SIAM International Conference on Data Mining (SDM 2022), (Acceptance Rate: 26%), accepted. All deadlines are at 11:59 PM anytime in the world. We invite researchers to submit either full-length research papers (8 pages) or extended abstracts (2 pages) describing novel contributions and preliminary results, respectively, to the topics above; a more extensive list of topics is available on the Workshop website. 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. Both the research papers track and the applied data science papers track will take . AI for infrastructure management and congestion. This is a 1-day workshop involving talks by pioneer researchers from respective areas, poster presentations, and short talks of accepted papers. At least one author of each accepted submission must be present at the workshop. Submissions are due by 12 November 2021. Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest. Attendance is open to all registered participants. Brave new ideas to learn AI models under bias and scarcity. Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, and Liang Zhao. Yuyang Gao, Giorgio Ascoli, Liang Zhao. Knowledge Discovery and Data Mining is an interdisciplinary area focusing As a result, many AI/ML systems faced serious performance challenges and failures. Authors are strongly encouraged to make data and code publicly available whenever possible. 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. Yuanqi Du*, Shiyu Wang* (co-first author), Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao. These challenges and issues call for robust artificial intelligence (AI) algorithms and systems to help. 2022. SIAM International Conference on Data Mining (SDM 2023) (Acceptance Rate: 27.4%), accepted. Neural Networks, (impact factor: 8.05), accepted. The deep learning community must often confront serious time and hardware constraints from suboptimal architectural decisions. These models can also generate instant feedback to instructors and help them to improve their teaching effectiveness. At the same time, multimodal hate-speech detection is an important problem but has not received much attention. Precision agriculture and farm management, Development of open-source software, libraries, annotation tools, or benchmark datasets, Bias/equity in algorithmic decision-making, AI for ITS time-series and spatio-temporal data analyses, AI for the applications of transportation, Applications and techniques in image recognition based on AI techniques for ITS, Applications and techniques in autonomous cars and ships based on AI techniques. 17th International Workshop on Mining and Learning with Graphs. Attendance is open to all, subject to any room occupancy constraints. The AAAI Workshop on Machine Learning for Operations Research (ML4OR) builds on the momentum that has been directed over the past 5 years, in both the OR and ML communities, towards establishing modern ML methods as a first-class citizen at all levels of the OR toolkit. This workshop aims to bring together researchers and practitioners working on different facets of these problems, from diverse backgrounds to share challenges, new directions, recent research results, and lessons from applications. Merge remote-tracking branch 'origin/master', 2. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. MLG 2022 - 17th International Workshop on Mining and Learning with Graphs Liang Zhao's Homepage - Emory University Poster/short/position papers submission deadline: Nov 5, 2021Full paper submission deadline: Nov 5, 2021Paper notification: Dec 3, 2021. search, ranking, recommendation, and personalization. Xuchao Zhang, Liang Zhao, Zhiqian Chen, and Chang-Tien Lu. Call for Papers Document Intelligence Workshop @ KDD 2022 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. Graph Neural Networks: Foundations, Frontiers, and Applications. Yuyang Gao, Tong Sun, Guangji Bai, Siyi Gu, Sungsoo Hong, and Liang Zhao. Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, and Yanfang Ye. At the AAAI-22 Workshop on Scientific Document Understanding (SDU@AAAI-22), we aim to gather insights into the recent advances and remaining challenges on scientific document understanding. Some will be selected for spotlight talks, and some for the poster session. Previous healthcare-related workshops focus on how to develop AI methods to improve the accuracy and efficiency of clinical decision-making, including diagnosis, treatment, triage. InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2013), demo track, pp. Identification of key challenges and opportunities for future research. Researchers from related domains are invited to submit papers on recent advanced technologies, resources, tools and challenges for VTU. 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. Note: The workshop is a collaboration between NASSMA organisation, Deepmind and UM6P. We hope to build upon that success. ACM RecSys 2022 will be held in Seattle, USA, from September 18 - 23, 2022. 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). The invited speakers, who are well-recognized experts of the field, will give a 30 minute talk. Short or position papers of up to 4 pages are also welcome. Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features. Novel mechanisms for eliciting and consuming user feedback, recommender, structured and generative models, concept acquisition, data processing, optimization; HCI and visualization challenges; Analysis of human factors/cognition and user modelling; Design, testing and assessment of IML systems; Studies on risks of interaction mechanisms, e.g., information leakage and bias; Business use cases and applications. Liming Zhang, Dieter Pfoser, Liang Zhao. In general, AI techniques are still not widely adopted in the real world. Our topics of interest span over prediction, planning, and decision problems for online marketplaces, including but not limited to. The scope of the workshop includes, but is not limited to, the following areas: We also invite participants to an interactive hack-a-thon. Submissions will be peer reviewed, single-blinded. This one-day workshop will bring concentrated discussions on self-supervision for the field of speech/audio processing via keynote speech, invited talks, contributed talks and posters based on community-submitted high-quality papers, and the result representation of SUPERB and Zero Speech challenge. Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu. 1503-1512, Aug 2015. "GA-based principal component selection for production performance estimation in mineral processing." Attendance is virtual and open to all. For program deadlines, click on the Admissions and Regulations tab on the specific page of study. and facilitate discussions and collaborations in developing trustworthy AI methods that are reliable and more acceptable to physicians. The workshop attracted about 100 attendees. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), short paper (acceptance rate: 19.9%), Singapore, Dec 2018, accepted. Submissions of technical papers can be up to 7 pages excluding references and appendices. 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. Position papers (4 pages in length for main content + 2 pages for references in AAAI format): we are seeking position papers that advocate for a particular approach or set of approaches, or present an overview of a promising relevant research area. 11-13. How to do good research, Get it published in SIGKDD and get it cited! A new and comprehensive view of AI Safety must cover a wide range of AI paradigms, including systems that are application-specific as well as those that are more general, considering potentially unanticipated risks. The cookies is used to store the user consent for the cookies in the category "Necessary". Liang Zhao, Ting Hua, Chang-Tien Lu, and Ing-Ray Chen. 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. 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. Jinliang Ding, Liang Zhao, Changxin Liu, and Tianyou Chai. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Motif-guided Heterogeneous Graph Deep Generation. As deep learning problems become increasingly complex, network sizes must increase and other architectural decisions become critical to success. IEEE Transactions on Knowledge and Data Engineerings (TKDE), (impact factor: 6.977), 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/. Integration of Deep Learning and Relational Learning. In this workshop, we aim to address the trustworthy issues of clinical AI solutions. 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. Additionally, adversaries continue to develop new attacks. Shuo Lei, Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu. Proceedings of the ACM on Human-Computer Interaction (CSCW 2022), to appear, 2022. Short papers 10m presentation and 5m discussion. This calls for novel methods and new methodologies and tools to address quality and reliability challenges of ML systems. The 48th International Conference on Parallel Processing (ICPP 2019), (acceptance rate: 20%), accepted, Kyoto, Japan. At least one author of each accepted submission must register and present their paper at the workshop. 20, 2022: We have announced Call for Nominations: , Jan. 25, 2022: Sponsorship Opportunities is available at, Jan. 6, 2022: Call for KDD Cup Proposals is available at, Dec. 26, 2021: Call for Workshop Proposals is available at, Dec. 26, 2021: Call for Tutorials is available at, Nov. 24, 2021: Those who are interested in serving as a PC, please feel free to fill in this, Nov. 12, 2021: Call for Research Track Papers is available at, Nov. 12, 2021: Call for Applied Data Science Track Papers is available at. 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. Scientists and engineers in diverse domains are increasingly relying on using AI tools to accelerate scientific discovery and engineering design. The AAAI-22 workshop program includes 39 workshops covering a [] 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. It is also central for tackling decision-making problems such as reinforcement learning, policy or experimental design. Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao. Finally, the workshop will welcome papers that describe the release of privacy-preserving benchmarks and data sets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples. 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. RL4ED is intended to facilitate tighter connections between researchers and practitioners interested in the broad areas of reinforcement learning (RL) and education (ED). The consideration and experience of adversarial ML from industry and policy making. Full (8 pages) and short (4 pages, work in progress) papers, AAAI style. Proceedings of the IEEE (impact factor: 9.237), vol. The aim of the hack-a-thon is not only to foster innovation and potentially provide answers to outstanding research problems, but rather to engage the community and create new collaborations. Knowledge discovery from various data sources has gained the attention of many practitioners in recent decades. ; (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. Frontiers in Neurorobotics, (impact factor: 2.574), accepted. However, workshop organizers may set up any archived publication mechanism that best suits their workshop. It is a forum to bring attention towards collecting, measuring, managing, mining, and understanding multimodal disinformation, misinformation, and malinformation data from social media. Linguistic analysis of business documents. 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. However, theoreticians and practitioners of AI and Safety are confronted with different levels of safety, different ethical standards and values, and different degrees of liability, that force them to examine a multitude of trade-offs and alternative solutions. Paper Submission Deadline: 23:59 on Thursday. Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior. DOI:https://doi.org/10.1145/3339823. We plan to invite 2-4 keynote speakers from prestigious universities and leading industrial companies. The objective of this workshop is to discuss the winning submissions of the Submissions to the Amazon KDD Cup 2022 issingle-blind (author names and affiliations should be listed). 2022. Continuous refinement of AI models using active/online learning. Dazhou Yu, Guangji Bai, Yun Li, and Liang Zhao. We will receive the paper on the CMT system. You can optionally export all deadlines to Google Calendar or .ics . Accepted submissions will be notified latest by August 7th, 2022. We received 38 paper submissions and accepted 23 of them. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. Registration information will be mailed directly to all invited participants in December. 5, pp. ICONF Guangji Bai and Liang Zhao. 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. We expect 50-65 people in the workshop. Modern surveillance systems employ tools and techniques from artificial intelligence and machine learning to monitor direct and indirect signals and indicators of disease activities for early, automatic detection of emerging outbreaks and other health-relevant patterns. Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. KDD - ACM Conferences 2020. Novel algorithmic solutions to causal inference or discovery problems using information-theoretic tools or assumptions. [paper] The robust development and assured deployment of AI systems: Participants will discuss how to leverage and update common software development paradigms, e.g., DevSecOps, to incorporate relevant aspects of system-level AI assurance. 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. Zirui Xu, Fuxun Xu, Liang Zhao, and Xiang Chen. Paper Final Version Due: Monday August 1, 2022. Check the deadlines for submitting your application. 2999-3006, New Orleans, US, Feb 2018. It has profoundly impacted several areas, including computer vision, natural language processing, and transportation. 25, 2022: We have announced Call for Nominations: , Mar. Knowledge and Information Systems (KAIS), (Impact Factor: 2.531), to appear, 2022. SIAM International Conference on Data Mining (SDM 2023) (Acceptance Rate: 27.4%), accepted. We invite paper submission on the following (and related) topics: The workshop will be a 1 day meeting comprising several invited talks from distinguished researchers in the field, spotlight lightning talks and a poster session where contributing paper presenters can discuss their work, and a concluding panel discussion focusing on future directions. As Artificial Intelligence (AI) begins to impact our everyday lives, industry, government, and society with tangible consequences, it becomes increasingly important for a user to understand the reasons and models underlying an AI-enabled systems decisions and recommendations. All papers must be submitted in PDF format using the AAAI-22 author kit. For example: The workshop will be a 1-day event with a number of invited talks by prominent researchers, a panel discussion, and a combination of oral and poster presentations of accepted papers. Metagraph Aggregated Heterogeneous Graph Neural Network for Illicit Traded Product Identification in Underground Market. Journal of Biomedical Semantics, (impact factor: 1.845), 2018, accepted. The workshop page ishttps://sites.google.com/view/aaaiwfs2022, and it will include the most up-to-date information, including the exact schedule. We also use third-party cookies that help us analyze and understand how you use this website. Event Prediction in the Big Data Era: A Systematic Survey. 2022. RAISAs systems-level perspective will be emphasized via three main thrusts: AI threat modeling, AI system robustness, explainable AI, system lifecycle attacks, system verification and validation, robustness benchmarks and standards, robustness to black-box and white-box adversarial attacks, defenses against training, operational and inversion attacks, AI system confidentiality, integrity, and availability, AI system fairness and bias.
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