 ​ ​ ​ August 6-10, 2023. ECoST: Energy-Efficient Co-Locating and Self-Tuning MapReduce Applications. Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted. Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Chang-Tien Lu, Sai Dinakarrao, Houman Homayoun, Liang Zhao. [code] The workshop organizers invite paper submissions on the following (and related) topics: This workshop will be a one-day workshop, featuring invited speakers, poster presentations, and short oral presentations of selected accepted papers. Hua, Ting, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. Interpretable Molecular Graph Generation via Monotonic Constraints. 2022. The goal of ITCI22 is to bring together researchers working at the intersection of information theory, causal inference and machine learning in order to foster new collaborations and provide a venue to brainstorm new ideas, exemplify to the information theory community causal inference and discovery as an application area and highlight important technical challenges motivated by practical ML problems, draw the attention of the wider machine learning community to the problems at the intersection of causal inference and information theory, and demonstrate to the community the utility of information-theoretic tools to tackle causal ML problems. and facilitate discussions and collaborations in developing trustworthy AI methods that are reliable and more acceptable to physicians. RES: A Robust Framework for Guiding Visual Explanation. Distant-supervision of heterogeneous multitask learning for social event forecasting with multilingual indicators. "Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning." The paper submissions must be in pdf format and use the AAAI official templates. Full papers: Submissions must represent original material that has not appeared elsewhere for publication and that is not under review for another refereed publication. Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Jiabin Wang, and Qi Xiong. Poster/short/position papers submission deadline: Nov 5, 2021Full paper submission deadline: Nov 5, 2021Paper notification: Dec 3, 2021. information bottleneck principle). We will instead host the accepted papers on this website (https://aka.ms/di-2022) indefinitely. Advances in AI technology, particularly perception and planning, have enabled unprecedented advances in autonomy, with autonomous systems playing an increasingly important role in day-to-day lives, with applications including IoT, drones, and autonomous vehicles. Welcome to PAKDD2022. Babies learn their first language through listening, talking, and interacting with adults. These abrupt changes impacted the environmental assumptions used by AI/ML systems and their corresponding input data patterns. Yuyang Gao, Giorgio Ascoli, Liang Zhao. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is considered to be more practical and more related with real-world applications. DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums. These research trends inform the need to explore the intersection of AI with behavioral science and causal inference, and how they can come together for applications in the social and health sciences. Taking the pulse of COVID-19: a spatiotemporal perspective. 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. 4 pages), and position (max. Using a social media account will simply make the application process easier: none of your activities on this site will be posted to your profile. 10, pp. simulation, evaluation and experimentation. Thirty-First AAAI Conference on Artificial Intelligence, pp. However, most models and AI systems are built with conservative operating environment assumptions due to regulatory compliance concerns. Scientists and engineers in diverse domains are increasingly relying on using AI tools to accelerate scientific discovery and engineering design. We collaborate with Saudi Aramco to use machine learning for simulating oil and water flows, . 1923-1935, 1 Oct. 2020, doi: 10.1109/TKDE.2019.2912187. SIAM International Conference on Data Mining (SDM 2023) (Acceptance Rate: 27.4%), accepted. Submit to: Papers are required to submit to:https://easychair.org/conferences/?conf=dlg22. We are interested in a broad range of topics, both foundational and applied. After seventh highly successful events, the eighth Symposium on Visualization in Data Science (VDS) will be held at a new venue, ACM KDD 2022 as well as IEEE VIS 2022. Submission site:https://cmt3.research.microsoft.com/AAAI2022HCSSL/Submission/Index, Ali Etemad (Queens University, ali.etemad@queensu.ca), Ali Etemad (Queens University, ali.etemad@queensu.ca), Ahmad Beirami (Facebook AI, ahmad.beirami@gmail.com), Akane Sano (Rice University, akane.sano@rice.edu), Aaqib Saeed (Philips Research & University of Cambridge, aqibsaeed@protonmail.com), Alireza Sepas-Moghaddam (Socure, alireza.sepasm@socure.com), Mathilde Caron (Inria & Facebook AI, mathilde@fb.com), Pritam Sarkar (Queens University & Vector Institute, pritam.sarkar@queensu.ca), Huiyuan Yang (Rice University, hy48@rice.edu), Supplemental website:https://hcssl.github.io/AAAI-22/. The cookie is used to store the user consent for the cookies in the category "Performance". These choices can only be analyzed holistically if the technological and ethical perspectives are integrated into the engineering problem, while considering both the theoretical and practical challenges of AI safety. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), regular paper (acceptance rate: 8.9%), Singapore, Dec 2018, accepted. Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior. [paper] Current rates of progress are insufficient, making it impossible to meet this goal without a technological paradigm shift. Even in cases where one is able to collect data, there are inherently many kinds of biases in this process, leading to biased models. Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. The AAAI template https://aaai.org/Conferences/AAAI-22/aaai22call/ should be used for all submissions. Deep Graph Learning for Circuit Deobfuscation. The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China, accepted. By entering your email, you consent to receive communications from UdeM. A Report on the First Workshop on Document Intelligence (DI) at NeurIPS 2019. Xiaojie Guo, Yuanqi Du, Liang Zhao. These challenges and issues call for robust artificial intelligence (AI) algorithms and systems to help. ", ACM Transactions on Spatial Algorithms and Systems (TSAS), (Acceptance Rate: 11%), Volume 2 Issue 4, Acticle No. Application fees are not refundable. We expect ~60 attendees. Short papers 10m presentation and 5m discussion. Note: The workshop is a collaboration between NASSMA organisation, Deepmind and UM6P. This workshop aims to bring together researchers from AI and diverse science/engineering communities to achieve the following goals: 1) Identify and understand the challenges in applying AI to specific science and engineering problems2) Develop, adapt, and refine AI tools for novel problem settings and challenges3) Community-building and education to encourage collaboration between AI researchers and domain area experts. Adaptive Kernel Graph Neural Network. And considering robustness, input data with noises frequently occur in open-world scenarios, which presents critical challenges for the building of robust AI systems in practice. 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. 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. Well also host a competition on adversarial ML along with this workshop. An example of the latter is theCascade Correlation algorithm, as well as others that incrementally build or modify a neural network during training, as needed for the problem at hand. In our workshop, we specifically focus on the trustworthy issues in AI for healthcare, aiming to make clinical AI methods more reliable in real clinical settings and be willingly used by physicians. Key obstacles include lack of high-quality data, difficulty in embedding complex scientific and engineering knowledge in learning, and the need for high-dimensional design space exploration under constrained budgets. Ranking, acceptance rate, deadline, and publication tips. Cleansing and image enhancement techniques for scanned documents. We invite the submission of original and high-quality research papers in the topics related to biased or scarce data. It leverages many emerging privacy-preserving technologies (SMC, Homomorphic Encryption, differential privacy, etc.) Check the CFP for details Deadline: ICDM 2020 . 1-11, Feb 2016. 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. Paper Submission:November 12, 2021, 11:59 pm (anywhere on earth) Author Notification: December 3, 2021Full conference:February 22 March 1, 2022Workshop:February 28 March 1, 2022. 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. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. A final tribute was paid on Saturday to former Coalition Avenir Qubec (CAQ) minister Nadine Girault, who died of lung cancer last month at age 63 . The topics of interest include, but are not limited to: The papers will be presented in poster format and some will be selected for oral presentation. Knowledge representation for business documents. The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), (Acceptance Rate: 26%), accepted. Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph. Transfer learning methods for business document reading and understanding. Yuyang Gao, Tanmoy Chowdhury (co-first author), Lingfei Wu, Liang Zhao. Deep Multi-attributed Graph Translation with Node-Edge Co-evolution. Deadline in . Shuo Lei, Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu. 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. All time are 23:59, AoE (Anywhere on Earth), Hongteng Xu (Renmin University of China, hongtengxu@ruc.edu.cn, main contact), Julie Delon (Universit de Paris, julie.delon@u-paris.fr), Facundo Mmoli (Ohio State University, facundo.memoli@gmail.com), Tom Needham (Florida State University, tneedham@fsu.edu). ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), (acceptance rate: 15.4%), accepted. 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. [materials][data]. Oral Paper (Top 5% among the accepted papers). Deep Learning models are at the core of research in Artificial Intelligence research today. 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 . IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. Award for Artificial Intelligence for the Benefit of Humanity, Patrick Henry Winston Outstanding Educator Award, A Report to ARPA on Twenty-First Century Intelligent Systems, The Role of Intelligent Systems in the National Information Infrastructure, Code of Conduct for Conferences and Events, Request to Reproduce Copyrighted Materials, AAAI Conference on Artificial Intelligence, W1: Adversarial Machine Learning and Beyond, W2: AI for Agriculture and Food Systems (AIAFS), W6: AI in Financial Services: Adaptiveness, Resilience & Governance, W7: AI to Accelerate Science and Engineering (AI2ASE), W8: AI-Based Design and Manufacturing (ADAM) (Half-Day), W9: Artificial Intelligence for Cyber Security (AICS)(2-Day), W10: Artificial Intelligence for Education (AI4EDU), W11: Artificial Intelligence Safety (SafeAI 2022)(1.5-Day), W12: Artificial Intelligence with Biased or Scarce Data, W13: Combining Learning and Reasoning: Programming Languages, Formalisms, and Representations (CLeaR), W14: Deep Learning on Graphs: Methods and Applications (DLG-AAAI22), W15: DE-FACTIFY :Multi-Modal Fake News and Hate-Speech Detection, W16: Dialog System Technology Challenge (DSTC10), W17: Engineering Dependable and Secure Machine Learning Systems (EDSMLS 2022) (Half-Day), W18: Explainable Agency in Artificial Intelligence, W19: Graphs and More Complex Structures for Learning and Reasoning (GCLR), W21: Human-Centric Self-Supervised Learning (HC-SSL), W22: Information-Theoretic Methods for Causal Inference and Discovery (ITCI22), W23: Information Theory for Deep Learning (IT4DL), W25: Knowledge Discovery from Unstructured Data in Financial Services (Half-Day), W26: Learning Network Architecture during Training, W27: Machine Learning for Operations Research (ML4OR) (Half-Day), W28: Optimal Transport and Structured Data Modeling (OTSDM), W29: Practical Deep Learning in the Wild (PracticalDL2022), W30: Privacy-Preserving Artificial Intelligence, W31: Reinforcement Learning for Education: Opportunities and Challenges, W32: Reinforcement Learning in Games (RLG), W33: Robust Artificial Intelligence System Assurance (RAISA) (Half-Day), W34: Scientific Document Understanding (SDU) (Half-Day), W35: Self-Supervised Learning for Audio and Speech Processing, W36: Trustable, Verifiable and Auditable Federated Learning, W38: Trustworthy Autonomous Systems Engineering (TRASE-22), W39: Video Transcript Understanding (Half-Day), https://openreview.net/group?id=AAAI.org/2022/Workshop/AdvML, https://openreview.net/group?id=AAAI.org/2022/Workshop/AIAFS, https://easychair.org/conferences/?conf=aaai-2022-workshop, https://rail.fzu.edu.cn/info/1014/1064.htm, https://aaai.org/Conferences/AAAI-22/aaai22call/, https://sites.google.com/view/aaaiwfs2022, https://www.aaai.org/Publications/Templates/AuthorKit22.zip, https://openreview.net/group?id=AAAI.org/2022/Workshop/ADAM, https://easychair.org/conferences/?conf=aics22, https://cmt3.research.microsoft.com/AIBSD2022, https://aibsdworkshop.github.io/2022/index.html, https://openreview.net/forum?id=6uMNTvU-akO, https://easychair.org/conferences/?conf=dlg22, https://deep-learning-graphs.bitbucket.io/dlg-aaai22/, https://cmt3.research.microsoft.com/DSTC102022, https://dstc10.dstc.community/calls_1/call-for-workshop-papers, https://easychair.org/my/conference?conf=edsmls2022, https://sites.google.com/view/edsmls-2022/home, https://sites.google.com/view/eaai-ws-2022/call, https://sites.google.com/view/eaai-ws-2022/topic, https://sites.google.com/view/gclr2022/submissions, https://cmt3.research.microsoft.com/AAAI2022HCSSL/Submission/Index, https://cmt3.research.microsoft.com/ITCI2022, https://easychair.org/conferences/?conf=it4dl, https://easychair.org/conferences/?conf=imlaaai22, https://sites.google.com/view/aaai22-imlw, https://easychair.org/conferences/?conf=kdf22, Learning Network Architecture During Training, https://cmt3.research.microsoft.com/OTSDM2022, https://cmt3.research.microsoft.com/PracticalDL2022, https://cmt3.research.microsoft.com/PPAI2022, https://easychair.org/conferences/?conf=rl4edaaai22, https://sites.google.com/view/raisa-2022/, https://sites.google.com/view/sdu-aaai22/home, https://cmt3.research.microsoft.com/SAS2022, https://easychair.org/conferences/?conf=fl-aaai-22, http://federated-learning.org/fl-aaai-2022/, https://cmt3.research.microsoft.com/TAIH2022, https://easychair.org/conferences/?conf=trase2022, https://easychair.org/my/conference?conf=vtuaaai2022, Symposium on Educational Advances in Artificial Intelligence (EAAI-22), Conference on Innovative Applications of Artificial Intelligence (IAAI-22). San Francisco, USA . The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Datasets and Benchmarks Track, accepted. 41-50, New Orleans, US, Dec 2017. However, we will also accept anonymous submissions. Frontiers in Big Data, accepted, 2021. Motif-guided Heterogeneous Graph Deep Generation. Spatiotemporal Innovation Center Team. CVPR 11 deadline . Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth Loparo, and Liang Zhao. The ability to read, understand and interpret these documents, referred to here as Document Intelligence (DI), is challenging due to their complex formats and structures, internal and external cross references deployed, quality of scans and OCR performed, and many domains of knowledge involved. The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2022 (ACM SIGSPATIAL 2022), poster track, to appear, 2022. Temporal Domain Generalization with Drift-Aware Dynamic Neural Network. Qingzhe Li, Liang Zhao, Yi-Ching Lee, Avesta Sassan, and Jessica Lin. Please use vds@ieeevis.org to get in touch with us, or follow us on Twitter at @VisualDataSci. Submissions should follow the AAAI 2022 formatting guidelines and the AAAI 2022 standards for double-blind review including anonymous submission. The reproducibility papers include a clarification phase: Deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone. Attendance is open to all prior registration to the workshop/conference. Each full paper will be reviewed by three PC members, while extended abstracts will not be reviewed. Please email to Lingfei Wu: lwu@email.wm.edu for any query.