kdd 2022 deadline

The fundamental mechanism of an online marketplace is to match supply and demand to generate transactions, with objectives considering service quality, participants experience, financial and operational efficiency. [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. Encore track papers that have been recently published, or accepted for publication in a conference or journal. Proceedings of the ACM on Human-Computer Interaction (CSCW 2022), to appear, 2022. "SimNest: Social Media Nested Epidemic Simulation via Online Semi-supervised Deep Learning." Topics include, but our not limited to: learning optimization models from data, constraint and objective learning, AutoAI, especially if combined with decision optimization models or environments, AutoRL, incorporating the inaccuracy of the automatically learnt models in the decision making process, and using machine learning to efficiently solve combinatorial optimization models. Our goal is to build a stronger community of researchers exploring these methods, and to find synergies among these related approaches and alternatives. In this workshop, we want to explore ways to bridge short-term with long-term issues, idealistic with pragmatic solutions, operational with policy issues, and industry with academia, to build, evaluate, deploy, operate and maintain AI-based systems that are demonstrably safe. Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen. In recent years, various information theoretic principles have also been applied to different deep learning related AI applications in fruitful and unorthodox ways. DOI:https://doi.org/10.1145/3339823. Babies learn their first language through listening, talking, and interacting with adults. Ranking, acceptance rate, deadline, and publication tips. convolutional neural network (CNN), recurrent neural network (RNN), etc.) ML4OR will place particular emphasis on: (1) ML methodologies for enhancing traditional OR algorithms for integer programming, combinatorial optimization, stochastic programming, multi-objective optimization, location and routing problems, etc. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), 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. Rabat, Morocco . Qingzhe Li, Liang Zhao, Jessica Lin and Yi-ching Lee. 1059-1072, May 1 2017. Submission Site: See the webpagehttps://sites.google.com/view/gclr2022/submissions; for detailed instructions and submission link. These datasets can be leveraged to learn individuals behavioral patterns, identify individuals at risk of making sub-optimal or harmful choices, and target them with behavioral interventions to prevent harm or improve well-being. Malicious attacks for ML models to identify their vulnerability in black-box/real-world scenarios. Web applications along with text processing programs are increasingly being used to harness online data and information to discover meaningful patterns identifying emerging health threats. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. 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. The workshop is a full day. Submissions will be collected via the OpenReview platform; URL forthcoming on the Workshop website. Publication in HC-SSL does not prohibit authors from publishing their papers in archival venues such as NeurIPS/ICLR/ICML or IEEE/ACM Conferences and Journals. We have invited several distinguished speakers with their research interests spanning from the theoretical to experimental aspects of complex networks. 4498-4505, New Orleans, US, Feb 2018. NOTE: Mandatory abstract deadline: 2022-08-08 Deadline: AAAI 157. Xiaosheng Li, Jessica Lin, and Liang Zhao. 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. Ting Hua, Chandan Reddy, Lijing Wang, Liang Zhao, Lei Zhang, Chang-Tien Lu, and Naren Ramakrishnan. We collaborate with Saudi Aramco to use machine learning for simulating oil and water flows, . NOTE: May 19: Notification. All papers must be submitted in PDF format using the AAAI-22 author kit. We invite submission of papers describing innovative research and applications around the following topics. "Controllable Data Generation by Deep Learning: A Review." Neurocomputing (Impact Factor: 5.719), accepted. Papers will be peer-reviewed and selected for oral and/or poster presentation at the workshop. The main interest of the proposed workshop is to look at a new perspective of system engineering where multiple disciplines such as AI and safety engineering are viewed as a larger whole, while considering ethical and legal issues, in order to build trustable intelligent autonomy. Deep learning and statistical methods for data mining. Integration of non-differentiable optimization models in learning. In decision-making domains as wide-ranging as medication adherence, vaccination uptakes, college enrollment, retirement savings, and energy consumption, behavioral interventions have been shown to encourage people towards making better choices. The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in artificial intelligence and, in particular, machine learning and optimization. Online Flu Epidemiological Deep Modeling on With this in mind, we welcome relevant contributions on the following (and related) topic areas: The submissions must be in PDF format, written in English, and formatted according to the AAAI camera-ready style. . Self-supervised learning approaches involving the interaction of speech/audio and other modalities. Template guidelines are here:https://www.acm.org/publications/proceedings-template. Each accepted paper presentation will be allocated between 15 and 20 minutes. Data Mining Conference Acceptance Rate. Poster/short/position papers: We encourage participants to submit preliminary but interesting ideas that have not been published before as short papers. text, images, and videos). Atlanta, Georgia, USA . Integrated syntax and semantic approaches for document understanding. 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. [slides] Chen Ling, Carl Yang, Liang Zhao. Additional advantages are possible, including decreased computational resources to solve a problem, reduced time for the network to make predictions, reduced requirements for training set size, and avoiding catastrophic forgetting. All submissions must be in PDF format and formatted according to the new Standard AAAI Conference Proceedings Template. Dazhou Yu, Guangji Bai, Yun Li, and Liang Zhao. Submissions of technical papers can be up to 7 pages excluding references and appendices. Submissions that are already accepted or under review for another conference or already accepted for a journal are not accepted. However, research in the AI field also shows that their performance in the wild is far from practical due to the lack of model efficiency and robustness towards open-world data and scenarios. Yuyang Gao and Liang Zhao. 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. KDD 2022 : Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. Deep Graph Translation. Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework. Deep Graph Transformation for Attributed, Directed, and Signed Networks. Yuanqi Du*, Shiyu Wang* (co-first author), Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao. Topics of interest include, but are not limited to: One day, comprising keynote, paper presentations and panel sessions. 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. Jan 13, 2022: Notification. The 21st IEEE International Conference on Data Mining (ICDM 2021), (Acceptance Rate: 9.9%), accepted. Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad. All papers must be submitted in PDF format, using the AAAI-22 author kit. International Journal of Digital Earth, (impact factor: 3.097), 25 Aug 2020, https://doi.org/10.1080/17538947.2020.1809723. upon methodologies and applications for extracting useful knowledge from data [1]. All the submissions should be anonymous. Authors are strongly encouraged to make data and code publicly available whenever possible. This workshop will follow a dual-track format. The main objective of the workshop is to bring researchers together to discuss ideas, preliminary results, and ongoing research in the field of reinforcement in games. 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. PLOS ONE (impact factor: 3.534), vo. Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, and Sai Dinakarrao. We have the following keynote speakers confirmed: Andreas Holzinger (Medical Univ. Zitao Liu (main contact) , TAL Education Group, liuzitao@tal.com, http://www.zitaoliu.com, Jiliang Tang (Michigan State University, tangjili@msu.edu, https://www.cse.msu.edu/~tangjili/), Lihan Zhao (TAL Education Group, zhaolihan@tal.com), and Xiao Zhai (TAL Education Group, zhaixiao@tal.com), Workshop URL:http://ai4ed.cc/workshops/aaai2022. Check the CFP for details Deadline: ICDM 2020 . Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [1] . Liyan Xu, Xuchao Zhang, Zong Bo, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho Choi. Liang Zhao, Junxiang Wang, and Xiaojie Guo. The workshop combines several disciplines, including ML, software engineering (with emphasis on quality), security, and game theory. This date takes priority over those shown below and could be extended for some programs. We invite submissions from participants who can contribute to the theory and applications of modeling complex graph structures such as hypergraphs, multilayer networks, multi-relational graphs, heterogeneous information networks, multi-modal graphs, signed networks, bipartite networks, temporal/dynamic graphs, etc. Attendance is open to all; at least one author of each accepted submission must be physically/virtually present at the workshop. "STED: semi-supervised targeted-interest event detectionin in twitter." Submit to: Submissions should be made via EasyChair athttps://easychair.org/conferences/?conf=it4dl, Jose C. Principe (University of Florida, principe@cnel.ufl.edu), Robert Jenssen (UiT The Arctic University of Norway, robert.jenssen@uit.no), Badong Chen (Xian Jiaotong University, chenbd@mail.xjtu.edu.cn), Shujian Yu (UiT The Arctic University of Norway, yusj9011@gmail.com), Supplemental workshop site:https://www.it4dl.org/. 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. 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The adversarial ML could also result in potential data privacy and ethical issues when deploying ML techniques in real-world applications. "GA-based principal component selection for production performance estimation in mineral processing." Junxiang Wang, Hongyi Li, Liang Zhao. Despite the great success of deep neural networks (DNNs) in many artificial intelligence (AI) tasks, they still suffer from limitations, such as poor generalization behavior for out-of-distribution (OOD) data, vulnerability to adversarial examples, and the black-box nature of DNNs. Qingzhe Li, Liang Zhao, Yi-Ching Lee, Yanfang Ye, Jessica Lin, and Lingfei Wu. If the admission deadline for international applicants is past, we suggest that you choose another session to begin your studies. Previously published work (or under-review) is acceptable. RES: A Robust Framework for Guiding Visual Explanation. Alan Yuille (Professor, Johns Hopkins University); Hao Su (Assistant Professor, UC San Diego); Rongrong Ji (Professor, Xiamen University); Xianglong Liu (Professor, Beihang University); Jishen Zhao (Associate Professor, UC San Diego); Tom Goldstein (Associate Professor, University of Maryland); Cihang Xie (Assistant Professor, UC Santa Cruz); Yisen Wang (Assistant Professor, Peking University); Bohan Zhuang (Assistant Professor, Monash University), Haotong Qin (Beihang University), Yingwei Li (Johns Hopkins University), Ruihao Gong (SenseTime Research), Xinyun Chen (UC Berkeley), Aishan Liu (Beihang University), Xin Dong (Harvard University), Jindong Guo (University of Munich), Yuhang Li (Yale University), Yiming Li (Tsinghua University), Yifu Ding (Beihang University), Mingyuan Zhang (Nanyang Technological University), Jiakai Wang (Beihang University), Jinyang Guo (University of Sydney), Renshuai Tao (Beihang University), Workshop site:https://practical-dl.github.io/. Virtual . Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Necessary cookies are absolutely essential for the website to function properly. 2, no. 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/. Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors. The goal of this workshop is to bring together the optimal transport, artificial intelligence, and structured data modeling, gathering insights from each of these fields to facilitate collaboration and interactions. 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. Unsupervised Deep Subgraph Anomaly Detection. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. August 14-18, 2022. Whats more, various AI based models are trained on massive student behavioral and exercise data to have the ability to take note of a students strengths and weaknesses, identifying where they may be struggling. 963-971, Apr-May 2015. All deadlines are at 11:59 PM anytime in the world. Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted. Novel AI-based techniques to improve modeling of engineering systems. The growing popularity of NAS methods demonstrates the communitys hunger for better ways of choosing or evolving network architectures that are well-matched to the problem at hand. Attendance is open to all prior registration to the workshop/conference. "Multi-resolution Spatial Event Forecasting in Social Media." 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. By clicking Accept All, you consent to the use of ALL the cookies. Hyperparameters such as the number of layers, the number of nodes in each layer, the pattern of connectivity, and the presence and placement of elements such as memory cells, recurrent connections, and convolutional elements are all manually selected. ML4OR is a one-day workshop consisting of a mix of events: multiple invited talks by recognized speakers from both OR and ML covering central theoretical, algorithmic, and practical challenges at this intersection; a number of technical sessions where researchers briefly present their accepted papers; a virtual poster session for accepted papers and abstracts; a panel discussion with speakers from academia and industry focusing on the state of the field and promising avenues for future research; an educational session on best practices for incorporating ML in advanced OR courses including open software and data, learning outcomes, etc. Their results will be submitted in either a short paper or poster format. How to do good research, Get it published in SIGKDD and get it cited! 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. Adaptive Kernel Graph Neural Network. https://doi.org/10.1007/s10707-019-00376-9. Registration in each workshop is required by all active participants, and is also open to all interested individuals. Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. The post-lunch session will feature a second keynote talk, two invited talks. 25-50 attendees including invited speakers and accepted papers. 2022. All submissions must be anonymous and conform to AAAI standards for double-blind review. The workshop will focus on the application of AI to problems in cyber-security. Submission site:https://cmt3.research.microsoft.com/DSTC102022, Koichiro Yoshino,Address: 2-2-2, Seika, Sohraku, Kyoto, 6190288, JapanAffiliation: RIKENPhone: +81-774-95-1360Email: koichiro.yoshino@riken.jp, Yun-Nung (Vivian) ChenAddress: No. arXiv preprint arXiv:2302.02093 (2023). URL: https://sites.google.com/view/kdd22onlinemarketplaces Call For Papers (Submission deadline: June3, 2022) 29, no. SIGMOD 2022 adheres to the ACM Policy Against Harassment. Knowledge representation for business documents. Topics of interest in the biomedical space include: Topics of general interest to cyber-security include: Submission site:https://easychair.org/conferences/?conf=aics22, Tamara Broderick (MIT CSAIL, tamarab@mit.edu), James Holt (Laboratory for Physical Sciences, USA, holt@lps.umd.edu), Edward Raff (Booz Allen Hamilton, USA, Raff_Edward@bah.com), Ahmad Ridley (National Security Agency), Dennis Ross (MIT Lincoln Laboratory, USA, dennis.ross@ll.mit.edu), Arunesh Sinha (Singapore Management University, Singapore, aruneshs@smu.edu.sg), Diane Staheli (MIT Lincoln Laboratory, USA, diane.staheli@ll.mit.edu), William W. Streilein (MIT Lincoln Laboratory, USA, wws@ll.mit.edu), Milind Tambe (Harvard University, USA, milind_tambe@harvard.edu), Yevgeniy Vorobeychik (Washington University in Saint Louis, USA, eug.vorobey@gmail.com) Allan Wollaber (MIT Lincoln Laboratory, USA, Allan.Wollaber@ll.mit.edu), Supplemental workshop site:http://aics.site/. The workshop follows a single-blind reviewing process. Typically, we receive around 40~60 submissions to each previous workshop. 5 (2014): 1447-1459. Topics of interest include, but are not limited to: Paper submissions will be in two formats: full paper (8 pages) and position paper (4 pages): The submission website ishttps://easychair.org/conferences/?conf=trase2022. ACM RecSys 2022 will be held in Seattle, USA, from September 18 - 23, 2022. Attendance is open to all registered participants. "Spatiotemporal Event Forecasting from Incomplete Hyper-local Price Data" The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017) , (acceptance rate: 21%), pp. simulation, evaluation and experimentation. 639-648, Nov 2015. In Proceedings of the 20th International Conference on Data Mining (ICDM 2020), (acceptance rate: 9.8%), November 17-20, 2020, Virtual Event, Sorrento, Italy, 10 pages. 4. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2014), industrial track, pp. TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints. 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. We will include a panel discussion to close the workshop, in which the audience can ask follow up questions and to identify the key AI challenges to push the frontiers in Chemistry. The workshop attracted about 100 attendees. The submission website ishttps://cmt3.research.microsoft.com/PPAI2022. At AAAI 2021, we successfully organized this workshop (https://taih20.github.io/). Liang Zhao, Feng Chen, Jing Dai, Ting Hua, Chang-Tien Lu, and Naren Ramakrishnan. Papers will be submitted electronically using Easychair. Why did so many AI/ML models fail during the pandemic? . the 33rd Annual Computer Security Applications Conference (ACSAC 2018), (acceptance rate: 20.1%), San Juan, Puerto Rico, USA, Dec 2018, accepted. What are the primary lessons learned from the model failures? 19-25, 2016. Gabriel Pedroza (CEA LIST), Jos Hernndez-Orallo (Universitat Politcnica de Valncia, Spain), Xin Cynthia Chen (University of Hong Kong, China), Xiaowei Huang (University of Liverpool, UK), Huascar Espinoza (KDT JU, Belgium), Mauricio Castillo-Effen (Lockheed Martin, USA), Sen higeartaigh (University of Cambridge, UK), Richard Mallah (Future of Life Institute, USA), John McDermid (University of York, UK), Supplemental workshop site:http://safeaiw.org/. These challenges are widely studied in enterprise networks, but there are many gaps in research and practice as well as novel problems in other domains. Detailed information could be found on the website of the workshop. ), 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. We especially welcome research from fields including but not limited to AI, human-computer interaction, human-robot interaction, cognitive science, human factors, and philosophy. "TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction", the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2019 (SIGSPATIAL 2019), long paper, (acceptance rate: 21.7%), Chicago, Illinois, USA, accepted. Our topics of interest span over prediction, planning, and decision problems for online marketplaces, including but not limited to. Qingzhe Li, Amir A. Fanid, Martin Slawski, Yanfang Ye, Lingfei Wu, Kai Zeng, and Liang Zhao. We are soliciting submissions of short papers in PDF format and formatted according to the Standard ACM Conference Proceedings Template. Government day with NSF, NIH, DARPA, NIST, and IARPA, Local industries in the DC Metro Area, including the Amazons second headquarter, New initiatives at KDD 2022: undergraduate research and poster session, Early career research day with postdoctoral scholars and assistant professors in a mentoring workshop and panel, Workshops and hands-on tutorials on emerging topics. Interpreting and Evaluating Neural Network Robustness. Papers will be peer-reviewed and selected for oral and/or poster presentations at the workshop. Novel algorithmic solutions to causal inference or discovery problems using information-theoretic tools or assumptions. At least one author of each accepted submission must be present at the workshop. Authors are invited to send a contribution in the AAAI-22 proceedings format. Big Data 2022 December 13-16, 2022. Metagraph Aggregated Heterogeneous Graph Neural Network for Illicit Traded Product Identification in Underground Market. SDU is expected to host 50-60 attendees. 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. This topic also encompasses techniques that augment or alter the network as the network is trained. Each full paper will be reviewed by three PC members, while extended abstracts will not be reviewed. : iCal Outlook robotics The official dates for submitting an application are detailed below, but see the exact deadline posted on the Description Page for the program of study. Yuanqi du, George Mason University, USA; Jian Pei, Simon Fraser University, Canada; Charu Aggarwal, IBM Research AI, USA; Philip S. Yu, University of Illinois at Chicago, USA; Xuemin Lin, University of New South Wales, Australia; Jiebo Luo, University of Rochester, USA; Lingfei Wu, JD.Com Silicon Valley Research Center, USA; Yinglong Xia, Facebook AI, USA; Jiliang Tang, Michigan State University, USA; Peng Cui, Tsinghua University, China; William L. Hamilton, McGill University, Canada; Thomas Kipf, University of Amsterdam, Netherlands, Workshop URL:https://deep-learning-graphs.bitbucket.io/dlg-aaai22/. While we are planning an in-person workshop to be held at AAAI-22, we aim to accommodate attendees who may not be able to travel to Vancouver by allowing participation via live virtual invited talks and virtual poster sessions. : Papers are submitted through the CMT portal for this workshop: Please select the track for your submission in Primary Subject Area and indicate if your submission is a full paper or an extended abstract in Secondary Subject Area. KDD 2022. Accepted contributions will be made publicly available as non-archival reports, allowing future submissions to archival conferences or journals.

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