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. We are soliciting submissions of short papers in PDF format and formatted according to the Standard ACM Conference Proceedings Template. 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. At AAAI 2021, we successfully organized this workshop (https://taih20.github.io/). NOTE: Mandatory abstract deadline on Oct 13, 2022. 10, pp. Proceedings of the ACM on Human-Computer Interaction (CSCW 2022), to appear, 2022. 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 ). Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Papers more suited for a poster, rather than a presentation, would be invited for a poster session. 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. Data Mining and Knowledge Discovery (DMKD), (impact factor: 3.670), accepted. Liang Zhao, Amir Alipour-Fanid, Martin Slawski and Kai Zeng. 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. Submission Site: See the webpagehttps://sites.google.com/view/gclr2022/submissions; for detailed instructions and submission link. the 33rd Annual Computer Security Applications Conference (ACSAC 2018), (acceptance rate: 20.1%), San Juan, Puerto Rico, USA, Dec 2018, accepted. iCal Outlook robotics Prediction-time Efficient Classification Using Feature Computational Dependencies. References will not count towards the page limit. The third AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22) builds on the success of previous years PPAI-20 and PPAI-21 to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. Accepted papers will not be archived but will be hosted on the workshop website. 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. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. 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. Zhiqian Chen, Lei Zhang, Gaurav Kolhe, Hadi Mardani Kamali, Setareh Rafatirad, Sai Manoj Pudukotai Dinakarrao, Houman Homayoun, Chang-Tien Lu, Liang Zhao. July 21: Clarified that the workshop this year will be held in-person. This one-day workshop will consist of: (1) an ice-breaking session, (2) paper presentations, (3) a poster session, and (4) an ideation brainstorming session. Why did so many AI/ML models fail during the pandemic? Welcome to the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022), which will be held in Chengdu, China on May 16-19, 2022. Authors are invited to send a contribution in the AAAI-22 proceedings format. 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). Yuanqi Du, Xiaojie Guo, Yinkai Wang, Amarda Shehu, Liang Zhao. Please use ACM Conference templates (two column format). Deep Multi-attributed Graph Translation with Node-Edge Co-evolution. Short or position papers of up to 4 pages are also welcome. Please refer tohttps://rl4ed.org/aaai2022/index.htmlfor additional information. Data mining systems and platforms, and their efficiency, scalability, security and privacy. to protect data owner privacy in FL. RL4ED is intended to facilitate tighter connections between researchers and practitioners interested in the broad areas of reinforcement learning (RL) and education (ED). Data science is the practice of deriving insights from data, enabled by statistical modeling, computational methods, interactive visual analysis, and domain-driven problem solving. Complex systems are often characterized by several components that interact in multiple ways among each other. 1466-1469. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), New Orleans, US, Feb 2018, pp. the 56th Design Automation Conference (DAC 2019), accepted, (acceptance rate: 20%), Las Vegas, US, 2019. 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. Interpreting and Evaluating Neural Network Robustness. Novel algorithms and theories to improve model robustness. in Proceedings of the 24st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018), research track (acceptance rate: 18.4%), London, United Kingdom, Aug 2018, accepted. The workshop will include several technical sessions, a virtual poster session where presenters can discuss their work, to further foster collaborations, multiple invited speakers covering crucial aspects for the practical deep learning in the wild, especially the efficient and robust deep learning, some tutorial talks, the challenge for efficient deep learning and solution presentations, and will conclude with a panel discussion. Ranking, acceptance rate, deadline, and publication tips. Deadlines are shown in America/Los_Angeles time. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. However, the performance and efficiency of these techniques are big challenges for performing real-time applications. 1-11, Feb 2016. Sathappan Muthiah, Patrick Butler, Rupinder Paul Khandpur, Parang Saraf, Nathan Self, Alla Rozovskaya, Liang Zhao, Jose Cadena et al. 2020. the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018) (acceptance rate: 20.6%), Stockholm, Sweden, Jul 2018, accepted. Dynamic Tracking and Relative Ranking of Airport Threats from News and Social Media. Submissions including full papers (6-8 pages) and short papers (2-4 pages) should be anonymized and follow the AAAI-22 Formatting Instructions (two-column format) at https://www.aaai.org/Publications/Templates/AuthorKit22.zip. Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao. It is difficult to expose false claims before they create a lot of damage. Their results will be submitted in either a short paper or poster format. What safety engineering considerations are required to develop safe human-machine interaction? Accepted submissions will be notified latest by August 7th, 2022. Held in conjunction with KDD'22 Aug 15, 2022 - Washington DC, USA. Junxiang Wang, Fuxun Yu, Xiang Chen, 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. Submit to:https://easychair.org/conferences/?conf=imlaaai22, Elizabeth DalyAddress: IBM Dublin Technology Campus, Dublin 15, IrelandEmail: elizabeth.daly@ie.ibm.com, Elizabeth Daly, IBM Research, Ireland (elizabeth.daly@ie.ibm.com), znur Alkan, IBM Research, Ireland (oalkan2@ie.ibm.com), Stefano Teso, University of Trento, Italy (stefano.teso@unitn.it), Wolfgang Stammer, TU Darmstadt, Germany (wolfgang.stammer@cs.tu-darmstadt.de), Workshop URL:https://sites.google.com/view/aaai22-imlw. 2022. The workshop on Robust Artificial Intelligence System Assurance (RAISA) will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems. 2022. An increasing world population, coupled with finite arable land, changing diets, and the growing expense of agricultural inputs, is poised to stretch our agricultural systems to their limits. Our topics of interest span over prediction, planning, and decision problems for online marketplaces, including but not limited to. Please keep your paper format according to AAAI Formatting Instructions (two-column format). All papers must be submitted in PDF format, using the AAAI-21 author kit and anonymized. As for the Kraken, they made one trade a month ago to acquire a seventh defenceman, Jaycob Megna and did nothing else (from 'Kraken remain quiet as NHL trade deadline passes,' The Seattle . 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. 1-39, November 2016. "Misinformation Propagation in the Age of Twitter." 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 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. 12 (2014): 90-94. For previous workshops held physically, each workshop attracts around 150~300 participants. 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/. [Best Paper Award]. 25-50 attendees including invited speakers and accepted papers. Qiang Yang, Hong Kong University of Science and Technology/ WeBank, China, (qyang@cse.ust.hk ), Sin G. Teo, Institute for Infocomm Research, Singapore (teosg@i2r.a-star.edu.sg), Han Yu, Nanyang Technological University, Singapore (han.yu@ntu.edu.sg), Lixin Fan, WeBank, China (lixinfan@webank.com), Chao Jin, Institute for Infocomm Research, Singapore (jin_chao@i2r.a-star.edu.sg), Le Zhang, University of Electronic Science and Technology of China (zhangleuestc@gmail.com), Yang Liu, Tsinghua University, China (liuy03@air.tsinghua.edu.cn), Zengxiang Li, Digital Research Institute, ENN Group, China (lizengxiang@enn.cn), Workshop site:http://federated-learning.org/fl-aaai-2022/. 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. However, despite increasing interest from various subfields, AI/ML techniques are yet to fulfill their full promise in achieving these advances. Information theory has demonstrated great potential to solve the above challenges. 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. Liang Zhao, Junxiang Wang, and Xiaojie Guo. This topic also encompasses techniques that augment or alter the network as the network is trained. Factorized Deep Generative Models for End-to-End Trajectory Generation with Spatiotemporal Validity Constraints. 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. 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. The advances in web science and technology for data management, integration, mining, classification, filtering, and visualization has given rise to a variety of applications representing real-time data on epidemics. Pengtao Xie (main contact), Assistant Professor, University of California, San Diego, pengtaoxie2008@gmail.com Engineer Ln, San Diego, CA 92161 (Tel)4123206230, Marinka Zitnik, Assistant Professor, Harvard University, marinka@hms.harvard.edu 10 Shattuck Street, Boston, MA 02115 (Tel)6503086763, Byron Wallace, Assistant Professor, Northeastern University, byron@ccs.neu.edu 177 Huntington Ave, Boston, MA 02115 (Tel)4135120352, Eric P. Xing, Professor, Carnegie Mellon University, epxing@cs.cmu.edu 5000 Forbes Ave, Pittsburgh, PA 15213 (Tel)4122682559, Ramtin Hosseini, PhD Student, University of California, San Diego, rhossein@eng.ucsd.edu (Tel) 3104293825, Ethics and fairness in autonomous systems, Robust robotic design, particularly of autonomous drones and/or vehicles. What techniques and approaches can be used to detect and effectively manage similar scenarios in the future? We welcome attendance from individuals who do not have something theyd like to submit but who are interested in RL4ED research. Xiaojie Guo, Yuanqi Du, Liang Zhao. A Systematic Survey on Deep Generative Models for Graph Generation. The submitted papers written in English must be in PDF format according to the AAAI camera ready style. iDetective: An Intelligent System for Automatic Identification of Key Actors in Online Hack Forums. Data Mining Conference Acceptance Rate. Hua, Ting, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. "Multi-resolution Spatial Event Forecasting in Social Media." IBM Research, 2018. ADMM for Efficient Deep Learning with Global Convergence. Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, and Yanfang Ye. 2022. 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. The submission website ishttps://cmt3.research.microsoft.com/PPAI2022. Furthermore, DNNs are data greedy in the context of supervised learning, and not well developed for limited label learning, for instance for semi-supervised learning, self-supervised learning, or unsupervised learning. These models can also generate instant feedback to instructors and help them to improve their teaching effectiveness. 4, Roosevelt Rd., Taipei, TaiwanAffiliation: National Taiwan UniversityPhone: +1-412-465-0130Email: yvchen@csie.ntu.edu.tw, Paul CrookAddress: 1 Hacker Way, Menlo Park, CA, USAAffiliation: FacebookPhone: +1-650-885-0094Email: pacrook@fb.com, DSTC 10 home:https://dstc10.dstc.community/homeDSTC 10 CFPs:https://dstc10.dstc.community/calls_1/call-for-workshop-papers. The submission website ishttps://cmt3.research.microsoft.com/PracticalDL2022. Liming Zhang, Liang Zhao, Dieter Pfoser, Shan Qin and Chen Ling. 1, 2022: Call For Paper: The Undergraduate Consortium at SIGKDD 2022 is available at, Mar. Please note as per the KDD Call for Workshop Proposals: Note: Workshop papers will not be archived in the ACM Digital Library. Submission Guidelines In recent years, we have seen examples of general approaches that learn to play these games via self-play reinforcement learning (RL), as first demonstrated in Backgammon. How can we make AI-based systems more ethically aligned? 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. The papers may consist of up to seven pages of technical content plus up to two additional pages for references. The trustworthy issues of clinical AI methods were not discussed. ITCI22 will be a one-day workshop. Topics of interest include but are not limited to: Acronyms, i.e., short forms of long phrases, are common in scientific writing. We invite submissions on a wide range of topics, spanning both theoretical and practical research and applications. 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. 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. Algorithms and theories for learning AI models under bias and scarcity. In spite of substantial research focusing on discovery from news, web, and social media data, its applications to datasets in professional settings such as financial filings and government reports, still present huge challenges. 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 . GNES: Learning to Explain Graph Neural Networks. Jos Miguel Hernndez-Lobato, University of CambridgeProf. Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, and Sai Dinakarrao.
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