Please note as per the KDD Call for Workshop Proposals: Note: Workshop papers will not be archived in the ACM Digital Library. [paper] Welcome to PAKDD2022. And considering robustness, input data with noises frequently occur in open-world scenarios, which presents critical challenges for the building of robust AI systems in practice. The 39th IEEE International Conference on Data Engineering (ICDE 2023), accepted. Are you sure you want to create this branch? 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. Workshops will be held Monday and Tuesday, February 28 and March 1, 2022. Furthermore, leveraging AI to connect disparate social networks amongst teachers \\cite{karimi2020towards}, we may be able to provide greater resources for their planning, which have been shown to significantly affect students achievement. Yuyang Gao and Liang Zhao. A Report on the First Workshop on Document Intelligence (DI) at NeurIPS 2019. Accepted papers will not be archived but will be hosted on the workshop website. 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. Papers will be submitted electronically using Easychair. There is a need for the research community to develop novel solutions for these practical issues. upon methodologies and applications for extracting useful knowledge from data [1]. Liang Zhao, Jiangzhuo Chen, Feng Chen, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. Qingzhe Li, Liang Zhao, Jessica Lin and Yi-ching Lee. PLOS ONE (impact factor: 3.534), vo. Everyone in the Top-10 leaderboard submissions will have a guaranteed opportunity for an in-person oral/poster presentation. We accept two types of submissions full research papers no longer than 8 pages (including references) and short/poster papers with 2-4 pages. Computer Communications, (impact factor: 3.34), Elsevier, vo. Applications of causal inference and discovery in machine learning/deep learning motivated by information-theoretic approaches (e.g. Submit to:https://openreview.net/group?id=AAAI.org/2022/Workshop/AdvML, Yinpeng Dong (dyp17@mails.tsinghua.edu.cn, 30 Shuangqing Road, Haidian District, Tsinghua University, Beijing, China, 100084, Phone: +86 18603303421), Yinpeng Dong (Tsinghua University, dyp17@mail.tsinghua.edu.cn), Tianyu Pang (Tsinghua University, pty17@mails.tsinghua.edu.cn), Xiao Yang (Tsinghua University, yangxiao19@mails.tsinghua.edu.cn), Eric Wong (MIT, wongeric@mit.edu), Zico Kolter (CMU, zkolter@cs.cmu.edu), Yuan He (Alibaba, heyuan.hy@alibaba-inc.com ). 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/. 12 (2014): 90-94. We invite novel contributions following the AAAI-22 formatting guidelines, camera-ready style. Papers will be peer-reviewed and selected for spotlight and/or poster presentation. 2022. The workshop also welcomes participants of SUPERB and Zero Speech challenge to submit their results. search, ranking, recommendation, and personalization. However, the performance and efficiency of these techniques are big challenges for performing real-time applications. Qingzhe Li, Jessica Lin, Liang Zhao and Huzefa Rangwala. Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest. job seekers, employers, recruiters and job agents. 639-648, Nov 2015. In light of these issues, and the ever-increasing pervasiveness of AI in the real world, we seek to provide a focused venue for academic and industry researchers and practitioners to discuss research challenges and solutions associated with building AI systems under data scarcity and/or bias. The VTU workshops accepts both short paper (4 pages) and long paper (8 pages). The research contributions may discuss technical challenges of reading and interpreting business documents and present research results. The Thirty-Sixth AAAI Conference on Artificial IntelligenceFebruary 28 and March 1, 2022Vancouver Convention CentreVancouver, BC, Canada AAAI is pleased to present the AAAI-22 Workshop Program. 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. Yujie Fan, Yanfang (Fanny) Ye, Qian Peng, Jianfei Zhang, Yiming Zhang, Xusheng Xiao, Chuan Shi, Qi Xiong, Fudong Shao, and Liang Zhao. 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. This workshop wants to emphasize on the importance of integrative paradigms for solving the new wave of AI applications. KDD 2022. Each accepted paper presentation will be allocated between 15 and 20 minutes. Submissions will be peer-reviewed, single-blinded, and assessed based on their novelty, technical quality, significance, clarity, and relevance regarding the workshop topics. the 56th Design Automation Conference (DAC 2019), accepted, (acceptance rate: 20%), Las Vegas, US, 2019. The workshop welcomes the submission of work on, but not limited to, the following research directions. 2, no. How can we characterize or evaluate AI systems according to their potential risks and vulnerabilities? Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang. Rabat, Morocco . 2022. Connor Coley, Massachusetts Institute of TechnologyProf. San Francisco, USA . ITCI22 will be a one-day workshop. a tutorial on how to structure data mining papers by Prof. Xindong Wu (University of Louisiana at Lafayette). You also have the option to opt-out of these cookies. Finally, there is an increasing interest in AI in moving beyond traditional supervised learning approaches towards learning causal models, which can support the identification of targeted behavioral interventions. 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. Oral Paper (Top 5% among the accepted papers). Yuanqi Du, Xiaojie Guo, Yinkai Wang, Amarda Shehu, Liang Zhao. Additional advantages are possible, including decreased computational resources to solve a problem, reduced time for the network to make predictions, reduced requirements for training set size, and avoiding catastrophic forgetting. The cookies is used to store the user consent for the cookies in the category "Necessary". Submissions of technical papers can be up to 7 pages excluding references and appendices. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. "Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework",The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China, accepted. Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning. Attendance is open to all prior registration to the workshop/conference. The workshop attracted about 100 attendees. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), 2022. We encourage authors to contact the organizers to discuss possible overlap. "Automatic Targeted-Domain Spatiotemporal Event Detection in Twitter." Papers should be up to 4 pages in length (excluding references) formatted using the AAAI template. To facilitate KDD related research, we create this repository with: *ICDM has two tracks (regular paper track and short paper track), but the exact statistic is not released, e.g., the split between these two tracks. Self-supervised learning utilizes proxy supervised learning tasks, for example, distinguishing parts of the input signal from distractors, or generating masked input segments conditioned on the unmasked ones, to obtain training data from unlabeled corpora. Paper Submission Deadline: 23:59 on Thursday. 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. Oct. 24, 2021: The KDD2022 website is LIVE! Xiaojie Guo and Liang Zhao. The submissions need to be anonymized. Nowadays, machine learning solutions are widely deployed. Deep Learning models are at the core of research in Artificial Intelligence research today. ACM Transactions on Spatial Algorithms and Systems (TSAS), 5, 3, Article 19 (September 2019), 28 pages. Detailed information could be found on the website of the workshop. The program of the workshop will include invited talks, paper presentations and a panel discussion. We expect 50-65 people in the workshop. It is a forum to bring attention towards collecting, measuring, managing, mining, and understanding multimodal disinformation, misinformation, and malinformation data from social media. 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. KDD 2022 : Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. 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. 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. 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. and deep learning techniques (e.g. LOG 2022 LOG '22 . We invite thought-provoking submissions on a range of topics in fields including, but not limited to, the following areas: The full-day workshop will start with a keynote talk, followed by an invited talk and contributed paper presentations in the morning. Yuyang Gao, Lingfei Wu, Houman Homayoun, and Liang Zhao. Liang Zhao, Ting Hua, Chang-Tien Lu, and Ing-Ray Chen. Submission site:https://easychair.org/conferences/?conf=kdf22, Chair:Xiaomo Liu (J.P. Morgan Chase AI Research, xiaomo.liu@jpmchase.com), Zhiqiang Ma (J.P. Morgan Chase AI Research), Armineh Nourbakhsh (J.P. Morgan Chase AI Research), Sameena Shah (J.P. Morgan Chase AI Research), Gerard de Melo (Hasso Plattner Institute), Le Song (Mohamed bin Zayed University of Artificial Intelligence), Workshop URL:https://aaai-kdf.github.io/kdf2022/. All submissions must be original contributions and will be peer reviewed, single-blinded. For each accepted paper, at least one author must attend the workshop and present the paper. Knowledge Discovery and Data Mining. Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng and Liang Zhao. Although machine learning (ML) approaches have demonstrated impressive performance on various applications and made significant progress for AI, the potential vulnerabilities of ML models to malicious attacks (e.g., adversarial/poisoning attacks) have raised severe concerns in safety-critical applications. Submissions including full papers (6-8 pages) and short papers (2-4 pages) should be anonymized and follow the AAAI-22 Formatting Instructions (two-column format) at https://www.aaai.org/Publications/Templates/AuthorKit22.zip. Submission Site:https://cmt3.research.microsoft.com/SAS2022, Abdelrahman Mohamed (Facebook, abdo@fb.com), Hung-yi Lee (NTU, hungyilee@ntu.edu.tw), Shinji Watanabe (CMU, shinjiw@ieee.org), Tara Sainath (Google, tsainath@google.com), Karen Livescu (TTIC, klivescu@ttic.edu), Shang-Wen Li (Facebook, shangwel@fb.com), Ewan Dunbar (University of Toronto, ewan.dunbar@utoronto.ca) Emmanuel Dupoux (EHESS/Facebook, dpx@fb.com), Workshop URL:https://aaai-sas-2022.github.io/. Technology has transformed over the last few years, turning from futuristic ideas into todays reality. Checklist for Revising a SIGKDD Data Mining Paper: Yuanqi Du*, Shiyu Wang* (co-first author), Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao. Submitting a short or long paper to VDS will give authors a chance to present at VDS events at both ACM KDD 2022(hybrid) and IEEE VIS 2022( hybrid). All these changes require novel solutions, and the AI community is well-positioned to provide both theoretical- and application-based methods and frameworks. This workshop will follow a dual-track format. The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China, accepted. VDS@KDD will be hybrid and VDS@VIS will be hybrid (both virtual and in-person) in 2022. Identification of key challenges and opportunities for future research. VDS will bring together domain scientists and methods researchers (including data mining, visualization, usability and HCI, data management, statistics, machine learning, and software engineering) to discuss common interests, talk about practical issues, and identify open research problems in visualization in data science. This thread already has a best answer. As deep learning problems become increasingly complex, network sizes must increase and other architectural decisions become critical to success. 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. In general, AI techniques are still not widely adopted in the real world. 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." Oct 14, 2021: Abstract Deadline. Journal of Biomedical Semantics, (impact factor: 1.845), 2018, accepted. Eliminating the need to guess the right topology in advance of training is a prominent benefit of learning network architecture during training. In the Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), (acceptance rate: 17.9%), accepted, Macao, China, Aug 2019. 2022. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. It is expected that one of the authors of accepted contributions will register and attend the workshop to present the work in video in-person in the workshops Paper Sessions. Saliency-Augmented Memory Completion for Continual Learning. Interactive Machine Learning (IML) is concerned with the development of algorithms for enabling machines to cooperate with human agents. ASPLOS 2023 will be moving to three submission deadlines. IEEE Transactions on Information Forensics and Security (TIFS), (impact factor: 7.178), accepted. Algorithms and theories for learning AI models under bias and scarcity. Prof. Max Welling, University of Amsterdam and Microsoft ResearchProf. We will use double-blind reviewing. ACM, New York, NY, USA, 10 pages. Integration of non-differentiable optimization models in learning. 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. 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. Research track papers reporting the results of ongoing or new research, which have not been published before. Their results will be submitted in either a short paper or poster format. The workshop will be co-located with the KDD 2022 conference at Washington DC Convention Center,Washington D.C., USA onAugust 17th, 2022 at1PM5PM (Eastern Standard Time). Attendance is open to all, subject to any room occupancy constraints. Frontiers in Neurorobotics, (impact factor: 2.574), accepted. Kaiqun Fu, Taoran Ji, Liang Zhao, and Chang-Tien Lu. Knowledge discovery from various data sources has gained the attention of many practitioners in recent decades. 2020. Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao. Topics of interest include but are not limited to: Acronyms, i.e., short forms of long phrases, are common in scientific writing. iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow. [code] The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 20%=174/870), short paper, to appear, 2022. We are in a conversation with some publishers once they confirm, we will announce accordingly. 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. Ferdinando Fioretto (Syracuse University), Emma Frejinger (Universit de Montral), Elias B. Khalil (University of Toronto), Pashootan Vaezipoor (University of Toronto).
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