Novel methods to learn from scarce/sparse, or heterogenous, or multimodal data. 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. Topics of interest include but are not limited to: (1) Survey papers summarizing recent advances in RL with applicability to ED; (2) Developing toolkits and datasets for applying RL methods to ED; (3) Using RL for online evaluation and A/B testing of different intervention strategies in ED; (4) Novel applications of RL for ED problem settings; (5) Using pedagogical theories to narrow the policy space of RL methods; (6) Using RL methodology as a computational model of students in open-ended domains; (7) Developing novel offline RL methods that can efficiently leverage historical student data; (8) Combining statistical power of RL with symbolic reasoning to ensure the robustness for ED. Graph Neural Networks: Foundations, Frontiers, and Applications. Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2021), (acceptance rate: 21.0%), accepted. SDU accepts both long (8 pages including references) and short (4 pages including references) papers. We consider submissions that havent been published in any peer-reviewed venue (except those under review). https://doi.org/10. Necessary cookies are absolutely essential for the website to function properly. This half day workshop will focus on research into the use of AI techniques to extract knowledge from unstructured data in financial services. We encourage authors to contact the organizers to discuss possible overlap. 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. 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. Authors are strongly encouraged to make data and code publicly available whenever possible. 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. Generative Adversarial Learning of Protein Tertiary Structures. 4 (2014): 185-195. ", ACM Transactions on Spatial Algorithms and Systems (TSAS), (Acceptance Rate: 11%), Volume 2 Issue 4, Acticle No. DeepGAR: Deep Graph Learning for Analogical Reasoning. All accepted papers will be archived on the workshop website, but there will not be formal proceedings. Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. Invited speakers, panels, poster sessions, and presentations. Qingzhe Li, Liang Zhao, Yi-Ching Lee, Avesta Sassan, and Jessica Lin. 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. [Best Paper Award Shortlist]. In addition, several invited speakers with distinguished professional background will give talks related the frontier topics of GNN. Attendance is virtual and open to all. Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph. 27, 2022: Please check out Speical Days at, Apr. What AI safety considerations and experiences are relevant from industry? The trustworthy issues of clinical AI methods were not discussed. 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. All submissions must be anonymous and conform to AAAI standards for double-blind review. Interesting challenges in this domain include the drastic increase of work from home or remote work, the imbalance between the demand and supply of the job market, the popularity of independent workers, the capability of helping job seekers on their whole job seeking journey and career development, the different objectives and behaviors of all major stakeholders in the ecosystem, e.g. Topics of interest include, but are not limited to: One day, comprising keynote, paper presentations and panel sessions. P. 6205, succursale Centre-villeMontral, (Qubec) H3C 3T5Canada. ECoST: Energy-Efficient Co-Locating and Self-Tuning MapReduce Applications. [Best Paper Award]. We invite a long research paper (8 pages) and a demo paper (4 pages) (including references). Counter-intuitive behaviors of ML models will largely affect the public trust on AI techniques, while a revolution of machine learning/deep learning methods may be an urgent need. Time Series Clustering in Linear Time Complexity. We invite submissions of full papers, as well as works-in-progress, position papers, and papers describing open problems and challenges. The workshop will be a one-day workshop, featuring speakers, panelists, and poster presenters from machine learning, biomedical informatics, natural language processing, statistics, behavior science. What safety engineering considerations are required to develop safe human-machine interaction? The design and implementation of these AI techniques to meet financial business operations require a joint effort between academia researchers and industry practitioners. Merge remote-tracking branch 'origin/master', 2. Neil T. Heffernan, Worcester Polytechnic Institute (Worcester, MA, USA), Andrew S. Lan, University of Massachusetts Amherst (Amherst, MA, USA), Anna N. Rafferty, Carleton College (Northfield, MN, USA), Adish Singla, Max Planck Institute for Software Systems (Saarbrucken, Germany). We invite thought-provoking submissions and talks on a range of topics in these fields. Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad. We also use third-party cookies that help us analyze and understand how you use this website. Check the CFP for details Deadline: ICDM 2020 . These challenges are widely studied in enterprise networks, but there are many gaps in research and practice as well as novel problems in other domains. 1059-1072, May 1 2017. Deep Classifier Cascades for Open World Recognition. Motif-guided Heterogeneous Graph Deep Generation. 2022. Identification of key challenges and opportunities for future research. Semantic understanding of business documents. anomaly detection, and ensemble learning. 9, no. Thirty-First AAAI Conference on Artificial Intelligence, pp. The workshop welcomes the submission of work on, but not limited to, the following research directions. Submission at:https://easychair.org/my/conference?conf=edsmls2022. for causal estimation in behavioral science. Algorithms and theories for explainable and interpretable AI models. Characterization of fundamental limits of causal quantities using information theory. Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. NOTE: Mandatory abstract deadline: 2022-08-08 Deadline: AAAI 157. We send a public call and we assume the workshop will be of interest to many AAAI main conference audiences; we expect 50 participants. Optimal transport theory, including statistical and geometric aspects; Gromov-Wasserstein distance and its variants; Bayesian inference for/with optimal transport; Gromovization of machine learning methods; Optimal transport-based generative modeling. ACM Computing Surveys (CSUR), (impact factor: 10.28), accepted. ^All accepted WSDM papers are associated with an interactive poster presentation in addition to oral presentations. This cookie is set by GDPR Cookie Consent plugin. Deep Graph Translation. Novel AI-based techniques to improve modeling of engineering systems. The workshop is organized by paper presentations.The length of the workshop: 1-day, 6-8 pages for full papers2-4 for poster/short/position papers, Submission URL:https://easychair.org/conferences/?conf=aaai-2022-workshop, Wenzhong Guo (Fuzhou University, fzugwz@163.com), Chin-Chen Chang (Feng Chia University, alan3c@gmail.com), Chi-Hua Chen (Fuzhou University, chihua0826@gmail.com), Haishuai Wang (Fairfield University & Harvard University, hwang@fairfield.edu), Feng-Jang Hwang (University of Technology Sydney), Cheng Shi (Xian University of Technology), Ching-Chun Chang (National Institute of Informatics, Japan). 4701-4707, San Francisco, California, USA, Feb 2017. We received 38 paper submissions and accepted 23 of them. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022. The goal of this workshop is to offer an opportunity to appreciate the diversity in applications, to draw connections to inform decision optimization across different industries, and to discover new problems that are fundamental to marketplaces of different domains. ISBN: 978-981-16-6053-5. Authors are invited to send a contribution in the AAAI-22 proceedings format. KDD 2022 is a dual-track conference that provides distinct programming in research and applied data science. ML4OR will serve as an interdisciplinary forum for researchers in both fields to discuss technical issues at this interface and present ML approaches that apply to basic OR building blocks (e.g., integer programming solvers) or specific applications. November 11-17, 2023. "Controllable Data Generation by Deep Learning: A Review." Information extraction from text and semi-structured documents. RL4ED is intended to facilitate tighter connections between researchers and practitioners interested in the broad areas of reinforcement learning (RL) and education (ED). ML4OR will place particular emphasis on: (1) ML methodologies for enhancing traditional OR algorithms for integer programming, combinatorial optimization, stochastic programming, multi-objective optimization, location and routing problems, etc. Yiming Zhang, Yujie Fan, Wei Song, Shifu Hou, Yanfang Ye, Xin Li, Liang Zhao, Chuan Shi, Jiabin Wang, Qi Xiong. Consult the list of programs available in the next session. Participants in the hack-a-thon will be asked to either register as a team or be randomly assigned to a team after registration. For program deadlines, click on the Admissions and Regulations tab on the specific page of study. Attendance is open to all, subject to any room occupancy constraints. "A Uniform Representation for Trajectory Learning Tasks", 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2017), short paper, DOI=10.1145/3139958.3140017, Redondo Beach, CA, USA, Nov 2017. Liang Zhao, Junxiang Wang, and Xiaojie Guo. 2, no. All submissions must be anonymous and conform to AAAI standard for double-blind review. 2022. Large-scale Cost-aware Classification Using Feature Computational Dependency Graph. 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. Checklist for Revising a SIGKDD Data Mining Paper, How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering, https://researcher.watson.ibm.com/researcher/view_group.php?id=144, IEEE International Conference on Big Data (, AAAI Conference on Artificial Intelligence (, IEEE International Conference on Data Engineering (, SIAM International Conference on Data Mining (, Pacific-Asia Conference on Knowledge Discovery and Data Mining (, ACM SIGKDD International Conference on Knowledge discovery and data mining (, European Conference on Machine learning and knowledge discovery in databases (, ACM International Conference on Information and Knowledge Management (, IEEE International Conference on Data Mining (, ACM International Conference on Web Search and Data Mining (, 18.4% (181/983, research track), 22.5% (112/497, applied data science track), 59.1% (107/181, research track), 35.7% (40/112, applied data science track), 17.4% (130/748, research track), 22.0% (86/390, applied data science track), 49.2% (64/130, research track), 41.9% (36/86, applied data science track), 18.1% (142/784, research track), 19.9% (66/331, applied data science track), 49.3% (70/142, research track), 60.1% (40/66, applied data science track), 18.5% (194/1046, overall), 9.1% (95/?, regular paper), ?% (99/?, short paper), 19.8% (188/948, overall), 8.9% (84/?, regular paper), ?% (104/?, short paper), 19.9% (155/778, overall), 9.3% (72/?, regular paper), ?% (83/?, short paper), 19.6% (178/904, overall), 8.6% (78/?, regular paper), ?% (100/?, short paper), 19.6% (202/1031, long paper), 22.7% (107/471, short paper), 21.8% (38/174m applied research), 17% (147/826, long paper), 23% (96/413, short paper), 25% (demo), 34% (industry paper), Short papers are presented at poster sessions, 20% (171/855, long paper), 28% (119/419, short paper), 38% (30/80, demo paper), 23% (160/701, long paper), 24% (55/234, short paper), 54 extended short papers (6 pages), 26% (94/354, research track), 26% (37/143, applied ds track), 15% (23/151, journal track), 27.8% (164/592, overall), 9.8% (58/592, long presentation), 18.1% (107/592, regular), 28.2% (129/458, overall), 9.8% (45/458, long presentation), 18.3% (84/458, regular), 29.6% (91/307, overall), 12.7% (39/307, long presentation), 16.9% (52/307, regular), 40.4% (34/84, long presentation), 59.5% (50/84, short presentation)^, 16.3% (84/514 in which 3 papers are withdrawn/rejected after the acceptance), 28.4% (23/81, long presentation), 71.6% (58/81, short presentation)^, 30% (24/80, long presentation), 70% (56/80, short presentation)^, 29.8% (20/67, long presentation), 70.2% (47/67, short presentation)^, 53.8% (21/39, long presentation), 46.2% (18/39, short presentation)^. Yuyang Gao, Giorgio Ascoli, Liang Zhao. KDD 2022. It is one of the key bottlenecks for financial services companies to improve their operating productivity. This workshop aims to bring researchers from these diverse but related fields together and embark on interesting discussions on new challenging applications that require complex system modeling and discovering ingenious reasoning methods. 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. 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.
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