This workshop has no archival proceedings. It is anticipated that this will be an in-person workshop, subject to changing travel restrictions and health measures. The workshop on Robust Artificial Intelligence System Assurance (RAISA) will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems. 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. 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. 5 (2014): 1447-1459. 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. ), Graduate (master's, specialized graduate diploma (DESS), etc. 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. Papers more suited for a poster, rather than a presentation, would be invited for a poster session. 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. Apr 11-14, 2022. IEEE Computer (impact factor: 3.564), vo. Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph. Invited speakers, panels, poster sessions, and presentations. 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. KDD - ACM Conferences 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. The final schedule will be available in November. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities. Their results will be submitted in either a short paper or poster format. 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). 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. Causality has received significant interest in ML in recent years in part due to its utility for generalization and robustness. The academic session will focus on most recent research developments on GNNs in various application domains. Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng and Liang Zhao. 1923-1935, 1 Oct. 2020, doi: 10.1109/TKDE.2019.2912187. Integration of declarative and procedural domain knowledge in learning. 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. AI Conference Deadlines - Hyunwoo Kim Notable examples include the information bottleneck (IB) approach on the explanation of the generalization behavior of DNNs and the information maximization principle in visual representation learning. Authors are strongly encouraged to make data and code publicly available whenever possible. Deadline: Fri Jun 09 2023 04:59:00 GMT-0700 Yahoo! Novel approaches and works in progress are encouraged. What is the status of existing approaches in ensuring AI and Machine Learning (ML) safety, and what are the gaps? Supplemental Workshop site:https://rl4ed.org/aaai2022/index.html. Note: This is the inaugural event of a conference dedicated to Graph Machine Learning. We expect 50-65 people in the workshop. Estimate of the audience size: 400-500 attendees (based on the number of attendees in previous DLG workshops in KDD19, AAAI20, KDD20 and AAAI21). Bioinformatics (Impact Factor: 6.937), accepted, 2022. This cookie is set by GDPR Cookie Consent plugin. Deadline in . The PAKDD is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. Welcome to the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022), which will be held in Chengdu, China on May 16-19, 2022. 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. For further information, please have a look at the call for contributions. For authors who do not wish their papers to be posted online, please mention this in the workshop submission. Yuyang Gao, Tong Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Hong, and Liang Zhao. We hope this will help bring the communities of data mining and visualization more closely connected. 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. Deep Graph Spectral Evolution Networks for Graph Topological Evolution. The accepted papers are allowed to be submitted to other conference venues. ACM, 2014. In recent years, we have seen examples of general approaches that learn to play these games via self-play reinforcement learning (RL), as first demonstrated in Backgammon. "Spatiotemporal Event Forecasting in Social Media." The design and implementation of these AI techniques to meet financial business operations require a joint effort between academia researchers and industry practitioners. Toward Model Parallelism for Deep Neural Network based on Gradient-free ADMM Framework. Deep Classifier Cascades for Open World Recognition. As for the Kraken, they made one trade a month ago to acquire a seventh defenceman, Jaycob Megna and did nothing else (from 'Kraken remain quiet as NHL trade deadline passes,' The Seattle . 10, pp. All the submissions must follow the AAAI-22 formatting guidelines, camera-ready style. 4 pages), and position (max. Novel methods to learn from scarce/sparse, or heterogenous, or multimodal data. Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019. We allow papers that are concurrently submitted to or currently under review at other conferences or venues. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, Chang-Tien Lu. The workshop attracted about 100 attendees. Papers can be submitted here as an extended abstract (4 pages limit excluding references) or a short paper (6 pages limit excluding references). Submission site:https://cmt3.research.microsoft.com/DSTC102022, Koichiro Yoshino,Address: 2-2-2, Seika, Sohraku, Kyoto, 6190288, JapanAffiliation: RIKENPhone: +81-774-95-1360Email: koichiro.yoshino@riken.jp, Yun-Nung (Vivian) ChenAddress: No. Nonetheless, human-centric problems (such as activity recognition, pose estimation, affective computing, BCI, health analytics, and others) rely on information modalities with specific spatiotemporal properties. SDU will be a one-day workshop. 41-50, New Orleans, US, Dec 2017. We welcome submissions of long (max. Integration of non-differentiable optimization models in learning. Welcome to PAKDD2022. 10 (2014): e110206. 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. Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. A primary reason for this is the inherent long-tailed nature of our world, and the need for algorithms to be trained with large amounts of data that includes as many rare events as possible. Out of these, around 20~30 papers are accepted. 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. CoRL 2023 97 days 17h 29m 15s November 06-09, 2023. Ferdinando Fioretto (Syracuse University), Emma Frejinger (Universit de Montral), Elias B. Khalil (University of Toronto), Pashootan Vaezipoor (University of Toronto). Jinliang Ding, Liang Zhao, Changxin Liu, and Tianyou Chai. information bottleneck principle). Track 2 focuses on the state of the art advances in the computational jobs marketplace. 639-648, Nov 2015. Positive applications of adversarial ML, i.e., adversarial for good. The 2023 ACM SIGMOD/PODS Conference: Seattle, Washington, USA - Welcome Combating fake news is one of the burning societal crises. Its capabilities have expanded from processing structured data (e.g. CS Conference Deadlines - Yanlin Published March 4, 2023 4:51 a.m. PST. We also invite papers that have been published at other venues to spark discussions and foster new collaborations. 1953-1970, Oct. 2017. Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification over Encrypted Wi-Fi Traffic. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), New Orleans, US, Feb 2018, pp. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), vol. 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. Long papers (up to 6 pages + references) and extended abstracts (2 pages + references) are welcome, including resubmissions of already accepted papers, work-in-progress, and position papers. Can AI achieve the same goal without much low-level supervision? 2022. Kaiqun Fu, Taoran Ji, Liang Zhao, and Chang-Tien Lu. Zheng Zhang and Liang Zhao. The goal of this workshop is to connect researchers in self-supervision inside and outside the speech and audio fields to discuss cutting-edge technology, inspire ideas and collaborations, and drive the research frontier. 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. 4, Roosevelt Rd., Taipei, TaiwanAffiliation: National Taiwan UniversityPhone: +1-412-465-0130Email: yvchen@csie.ntu.edu.tw, Paul CrookAddress: 1 Hacker Way, Menlo Park, CA, USAAffiliation: FacebookPhone: +1-650-885-0094Email: pacrook@fb.com, DSTC 10 home:https://dstc10.dstc.community/homeDSTC 10 CFPs:https://dstc10.dstc.community/calls_1/call-for-workshop-papers. 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 positive/negative social impacts and ethical issues related to adversarial ML. Submissions will undergo double blind review. SIGKDD Explorations, Vol. 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. Submissions that do not meet the formatting requirements will be rejected without review. STGEN: Deep Continuous-time Spatiotemporal Graph Generation. This workshop will follow a dual-track format. GeoInformatica (impact factor: 2.392), 24, 443475 (2020). Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. These complex demands have brought profound implications and an explosion of interest for research into the topic of this workshop, namely building practical AI with efficient and robust deep learning models. Please refer and submit through theLearning Network Architecture During Trainingworkshop website, which has more detailed information. "Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling." Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Chang-Tien Lu, Sai Dinakarrao, Houman Homayoun, Liang Zhao. . We are soliciting submissions of short papers in PDF format and formatted according to the Standard ACM Conference Proceedings Template. 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). Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features. Are you sure you want to create this branch? IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 24.31), accepted. The papers may consist of up to seven pages of technical content plus up to two additional pages for references. 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. 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. With the rapid development of advanced techniques on the intersection between information theory and machine learning, such as neural network-based or matrix-based mutual information estimator, tighter generalization bounds by information theory, deep generative models and causal representation learning, information theoretic methods can provide new perspectives and methods to deep learning on the central issues of generalization, robustness, explainability, and offer new solutions to different deep learning related AI applications.This workshop aims to bring together both academic researchers and industrial practitioners to share visions on the intersection between information theory and deep learning, and their practical usages in different AI applications. Fine tuning a neural network is very time consuming and far from optimal. 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. Liang Zhao, Ting Hua, Chang-Tien Lu, and Ing-Ray Chen. Yuyang Gao, Tong Sun, Guangji Bai, Siyi Gu, Sungsoo Hong, and Liang Zhao. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted. This website uses cookies to improve your experience while you navigate through the website. Call for Papers Document Intelligence Workshop @ KDD 2022 "Bridging the gap between spatial and spectral domains: A survey on graph neural networks." 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 last few years have seen the rapid development of mathematical methods for modeling structured data coming from biology, chemistry, network science, natural language processing, and computer vision applications. Functional Connectivity Prediction with Deep Learning for Graph Transformation. Amir A. Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng. Shi, Y., Deng, M., Yang, X., Liu, Q., Zhao, L., & Lu, C. T. "A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter." May 8, 2022: Student Travel Awards announcement is, Apr. Yanfang Ye, Yiming Zhang, Yujie Fan, Chuan Shi and Liang Zhao. Junxiang Wang, Hongyi Li, Liang Zhao. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), accepted. Well also host a competition on adversarial ML along with this workshop. Papers should be up to 4 pages in length (excluding references) formatted using the AAAI template. Please refer tohttps://rl4ed.org/aaai2022/index.htmlfor additional information. Balaraman Ravindran (Indian Institute of Technology Madras, India ravi@cse.iitm.ac.in), Balaraman Ravindran (Indian Institute of Technology Madras, India Primary contact (ravi@cse.iitm.ac.in), Kristian Kersting (TU Darmstadt, Germany, kersting@cs.tu-darmstadt.de), Sriraam Natarajan (Univ of Texas Dallas, USA, Sriraam.Natarajan@utdallas.edu), Ginestra Bianconi (Queen Mary University of London, UK, ginestra.bianconi@gmail.com), Philip S. Chodrow (University of California, Los Angeles, USA, phil@math.ucla.edu) Tarun Kumar (Indian Institute of Technology Madras, India, tkumar@cse.iitm.ac.in), Deepak Maurya (Purdue University, India, maurya@cse.iitm.ac.in), Shreya Goyal (Indian Institute of Technology Madras, India, Goyal.3@iitj.ac.in), Workshop URL:https://sites.google.com/view/gclr2022/. Encore track papers that have been recently published, or accepted for publication in a conference or journal. We will specifically invite participants of the DSTC10 tasks, track organizers, and authors of accepted papers in the general technical track. [materials]. 2085-2094, Aug 2016. Check the CFP for details Deadline: ICDM 2020 . We invite submissions of full papers, as well as works-in-progress, position papers, and papers describing open problems and challenges. Accepted contributions will be made publicly available as non-archival reports, allowing future submissions to archival conferences or journals. Xiaosheng Li, Jessica Lin, and Liang Zhao. Hence, AI methods are required to understand and protect the cyber domain. Deep Generative Model for Periodic Graphs. We welcome attendance from individuals who do not have something theyd like to submit but who are interested in RL4ED research. Jan 13, 2022: Notification. Robust Regression via Online Feature Selection under Adversarial Data Corruption. The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2022 (ACM SIGSPATIAL 2022) (Acceptance Rate: 23.8%), full paper track, to appear, 2022. 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. These research trends inform the need to explore the intersection of AI with behavioral science and causal inference, and how they can come together for applications in the social and health sciences. Videos have become an omnipresent source of knowledge: courses, presentations, conferences, documentaries, live streams, meeting recordings, vlogs. Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao. Please keep your paper format according to AAAI Formatting Instructions (two-column format). Keynotes and invited talks: Several keynotes and invited talks by leading researchers in the area will be presented. "Multi-resolution Spatial Event Forecasting in Social Media." Liyan Xu, Xuchao Zhang, Zong Bo, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho Choi. This policy also applies to papers that overlap substantially in technical content with papers previously published, accepted, or under review. 25-50 attendees including invited speakers and accepted papers. All submissions must be anonymous and conform to AAAI standard for double-blind review. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu. 1-39, November 2016. The cookie is used to store the user consent for the cookies in the category "Analytics". Like other systems, ML systems must meet quality requirements. Proceedings of the IEEE (impact factor: 9.237), vol. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is considered to be more practical and more related with real-world applications. Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Datasets and Benchmarks Track, accepted. 2022. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is Examples of the datasets which may be considered are the DBTex Radiology Mammogram dataset and the Johns Hopkins COVID-19 case reports. The main research questions and topics of interest include, but are not limited to: This will be a one day workshop, including four invited speakers, one panel session, a number of oral presentations of the accepted long papers and two poster sessions for all accepted papers including short and long. Industry-wide reports highlight large-scale remediation efforts to fix the failures and performance issues. Attendance is expected to be 150-200 participants (estimated), including organizers and speakers. 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. "EMBERS at 4 years:Experiences operating an Open Source Indicators Forecasting System." Participants will be given access to publicly available datasets and will be asked to use tools from AI and ML to generate insight from the data. Efficient Learning with Exponentially-Many Conjunctive Precursors for Interpretable Spatial Event Forecasting. Yuanqi Du, Xiaojie Guo, Hengning Cao, Yanfang Ye, Liang Zhao. This workshop starts with acknowledging the fundamental challenges of robustness and adaptiveness in financial services modeling and explores systematic solutions to solve these underlying problems to prevent future failures. December, 12-16, 2022. Andrew White, University of RochesterDr. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. 9, no. Topics of interest include, but are not limited to: Paper submissions will be in two formats: full paper (8 pages) and position paper (4 pages): The submission website ishttps://easychair.org/conferences/?conf=trase2022. [materials][data]. KDD 2022 | Washington DC, U.S. Submissions are due by 12 November 2021. "Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning." All deadlines are at 11:59 PM anytime in the world. Xiaojie Guo, Lingfei Wu, Liang Zhao. Algorithms and theories for trustworthy AI models. algorithms applied to the above topics: deep learning, reinforcement learning, multi-armed bandits, causal inference, mathematical programming, and stochastic optimization. All papers must be submitted in PDF format using the AAAI-22 author kit. Abstracts of the following flavors will be sought: (1) research ideas, (2) case studies (or deployed projects), (3) review papers, (4) best practice papers, and (5) lessons learned. Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang. The excellent papers will be recommended for publications in SCI or EI journals. We are excited to continue promoting innovation in self-supervision for the speech/audio processing fields and inspiring the fields to contribute to the general machine learning community. We encourage all the teams who participated in the challenge to join the workshop. 2020. Additionally, adversaries continue to develop new attacks. The 21st Web Conference (WWW 2022), (Acceptance Rate: 17.7%), accepted. Continuous refinement of AI models using active/online learning. The thematic sessions will be structured into short pitches and a common panel slot to discuss both individual paper contributions and shared topic issues. Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao. Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. ML4OR is a one-day workshop consisting of a mix of events: multiple invited talks by recognized speakers from both OR and ML covering central theoretical, algorithmic, and practical challenges at this intersection; a number of technical sessions where researchers briefly present their accepted papers; a virtual poster session for accepted papers and abstracts; a panel discussion with speakers from academia and industry focusing on the state of the field and promising avenues for future research; an educational session on best practices for incorporating ML in advanced OR courses including open software and data, learning outcomes, etc. Autonomous vehicles can share their detected information (e.g., traffic signs, collision events, etc.) PLOS ONE (impact factor: 3.534), vo. Deadline: FSE 2023. The post-lunch session will feature one long talk, two short talks, and a poster session. RecSys 2022 - Important Dates - RecSys The trustworthy issues of clinical AI methods were not discussed. SDU will also host a session for presenting the short research papers and the system reports of the shared tasks. The biomedical space has seen a flurry of activity recently, and cyber criminals have amplified their efforts with health-related phishing attacks, spreading misinformation, and intruding into health infrastructure. : Papers must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template. Deep Generation of Heterogeneous Networks. Methods for learning network architecture during training, including Incrementally building neural networks during training, new performance benchmarks for the above. Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, and Liang Zhao. Oilers Outperform Division Rivals at 2023 Trade Deadline However, workshop organizers may set up any archived publication mechanism that best suits their workshop. These cookies track visitors across websites and collect information to provide customized ads. Characterization of fundamental limits of causal quantities using information theory. The bottleneck to discovery is now our ability to analyze and make sense of heterogeneous, noisy, streaming, and often massive datasets. Full papers: Submissions must represent original material that has not appeared elsewhere for publication and that is not under review for another refereed publication. Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting.Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), (acceptance rate: 16.2%), Hawaii, USA, Feb 2019, accepted.
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