Deep Graph Learning for Circuit Deobfuscation. Expected attendance is 40-50 people. The trained models are intended to assign scores to novel utterances, assessing whether they are possible or likely utterances in the training language. Liang Zhao's Homepage - Emory University An Invertible Graph Diffusion Model for Source Localization. The accepted papers will be posted on the workshop website and will not appear in the AAAI proceedings. Attendance is open to all; at least one author of each accepted submission must be physically/virtually present at the workshop. The 21st IEEE International Conference on Data Mining (ICDM 2021), (Acceptance Rate: 9.9%), accepted. The topics of interest include but are not limited to: Theoretical and Computational Optimal Transport: Optimal Transport-Driven Machine Learning: Optimal Transport-Based Structured Data Modeling: The full-day workshop will start with two long talks and one short talk in the morning. Short papers 10m presentation and 5m discussion. All papers will be peer-reviewed, single-blinded (i.e., please include author names/affiliations/email addresses on your first page). The AAAI-22 workshop program includes 39 workshops covering a wide range of topics in artificial intelligence. We invite submission of papers describing innovative research on all aspects of knowledge discovery and data science, ranging from theoretical foundations to novel models and algorithms for data science problems in science, business, medicine, and engineering. 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. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. We also use third-party cookies that help us analyze and understand how you use this website. Knowledge Discovery and Data Mining is an interdisciplinary area focusing Ting Hua, Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Following this AAAI conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. GeoInformatica (impact factor: 2.392), 24, 443475 (2020). To adapt SSL frameworks to build effective human-centric deep learning solutions for human-centric data, a number of key challenges and opportunities need to be explored. Videos have become an omnipresent source of knowledge: courses, presentations, conferences, documentaries, live streams, meeting recordings, vlogs. Deep Generation of Heterogeneous Networks. DI@KDD2022 Call for Papers Organization Program Keynote Talk Accepted Papers Call for Papers Document Intelligence Workshop @ KDD 2022 UPDATES August 6: Final versions of the papersare posted! Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features. References will not count towards the page limit. Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, et al.. "'Beating the news' with EMBERS:forecasting civil unrest using open source indicators." Each oral presentation will be allocated between 10-15 minutes, while the spotlight presentation will be 2 minute each. 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. For questions on submission and the workshop, please send email through the following link: Track 1: Tony Qin (Lyft), Rui Song (NC State & Amazon), Hongtu Zhu (UNC), Michael Jordan (Berkeley), Track 2: Liangjie Hong (LinkedIn), Mohammed Korayem (CareerBuilder), Haiyan Luo (Indeed). Virtual . Keynotes and invited talks: Several keynotes and invited talks by leading researchers in the area will be presented. Positive applications of adversarial ML, i.e., adversarial for good. 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. 2999-3006, New Orleans, US, Feb 2018. The 33rd European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databasesg (ECML-PKDD 2022) (Acceptance Rate: 26%), accepted, 2022. . After the submission deadline, the names and order of authors cannot be changed. Rather than studying robustness with respect to particular ML algorithms, our approach will be to explore robustness assurance at the system architecture level, during both development and deployment, and within the human-machine teaming context. 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. 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. We will end the workshop with a panel discussion by invited speakers from different fields to enlist future directions. Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. All submissions must be anonymous and conform to AAAI standards for double-blind review. . 2022. All the workshop chairs, most of the Committees, and the authors of the accepted papers will attend the workshop also. Cleansing and image enhancement techniques for scanned documents. Online and Distributed Robust Regressions with Extremely Noisy Labels. As far as we know, we are the first workshop to focus on practical deep learning in the wild for AI, which is of great significance. To push forward the research on acronym understanding in scientific text, we propose two shared tasks on acronym extraction (i.e., recognizing acronyms and phrases in text) and disambiguation (i.e., finding the correct expansion for an ambiguous acronym). Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), New Orleans, US, Feb 2018, pp. Junxiang Wang, Fuxun Yu, Xiang Chen, and Liang Zhao. Deadline: AI4science NASSMA 2022 2022 AI4science NASSMA 2022 '22 . We expect 50-65 people in the workshop. Prediction-time Efficient Classification Using Feature Computational Dependencies. KDD: Knowledge Discovery and Data Mining 2024 2023 2022 - WikiCFP In fact, the increasingly digitized education tools and the popularity of online learning have produced an unprecedented amount of data that provides us with invaluable opportunities for applying AI in education. P. 6205, succursale Centre-villeMontral, (Qubec) H3C 3T5Canada. Papers must be between 4-8 pages with the AAAI submission format submitted to the track of regular paper, SUPERB or Zero Speech result paper. Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth Loparo, and Liang Zhao. "Online Spatial Event Forecasting in Microblogs. Proceedings of the ACM on Human-Computer Interaction (CSCW 2022), to appear, 2022. 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. Kyoto . Geographical Mapping and Visual Analytics for Health Data, Biomedical Ontologies, Terminologies, and Standards, Bayesian Networks and Reasoning under Uncertainty, Temporal and Spatial Representation and Reasoning, Crowdsourcing and Collective Intelligence, Risk Assessment, Trust, Ethics, Privacy, and Security, Computational Behavioral/Cognitive Modeling, Health Intervention Design, Modeling and Evaluation, Applications in Epidemiology and Surveillance (e.g., Bioterrorism, Participatory Surveillance, Syndromic Surveillance, Population Screening), Hybrid methods, combining data driven and predictive forward models, biomedical signal analysis/modeling (EEG, ECG, PPG, EMG, fMRI, IMU, medical/clinical data, etc. Knowledge and Information Systems (KAIS), (Impact Factor: 2.531), to appear, 2022. Deep Spatial Domain Generalization. . Knowledge representation for business documents. The first achievements in playing these games at super-human level were attained with methods that relied on and exploited domain expertise that was designed manually (e.g. Submissions may consist of up to 4 pages plus one additional page solely for references. Integration of logical inference in training deep models. 5, pp. Deep Generative Models for Spatial Networks. STGEN: Deep Continuous-time Spatiotemporal Graph Generation. Papers that introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain are encouraged. Federated learning (FL) is one promising machine learning approach that trains a collective machine learning model using sharing data owned by various parties. ; (2) Deep Learning (DL) approaches that can exploit large datasets, particularly Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL); (3) End-to-end learning methodologies that mend the gap between ML model training and downstream optimization problems that use ML predictions as inputs; (4) Datasets and benchmark libraries that enable ML approaches for a particular OR application or challenging combinatorial problems. Deep Graph Transformation for Attributed, Directed, and Signed Networks. First, large data sources, both conventionally used in social sciences (EHRs, health claims, credit card use, college attendance records) and unconventional (social networks, fitness apps), are now available, and are increasingly used to personalize interventions. Algorithms and theories for learning AI models under bias and scarcity. 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. Characterization of fundamental limits of causal quantities using information theory. While progress has been impressive, we believe we have just scratched the surface of what is capable, and much work remains to be done in order to truly understand the algorithms and learning processes within these environments. 11, 2022: We have posted the list of accepted Workshops at, Apr. The submission website ishttps://easychair.org/conferences/?conf=fl-aaai-22. Representation Learning on Spatial Networks. Atlanta, Georgia, USA . [Best Paper Award Shortlist]. Conference Management Toolkit - Login Yuyang Gao, Tong Sun, Sungsoo Hong, and Liang Zhao. Attendance is open to all; at least one author of each accepted paper must be virtually present at the workshop. iCal Outlook robotics The submission website ishttps://cmt3.research.microsoft.com/PracticalDL2022. arXiv preprint arXiv:2002.11867 (2021), Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao. : Papers must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template. Check the deadlines for submitting your application. What is the status of existing approaches in ensuring AI and Machine Learning (ML) safety, and what are the gaps? The cookie is used to store the user consent for the cookies in the category "Performance". Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest. The goal of the inaugural HC-SSL workshop is to highlight and facilitate discussions in this area and expose the attendees to emerging potentials of SSL for human-centric representation learning, and promote responsible AI within the context of SSL. It is difficult to expose false claims before they create a lot of damage. Manuscripts must be submitted as PDF files viaEasyChair online submission system. Causal inference is one of the main areas of focus in artificial intelligence (AI) and machine learning (ML) communities. Attendance is open to all. Algorithms for secure and privacy-aware machine learning for AI. All questions about submissions should be emailed to [email protected], AmazonKDDCup2022: KDD Cup 2022 Workshop: ESCI Challenge for Improving Product Search, Washington DC, DC, United States, August 17, 2022, https://easychair.org/conferences/?conf=amazonkddcup2022, https://www.acm.org/publications/proceedings-template. Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning. 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. Scientific documents such as research papers, patents, books, or technical reports are one of the most valuable resources of human knowledge. Papers will be submitted electronically using Easychair. 4. DeepGAR: Deep Graph Learning for Analogical Reasoning. Adverse event detection by integrating Twitter data and VAERS. It is anticipated that this will be an in-person workshop, subject to changing travel restrictions and health measures. 105, no. Submission URL:https://easychair.org/my/conference?conf=vtuaaai2022. 2020. Attendance is open to all prior registration to the workshop/conference. For authors who do not wish their papers to be posted online, please mention this in the workshop submission. Zheng Zhang and Liang Zhao. Pattern Recognition, (impact factor: 7.196),112 (2021): 107711. The program consists of poster sessions for accepted papers, and invited and spotlight talks. Submissions may consist of up to 7 pages of technical content plus up to two additional pages solely for references. These challenges and issues call for robust artificial intelligence (AI) algorithms and systems to help. "Multi-resolution Spatial Event Forecasting in Social Media." Contrast Pattern Mining in Paired Multivariate Time Series of Controlled Driving Behavior Experiment. the 33rd Annual Computer Security Applications Conference (ACSAC 2018), (acceptance rate: 20.1%), San Juan, Puerto Rico, USA, Dec 2018, accepted. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. The invited speakers, who are well-recognized experts of the field, will give a 30 minute talk. Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang. Make sure your desired study programs are open for admission in the session when you would like to start your studies. The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2021), (acceptance rate: 23.6%), accepted. Creative Commons Attribution-Share Alike 3.0 License, 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 25TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, Knowledge Discovery and Data Mining Conference, 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 18th ACM SIGKDD Knowledge Discovery and Data Mining, The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Innovation, Service, and Rising Star Awards. Their results will be submitted in either a short paper or poster format. It highlights the importance of declarative languages that enable such integration for covering multiple formalisms at a high-level and points to the need for building a new generation of ML tools to help domain experts in designing complex models where they can declare their knowledge about the domain and use data-driven learning models based on various underlying formalisms. This workshop on Trustworthy Autonomous Systems Engineering (TRASE) offers an opportunity to highlight state of the art research in trustworthy autonomous systems, as well as provide a vision for future foundational and applied advances in this critical area at the intersection of AI and Cyber-Physical Systems. in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), applied data science track, accepted (acceptance rate: 19.9%), pp. arXiv preprint arXiv:2212.03954 (2022). We expect 50~75 participants and potentially more according to our past experiences. In general, AI techniques are still not widely adopted in the real world. The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), (Acceptance Rate: 26%), accepted. 2022. Papers will be peer-reviewed and selected for spotlight and/or poster presentation. The research contributions may discuss technical challenges of reading and interpreting business documents and present research results. Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. Yujie Fan, Yiming Zhang, Shifu Hou, Lingwei Chen, Yanfang Ye, Chuan Shi, Liang Zhao, Shouhuai Xu. Ting Hua, Chandan Reddy, Lijing Wang, Liang Zhao, Lei Zhang, Chang-Tien Lu, and Naren Ramakrishnan. Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features. To view them in conference website timezones, click on them. Spatio-temporal Event Forecasting Using Incremental Multi-source Feature Learning. Junxiang Wang, Hongyi Li, Liang Zhao. Full papers: Submissions must represent original material that has not appeared elsewhere for publication and that is not under review for another refereed publication. Xuchao Zhang, Xian Wu, Fanglan Chen, Liang Zhao, Chang-Tien Lu. A striking feature of much of this recent work is the application of new theoretical and computational techniques for comparing probability distributions defined on spaces with complex structures, such as graphs, Riemannian manifolds and more general metric spaces. 2022. 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. The goal of this workshop is to bring together the causal inference, artificial intelligence, and behavior science communities, gathering insights from each of these fields to facilitate collaboration and adaptation of theoretical and domain-specific knowledge amongst them. applications: ridesharing, online retail, food delivery, house rental, real estate, and more. Liming Zhang, Dieter Pfoser, Liang Zhao. Efficient Learning with Exponentially-Many Conjunctive Precursors for Interpretable Spatial Event Forecasting. Submissions will be peer reviewed, single-blinded. Use Compass, the interactive checklist designed exclusively for the Universit de Montral, to carefully prepare your application and to avoid common pitfalls along the way. The design and implementation of these AI techniques to meet financial business operations require a joint effort between academia researchers and industry practitioners. 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. You also have the option to opt-out of these cookies. Some will be selected for spotlight talks, and some for the poster session. The 39th IEEE International Conference on Data Engineering (ICDE 2023), accepted. 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/. These cookies track visitors across websites and collect information to provide customized ads. (Depending on the volume of submissions, we may be able to accommodate only a subset of them.). 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/. ISBN: 978-981-16-6053-5. We will receive the paper on the CMT system. Trade-Off between Privacy-Preserving and Explainable Federated Learning Federated Learning Multi-Party Computation, Federated Learning Homomorphic Encryption, Federated Learning Personalization Techniques, Federated Learning Meets Mean-Field Game Theory, Federated Learning-based Corporate Social Responsibility. Frontiers in Neurorobotics, (impact factor: 2.574), accepted. Authors are strongly encouraged to make data and code publicly available whenever possible. Submission site:https://cmt3.research.microsoft.com/ITCI2022, Murat Kocaoglu, Chair (Purdue University, [email protected]), Negar Kiyavash (EPFL, [email protected]), Todd Coleman (UCSD, [email protected]), Supplemental workshop site:https://sites.google.com/view/itci22. Recently developed tools and cutting-edge methodologies coming from the theory of optimal transport have proved to be particularly successful for these tasks. Submissions are limited to 4 pages, not including references. We have the following keynote speakers confirmed: Andreas Holzinger (Medical Univ. We welcome submissions of long (max. Small Molecule Generation via Disentangled Representation Learning. Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao. 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. This has created a strong demand for transcript understanding. 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. Outcomes include outlining the main research challenges in this area, potential future directions, and cross-pollination between AI researchers and domain experts in agriculture and food systems. Consequently, standard notions of software quality and reliability such as deterministic functional correctness, black box testing, code coverage, and traditional software debugging become practically irrelevant for ML systems. "How events unfold: spatiotemporal mining in social media." Public health authorities and researchers collect data from many sources and analyze these data together to estimate the incidence and prevalence of different health conditions, as well as related risk factors. "EMBERS at 4 years:Experiences operating an Open Source Indicators Forecasting System." The current research in this area is focused on extending existing ML algorithms as well as network science measures to these complex structures. Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao. July 21: Clarified that the workshop this year will be held in-person. Through invited talks and presentations by the participants, this workshop will bring together current advances in Network Science as well as Machine Learning, and set the stage for continuing interdisciplinary research discussions. How to do good research, Get it published in SIGKDD and get it cited! 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. Naftali Cohen (JP Morgan Chase & New York University), Eren Kurshan (Bank of America & Columbia University), Senthil Kumar (Capital One), Susan Tibbs (Financial Institutions Regulatory Authority, FINRA), Tucker Balch (JP Morgan Chase & Georgia Institute of Technology), and Kevin Compher (Securities Exchange Commission).