Highlights: Government day with NSF, NIH, DARPA, NIST, and IARPA Local industries in the DC Metro Area, including the Amazon's second headquarter New initiatives at KDD 2022: undergraduate research and poster session Early career research day with postdoctoral scholars and assistant professors in a mentoring workshop and panel Workshops and hands-on tutorials on emerging topics Attendance is expected to be 150-200 participants (estimated), including organizers and speakers. This calls for novel methods and new methodologies and tools to address quality and reliability challenges of ML systems. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao. Xuchao Zhang, Liang Zhao, Zhiqian Chen, and Chang-Tien Lu. Attendance is open to all. [Best Poster Runner-Up Award]. Rupinder Khandpur, Taoran Ji, Yue Ning, Liang Zhao, Chang-Tien Lu, Erik Smith, Christopher Adams and Naren Ramakrishnan. Although textual data is prevalent in a large amount of finance-related business problems, we also encourage submissions of studies or applications pertinent to finance using other types of unstructured data such as financial transactions, sensors, mobile devices, satellites, social media, etc. Papers will be peer-reviewed by the Program Committee (2-3 reviewers per paper). Linguistic analysis of business documents. Novel algorithmic solutions to causal inference or discovery problems using information-theoretic tools or assumptions. Attendance is open to all. You also have the option to opt-out of these cookies. 1953-1970, Oct. 2017. Liang Zhao, Amir Alipour-Fanid, Martin Slawski and Kai Zeng. Papers can be submitted here as an extended abstract (4 pages limit excluding references) or a short paper (6 pages limit excluding references). Examples of the datasets which may be considered are the DBTex Radiology Mammogram dataset and the Johns Hopkins COVID-19 case reports. Liming Zhang, Dieter Pfoser, Liang Zhao. 2999-3006, New Orleans, US, Feb 2018. Important Dates. 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. upon methodologies and applications for extracting useful knowledge from data [1]. 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. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), New Orleans, US, Feb 2018, pp. The papers have to be submitted through EasyChair. Submission site:https://cmt3.research.microsoft.com/AAAI2022HCSSL/Submission/Index, Ali Etemad (Queens University, ali.etemad@queensu.ca), Ali Etemad (Queens University, ali.etemad@queensu.ca), Ahmad Beirami (Facebook AI, ahmad.beirami@gmail.com), Akane Sano (Rice University, akane.sano@rice.edu), Aaqib Saeed (Philips Research & University of Cambridge, aqibsaeed@protonmail.com), Alireza Sepas-Moghaddam (Socure, alireza.sepasm@socure.com), Mathilde Caron (Inria & Facebook AI, mathilde@fb.com), Pritam Sarkar (Queens University & Vector Institute, pritam.sarkar@queensu.ca), Huiyuan Yang (Rice University, hy48@rice.edu), Supplemental website:https://hcssl.github.io/AAAI-22/. Full papers are allocated 20m presentation and 10m discussion. : 2022. Thirty-First AAAI Conference on Artificial Intelligence, pp. The workshop follows a single-blind reviewing process. 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. Deep learning has achieved significant success for artificial intelligence (AI) in multiple fields. 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. Spatio-temporal Event Forecasting Using Incremental Multi-source Feature Learning. ACM Transactions on Spatial Algorithms and Systems (TSAS), 5, 3, Article 19 (September 2019), 28 pages. Nowadays, machine learning solutions are widely deployed. 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. To provide proper alerts and timely response, public health officials and researchers systematically gather news and other reports about suspected disease outbreaks, bioterrorism, and other events of potential international public health concern, from a wide range of formal and informal sources. Yuyang Gao, Giorgio Ascoli, Liang Zhao. 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. Attendance is open to any interested participants at AAAI-22. 1, Sec. August 14-18, 2022. It will start with a 60-minute mini-tutorial covering the basics of RL in games, and will include 2-4 invited talks by prominent contributors to the field, paper presentations, a poster session, and will close with a discussion panel. 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. Submissions will be peer-reviewed, single-blinded, and assessed based on their novelty, technical quality, significance, clarity, and relevance regarding the workshop topics. Interactive Machine Learning (IML) is concerned with the development of algorithms for enabling machines to cooperate with human agents. Geoinformatica, (impact factor: 2.392), Volume 20, Issue 4, pp 765-795, Oct 2016. December 2020, July 21: Clarified that the workshop this year will be held, June 20: Paper notification is now extended to, Paper reviews are underway! 27, 2022: Please check out Speical Days at, Apr. Accepted papers will be published in the workshop proceedings. with other vehicles via vehicular communication systems (e.g., dedicated short range communication (DSRC), vehicular ad hoc networks (VANETs), long term evolution (LTE), and 5G/6G mobile networks) for cooperation. 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. This workshop will encourage researchers from interdisciplinary domains working on multi-modality and/or fact-checking to come together and work on multimodal (images, memes, videos etc.) Virtual . How can we make AI-based systems more ethically aligned? After seventh highly successful events, the eighth Symposium on Visualization in Data Science (VDS) will be held at a new venue, ACM KDD 2022 as well as IEEE VIS 2022. To view them in conference website timezones, click on them. Additional information about formatting and style files is available here: : Full papers are limited to a total of 6 pages, including all content and references. Attendance is open to all; at least one author of each accepted submission must be physically/virtually present at the workshop. The first AAAI Workshop on AI for Design and Manufacturing, ADAM, aims to bring together researchers from core AI/ML, design, manufacturing, scientific computing, and geometric modeling. Scientific documents such as research papers, patents, books, or technical reports are one of the most valuable resources of human knowledge. 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. Submissions will be collected via the OpenReview platform; URL forthcoming on the Workshop website. In this workshop, we aim to address the trustworthy issues of clinical AI solutions. Saliency-Augmented Memory Completion for Continual Learning. The cookies is used to store the user consent for the cookies in the category "Necessary". We would especially like to highlight approaches that are qualitatively different from some popular but computationally intensive NAS methods. ; (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. The accepted papers are allowed to be submitted to other conference venues. 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. the 56th Design Automation Conference (DAC 2019), accepted, (acceptance rate: 20%), Las Vegas, US, 2019. Public health authorities and researchers collect data from many sources and analyze these data together to estimate the incidence and prevalence of different health conditions, as well as related risk factors. We accept two types of submissions full research papers no longer than 8 pages (including references) and short/poster papers with 2-4 pages. In decision-making domains as wide-ranging as medication adherence, vaccination uptakes, college enrollment, retirement savings, and energy consumption, behavioral interventions have been shown to encourage people towards making better choices. We hope to build upon that success. Table identification and extraction from business documents. Oilers Outperform Division Rivals at 2023 Trade Deadline Estimate of the audience size: 400-500 attendees (based on the number of attendees in previous DLG workshops in KDD19, AAAI20, KDD20 and AAAI21). Semantic understanding of business documents. RES: A Robust Framework for Guiding Visual Explanation. 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. robust and interpretable natural language processing for healthcare. It is a forum to bring attention towards collecting, measuring, managing, mining, and understanding multimodal disinformation, misinformation, and malinformation data from social media. Ferdinando Fioretto (Syracuse University), Emma Frejinger (Universit de Montral), Elias B. Khalil (University of Toronto), Pashootan Vaezipoor (University of Toronto). Please refer tohttps://rl4ed.org/aaai2022/index.htmlfor additional information. 10 (2014): e110206. Submission link:https://easychair.org/cfp/raisa-2022, William Streilein, MIT Lincoln Laboratory, 244 Wood St., Lexington, MA, 02420, (781) 981-7200, wws@ll.mit.edu, Olivia Brown (MIT Lincoln Laboratory, Olivia.Brown@ll.mit.edu), Rajmonda Caceres (MIT Lincoln Laboratory, Rajmonda.Caceres@ll.mit.edu), Tina Eliassi-Rad (Northeastern University, teliassirad@northeastern.edu), David Martinez (MIT Lincoln Laboratory, dmartinez@ll.mit.edu), Sanjeev Mohindra (MIT Lincoln Laboratory, smohindra@ll.mit.edu), Elham Tabassi (National Institute of Standards and Technology, elham.tabassi@nist.gov), Workshop URL:https://sites.google.com/view/raisa-2022/. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. 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. Accepted papers are likely to be archived. DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums. Second, psychological experiments in laboratories and in the field, in partnership with technology companies (e.g., using apps), to measure behavioral outcomes are being increasingly used for informing intervention design. Jinliang Ding, Liang Zhao, Changxin Liu, and Tianyou Chai. 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. 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. It leverages many emerging privacy-preserving technologies (SMC, Homomorphic Encryption, differential privacy, etc.) Submissions may consist of up to 4 pages plus one additional page solely for references. KDD 2022 Reveals Schedule of Data Mining and Knowledge Discovery Papers Note: Mandatory abstract deadline on May 16, 2022 Deadline: ISMIR 2022 ISMIR '22 ​ . However, the use of rich data sets also raises significant privacy concerns: They often reveal personal sensitive information that can be exploited, without the knowledge and/or consent of the involved individuals, for various purposes including monitoring, discrimination, and illegal activities. Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang. Data Mining and Knowledge Discovery (DMKD), (impact factor: 3.670), accepted. Self-supervised learning (SSL) has shown great promise in problems involving natural language and vision modalities. 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. NOTE: May 19: Notification. Causal inference is one of the main areas of focus in artificial intelligence (AI) and machine learning (ML) communities. We will use double-blind reviewing. This workshop seeks to explore new ideas on AI safety with particular focus on addressing the following questions: Contributions are sought in (but are not limited to) the following topics: To deliver a truly memorable event, we will follow a highly interactive format that will include invited talks and thematic sessions. Welcome to the home of the 2023 ACM SIGMOD/PODS Conference, to be held in the Seattle metropolitan area, Washington, USA, on June 18 - June 23, 2023. Whats more, various AI based models are trained on massive student behavioral and exercise data to have the ability to take note of a students strengths and weaknesses, identifying where they may be struggling. GeoInformatica (impact factor: 2.392), 24, 443475 (2020). The 11th International Conference on Learning Representations (ICLR 2023), accepted. Representation learning, distributed representations learning and encoding in natural language processing for financial documents; Synthetic or genuine financial datasets and benchmarking baseline models; Transfer learning application on financial data, knowledge distillation as a method for compression of pre-trained models or adaptation to financial datasets; Search and question answering systems designed for financial corpora; Named-entity disambiguation, recognition, relationship discovery, ontology learning and extraction in financial documents; Knowledge alignment and integration from heterogeneous data; Using multi-modal data in knowledge discovery for financial applications; Data acquisition, augmentation, feature engineering, and analysis for investment and risk management; Automatic data extraction from financial fillings and quality verification; Event discovery from alternative data and impact on organization equity price; AI systems for relationship extraction and risk assessment from legal documents; Accounting for Black-Swan events in knowledge discovery methods. Cesa Salaam (Howard University, USA), Hwanhee Lee (Seoul National University, South Korea), Jaemin Cho (University of North Carolina at Chapel Hill, USA), Jielin Qiu (Carnegie Mellon University, USA), Joseph Barrow (University of Maryland, US), Mengnan Du (Texas A&M University, USA), Minh Van Nguyen (University of Oregon, USA), Nicole Meister (Princeton University, USA), Sajad Sotudeh Gharebagh (Georgetown University, USA), Sampreeth Chebolu (University of Houston, USA), Sarthak Jain (Northeastern University, USA),Shufan Wang (University of Massachusetts Amherst, USA), Supplemental Workshop site:https://vtuworkshop.github.io/2022/, https://research.ibm.com/haifa/Workshops/AAAI-22-AI4DO/. Junxiang Wang, Junji Jiang, Liang Zhao. Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification over Encrypted Wi-Fi Traffic. Question answering on business documents. 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. Yuanqi Du*, Shiyu Wang* (co-first author), Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao. Submissions can be original research contributions, or abstracts of papers previously submitted to top-tier venues, but not currently under review in other venues and not yet published. The advances in web science and technology for data management, integration, mining, classification, filtering, and visualization has given rise to a variety of applications representing real-time data on epidemics. The trained models are intended to assign scores to novel utterances, assessing whether they are possible or likely utterances in the training language. Lastly, learning joint modalities is of interest to both Natural Language Processing (NLP) and Computer Vision (CV) forums. the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018) (acceptance rate: 20.6%), Stockholm, Sweden, Jul 2018, accepted. Positive applications of adversarial ML, i.e., adversarial for good. All accepted papers will be archived on the workshop website, but there will not be formal proceedings.
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