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. Workshops are one day unless otherwise noted in the individual descriptions. The 9th International Conference on Learning Representations (ICLR 2021), (acceptance rate: 28.7%), accepted. Microsoft's Conference Management Toolkit is a hosted academic conference management system. Apr 11-14, 2022. Current rates of progress are insufficient, making it impossible to meet this goal without a technological paradigm shift. Please refer and submit through theLearning Network Architecture During Trainingworkshop website, which has more detailed information. Because of the time needed to complete the formalities for entering Canada and Quebec, the admission period for international applicants ends several weeks before the session begins. Knowledge and Information Systems (KAIS), (impact factor: 2.936), accepted. Submitted technical papers can be up to 4 pages long (excluding references and appendices). Guangji Bai and Liang Zhao. 625-634, New Orleans, US, Dec 2017. Liang Gou, Bosch Research (IEEE VIS liaison), Claudia Plant, University of Vienna (KDD liaison), Alvitta Ottley, Washington University, St. Louis, Junming Shao, University of Electronic Science and Technology of China, Visualization in Data Science (VDS at ACM KDD and IEEE VIS), Visualization in Data Science (VDS at ACM KDD and IEEE VIS). Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. KDD 2022 is a dual-track conference that provides distinct programming in research and applied data science. At the AAAI 2022 Workshop on Video Transcript Understanding (VTU @ AAAI 2022), we aim to bring together researchers from various domains to make the best of the knowledge that all these videos contain. Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. "Online and Distributed Robust Regressions under Adversarial Data Corruption", in Proceedings of the IEEE International Conference on Data Mining (ICDM 2017) , regular paper; (acceptance rate: 9.25%), pp. 40, no. The workshop will include original contributions on theory, methods, systems, and applications of data mining, machine learning, databases, network theory, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based healthcare applications, with a focus on applications in population and personalized health. 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. Some specific topics in the context of scientific discovery and engineering design include (but not limited to): This will be a one day workshop with a number of paper presentations and poster spotlights, a poster session, several invited talks, and a panel discussion. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu. Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted. Yuanqi Du, Xiaojie Guo, Yinkai Wang, Amarda Shehu, Liang Zhao. Novel algorithms and theories to improve model robustness. The workshop will focus on the application of AI to problems in cyber-security. Benchmarks to reliably evaluate attacks/defenses and measure the real progress of the field. 7, no. Junxiang Wang, Liang Zhao, Yanfang Ye, and Yuji Zhang. Virtual . Track 2 focuses on the state of the art advances in the computational jobs marketplace. The acceptance decisions will take in account novelty, technical depth and quality, insightfulness, depth, elegance, practical or theoretical impact, reproducibility and presentation. Andrew White, University of RochesterDr. NOTE: May 19: Notification. Invited speakers, panels, poster sessions, and presentations. Yuyang Gao, Tanmoy Chowdhury (co-first author), Lingfei Wu, Liang Zhao. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Universit de MontralOffice of Admissions and RecruitmentC. Novel AI-enabled generative models for system design and manufacturing. Explainable Agency captures the idea that AI systems will need to be trusted by human agents and, as autonomous agents themselves must be able to explain their decisions and the reasoning that produced their choices (Langley et al., 2017). We invite participants to submit papers by the 12th of November, based on but not limited to, the following topics: RL in various formalisms: one-shot games, turn-based, and Markov games, partially-observable games, continuous games, cooperative games; deep RL in games; combining search and RL in games; inverse RL in games; foundations, theory, and game-theoretic algorithms for RL; opponent modeling; analyses of learning dynamics in games; evolutionary methods for RL in games; RL in games without the rules; search and planning; and online learning in games. Dynamic Tracking and Relative Ranking of Airport Threats from News and Social Media. Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth Loparo, and Liang Zhao. We welcome full paper submissions (up to 8 pages, excluding references or supplementary materials). It further combines academia and industry in a quest for well-founded practical solutions. Deadline: Fri Jun 09 2023 04:59:00 GMT-0700 Yahoo! Online and Distributed Robust Regressions with Extremely Noisy Labels. The thematic sessions will be structured into short pitches and a common panel slot to discuss both individual paper contributions and shared topic issues. The 39th IEEE International Conference on Data Engineering (ICDE 2023), accepted. 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. 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. Dazhou Yu, Guangji Bai, Yun Li, and Liang Zhao. ), 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. Submission at:https://easychair.org/my/conference?conf=edsmls2022. ", ACM Transactions on Spatial Algorithms and Systems (TSAS), (Acceptance Rate: 11%), Volume 2 Issue 4, Acticle No. Continuous V&V and predictability of AI safety properties, Runtime monitoring and (self-)adaptation of AI safety, Accountability, responsibility and liability of AI-based systems, Avoiding negative side effects in AI-based systems, Role and effectiveness of oversight: corrigibility and interruptibility, Loss of values and the catastrophic forgetting problem, Confidence, self-esteem and the distributional shift problem, Safety of AGI systems and the role of generality, Self-explanation, self-criticism and the transparency problem, Regulating AI-based systems: safety standards and certification, Human-in-the-loop and the scalable oversight problem, Experiences in AI-based safety-critical systems, including industrial processes, health, automotive systems, robotics, critical infrastructures, among others. Computer Science and Engineering, INESC-ID, IST Ulisboa, Lisbon, Portugal currently at Sorbonne University, Paris, France silvia.tulli@gaips.inesc-id.pt), Prashan Madumal (Science and Information Systems, University of Melbourne, Parkville, Australia pmathugama@student.unimelb.edu.au), Mark T. Keane (School of Computer Science, University College Dublin, Dublin, Ireland mark.keane@ucd.ie), David W. Aha (Navy Center for Applied Research in AI, Naval Research Laboratory, Washington, DC, USA david.aha@nrl.navy.mil), Adam Johns (Drexel University, Philadelphia, PA USA), Tathagata Chakraborti (IBM Research AI, Cambridge, MA USA), Kim Baraka (VU University Amsterdam, Netherlands), Isaac Lage (Harvard University, Cambridge, MA USA), David Martens (University of Antwerp, Belgium), Mohamed Chetouani (Sorbonne Universit, Paris, France), Peter Flach (University of Bristol, United Kingdom), Kacper Sokol (University of Bristol, United Kingdom), Ofra Amir (Technion, Haifa, Israel), Dimitrios Letsios (Kings College London, London, United Kingdom), Supplemental workshop site:https://sites.google.com/view/eaai-ws-2022/topic. 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. ACM, 2013. Comparison or integration of self-supervised learning methods and other semi-supervised and transfer learning methods in speech and audio processing tasks. It is one of the key bottlenecks for financial services companies to improve their operating productivity. Xuchao Zhang, Xian Wu, Fanglan Chen, Liang Zhao, Chang-Tien Lu. We will receive the paper on the CMT system. AAAI, specifically, is a great venue for our workshop because its audience spans many ML and AI communities. Typically, we receive around 40~60 submissions to each previous workshop. Previously published work (or under-review) is acceptable. The AAAI-22 workshop program includes 39 workshops covering a [] Estimate of the audience size: 400-500 attendees (based on the number of attendees in previous DLG workshops in KDD19, AAAI20, KDD20 and AAAI21). in Proceedings of the IEEE International Conference on Data Mining (ICDM 2016), regular paper, (acceptance rate: 8.5%), pp. Martin Michalowski, PhD, FAMIA (Co-chair), University of Minnesota; Arash Shaban-Nejad, PhD, MPH (Co-chair), The University of Tennessee Health Science Center Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics; Simone Bianco, PhD (Co-chair), IBM Almaden Research Center; Szymon Wilk, PhD, Poznan University of Technology; David L. Buckeridge, MD, PhD, McGill University; John S. Brownstein, PhD, Boston Childrens Hospital, Workshop URL:http://w3phiai2022.w3phi.com/. Babies learn their first language through listening, talking, and interacting with adults. All papers will be peer-reviewed, single-blinded (i.e., please include author names/affiliations/email addresses on your first page). information bottleneck principle). Submission instructions will be available at the workshop web page. 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. At least one author of each accepted submission must be present at the workshop. The workshop will focus on two thrusts: 1) Exploring how we can leverage recent advances in RL methods to improve state-of-the-art technology for ED; 2) Identifying unique challenges in ED that can help nurture technical innovations and next breakthroughs in RL. California, United Stes. This AAAI-22 workshop on AI for Decision Optimization (AI4DO) will explore how AI can be used to significantly simplify the creation of efficient production level optimization models, thereby enabling their much wider application and resulting business values.The desired outcome of this workshop is to drive forward research and seed collaborations in this area by bringing together machine learning and decision-making from the lens of both dynamic and static optimization models. The submission website ishttps://cmt3.research.microsoft.com/OTSDM2022. simulation, evaluation and experimentation. The submission website ishttps://cmt3.research.microsoft.com/PracticalDL2022. 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 papers may consist of up to seven pages of technical content plus up to two additional pages for references. July 21: Clarified that the workshop this year will be held in-person. Saliency-Augmented Memory Completion for Continual Learning. You can optionally export all deadlines to Google Calendar or .ics . Three specific roles are part of this format: session chairs, presenters and paper discussants. Why did so many AI/ML models fail during the pandemic? job seekers, employers, recruiters and job agents. Track 1 covers the issues and algorithms pertinent to general online marketplaces as well as specific problems and applications arising from those diverse domains, such as ridesharing, online retail, food delivery, house rental, real estate, and more. Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Chang-Tien Lu, Sai Dinakarrao, Houman Homayoun, Liang Zhao. All deadlines are at 11:59 PM anytime in the world. Identification of information-theoretic quantities relevant for causal inference and discovery. However, the performance and efficiency of these techniques are big challenges for performing real-time applications. 9, no. We welcome full research papers, position papers, and extended abstracts. 4 pages) papers describing research at the intersection of AI and science/engineering domains including chemistry, physics, power systems, materials, catalysis, health sciences, computing systems design and optimization, epidemiology, agriculture, transportation, earth and environmental sciences, genomics and bioinformatics, civil and mechanical engineering etc. Scientists and engineers in diverse domains are increasingly relying on using AI tools to accelerate scientific discovery and engineering design. At least one author of each accepted submission must be present at the workshop. Merge remote-tracking branch 'origin/master', 2. For instance, advanced driver assistance systems and autonomous cars have been developed based on AI techniques to perform forward collision warning, blind spot monitoring, lane departure warning systems, traffic sign recognition, traffic safety, infrastructure management and congestion, and so on. If it turns out that the architecture is not appropriate for the task, the user must repeatedly adjust the architecture and retrain the network until an acceptable architecture has been obtained. Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao. 2022. 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. Submissions are limited to 4 pages, not including references. 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. [code] The KDD 2022 program promises to be the most robust and diverse to date, with keynote presentations, industry-led sessions, workshops, and tutorials spanning a wide range of topics - from data-driven humanitarian mapping and applied data science in healthcare to the uses of artificial intelligence (AI) for climate mitigation and decision . Kyoto . 2020. Online marketplaces exist in a diverse set of domains and industries, for example, rideshare (Lyft, DiDi, Uber), house rental (Airbnb), real estate (Beke), online retail (Amazon, Ebay), job search (LinkedIn, Indeed.com, CareerBuilder), and food ordering and delivery (Doordash, Meituan). Amir A. Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng. It drives discoveries in business, economy, biology, medicine, environmental science, the physical sciences, the humanities and social sciences, and beyond. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. Xiaojie Guo, Yuanqi Du, Liang Zhao. In addition, any other work on dialog research is welcome to the general technical track. Ranking, acceptance rate, deadline, and publication tips. 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. Share. This cookie is set by GDPR Cookie Consent plugin. Causal inference is one of the main areas of focus in artificial intelligence (AI) and machine learning (ML) communities. 4701-4707, San Francisco, California, USA, Feb 2017. The review process will be single blind. This workshop will follow a dual-track format. Graph neural networks on node-level, graph-level embedding, Joint learning of graph neural networks and graph structure, Learning representation on heterogeneous networks, knowledge graphs, Deep generative models for graph generation/semantic-preserving transformation, Graph2seq, graph2tree, and graph2graph models, Spatial and temporal graph prediction and generation, Learning and reasoning (machine reasoning, inductive logic programming, theory proving), Natural language processing (information extraction, semantic parsing, text generation), Bioinformatics (drug discovery, protein generation, protein structure prediction), Reinforcement learning (multi-agent learning, compositional imitation learning), Financial security (anti-money laundering), Cybersecurity (authentication graph, Internet of Things, malware propagation), Geographical network modeling and prediction (Transportation and mobility networks, social networks), Computer vision (object relation, graph-based 3D representations like mesh), Lingfei Wu (JD.Com Silicon Valley Research Center),lwu@email.wm.edu, 757-634-5455, https://sites.google.com/a/email.wm.edu/teddy-lfwu/, Jian Pei (Simon Fraser University), jian_pei@sfu.ca, 778-782-6851, https://sites.google.com/view/jpei/jian-peis-homepage, Jiliang Tang (Michigan State University), tangjili@msu.edu, 408-744-2053, https://www.cse.msu.edu/~tangjili/, Yinglong Xia (Facebook AI), yinglongxia@gmail.com, 213-309-9908, https://sites.google.com/site/yinglongxia/, Xiaojie Guo (JD.Com Silicon Valley Research Center), Xguo7@gmu.edu, 571-224-5527, https://sites.google.com/view/xiaojie-guo-personal-site, Sutanay Choudhury (Pacific Northwest National Lab), Stephan Gnnemann (Technical University of Munich), Shen Wang, (University of Illinois at Chicago), Yizhou Sun (University of California, Los Angeles), Lingfei Wu (JD.Com Silicon Valley Research Center), Zhan Zheng (Washington University in St. Louis), Feng Chen (University at Albany State University of New York), Development of corpora and annotation guidelines for multimodal fact checking, Computational models for multimodal fact checking, Development of corpora and annotation guidelines for multimodal hate speech detection and classification, Computational models for multimodal hate speech detection and classification, Analysis of diffusion of Multimodal fake news and hate speech in social networks, Understanding the impact of the hate content on specific groups (like targeted groups), Fake news and hate speech detection in low resourced languages, Vulnerability, sensitivity and attacks against ML, Adversarial ML and adversary-based learning models, Case studies of successful and unsuccessful applications of ML techniques, Correctness of data abstraction, data trust, Choice of ML techniques to meet security and quality, Size of the training data, implied guaranties, Application of classical statistics to ML systems quality, Sensitivity to data distribution diversity and distribution drift, The effect of labeling costs on solution quality (semi-supervised learning), Software engineering aspects of ML systems and quality implications, Testing of the quality of ML systems over time, Quality implication of ML algorithms on large-scale software systems, Explainable/Interpretable Machine Learning, Fairness, Accountability and Transparency, Interactive Teaching Strategies and Explainability, Novel Research Contribution describing original methods and/or results (6 pages plus references), Surveys summarizing and organizing recent research results (up to 8 pages plus references), Demonstrations detailing applications of research findings, and/or debating relevant challenges and issues in the field (4 pages plus references), Constraint satisfaction and programming (CP), (inductive) logic programming (LP and ILP), Learning with Multi-relational graphs (alignment, knowledge graph construction, completion, reasoning with knowledge graphs, etc. Xiaosheng Li, Jessica Lin, and Liang Zhao. Data Mining and Knowledge Discovery (DMKD), (impact factor: 3.670), accepted. It will include multiple keynote speakers, invited talks, a panel discussion, and two poster sessions for the accepted papers. [Best Paper Candidate]. 2022. 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. How can we make AI-based systems more ethically aligned? All papers must be submitted in PDF format, using the AAAI-21 author kit and anonymized. Roco Mercado, Massachusetts Institute of Technology. 689-698, Barcelona, Spain, Dec 2016. If you are interested, please send a short email to rl4edorg@gmail.com and we can add you to the invitee list. SIGSPATIAL Special (invited paper), vo. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), vol. Knowledge discovery from various data sources has gained the attention of many practitioners in recent decades. Temporal Domain Generalization with Drift-Aware Dynamic Neural Network. This workshop aims to provide a premier interdisciplinary forum for researchers in different communities to discuss the most recent trends, innovations, applications, and challenges of optimal transport and structured data modeling. Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad. 4498-4505, New Orleans, US, Feb 2018. Submissions should follow the AAAI-2022https://aaai.org/Conferences/AAAI-22/aaai22call/. Full papers are allocated 20m presentation and 10m discussion. Please use vds@ieeevis.org to get in touch with us, or follow us on Twitter at @VisualDataSci. 1, Sec. The workshop attracted about 100 attendees. 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. The impact of robustness assurance on other AI ethics principles: RAISA will also explore aspects related to ethical AI that overlap and interact with robustness concerns, including security, fairness, privacy, and explainability. Submission Guidelines 11, 2022: We have posted the list of accepted Workshops at, Apr. Attendance is open to all; at least one author of each accepted paper must be virtually present at the workshop. Papers will be peer-reviewed and selected for oral and/or poster presentations at the workshop. 1466-1469. Call for Participation The 3rd KDD Workshop on Data-driven Humanitarian Mapping and Policymaking solicits research papers, case studies, vision papers, software demos, and extended abstracts. Attendance is open to all. This calls for novel methods and new methodologies and tools to address quality and reliability challenges of ML systems. [Best Paper Award]. Attendance is open to all registered participants. Integration of AI-based approaches with engineering prototyping and manufacturing. Recently developed tools and cutting-edge methodologies coming from the theory of optimal transport have proved to be particularly successful for these tasks. a fantastic tutorial on SIGKDD'09 by Prof. Eamonn Keogh (UC Riverside). For example, AI tools are built to ease the workload for teachers. Research efforts and datasets on text fact verification could be found, but there is not much attention towards multi-modal or cross-modal fact-verification. In this 2nd instance of GCLR (Graphs and more Complex structures for Learning and Reasoning) workshop, we will focus on various complex structures along with inference and learning algorithms for these structures. Document structure and layout learning and recognition. : Papers must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template. Some will be selected for spotlight talks, and some for the poster session. Online . Liang Zhao, Junxiang Wang, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. 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. December, 12-16, 2022. The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Datasets and Benchmarks Track, accepted. Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. However, ML systems may be non-deterministic; they may re-use high-quality implementations of ML algorithms; and, the semantics of models they produce may be incomprehensible. We encourage long papers, short papers and demo papers. Accepted submissions will be notified latest by August 7th, 2022. "Multi-Task Learning for Spatio-Temporal Event Forecasting." References will not count towards the page limit. Our goal is to build a stronger community of researchers exploring these methods, and to find synergies among these related approaches and alternatives. "Automatic Targeted-Domain Spatiotemporal Event Detection in Twitter." This thread already has a best answer. Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang. It is valuable to bring together researchers and practitioners from different application domains to discuss their experiences, challenges, and opportunities to leverage cross-domain knowledge. and Simone Stumpf (Univ. The workshop will be a one-day meeting and will include a number of technical sessions, a virtual poster session where presenters can discuss their work, with the aim of further fostering collaborations, multiple invited speakers covering crucial challenges for the field of privacy-preserving AI applications, including policy and societal impacts, a tutorial talk, and will conclude with a panel discussion.

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