ACM provides the computing field's premier Digital Library and serves its members and the computing profession with leading-edge publications, conferences, and career resources. Videos for related products. Jim Ormond Thorsten Joachims, professor of Computer Science and Information Science at Cornell University, is recognized for his research contributions in machine learning, including influential work studying human biases in information retrieval, support vector machines (SVM) and structured output prediction. Bootleg is open-source at http://hazyresearch.stanford.edu/bootleg/. ormond@hq.acm.org, Advancing Computing as a Science & Profession, Software and Data Artifacts in the ACM Digital Library, Virtual Conferences: A Guide to Best Practices, Chapter-in-a-Box: Everything You Need to Run and Grow Your ACM Chapter, Munmun De Choudhury Receives ACM-W Rising Star Award, Agent-Human Collaboration and Learning for Improving Human Satisfaction, Reproducing 150 Research Papers and Testing Them in the Real World: Challenges and Solutions, ACM Europe TPC Comments on UK National Data Strategy, USTPC Urges Narrower Definition of Computer Fraud and Abuse Act, ACM Celebrates US #BlackHistoryMonth 2021, ACM Diversity and Inclusion Council Co-chairs, KDD 2020 Showcases Brightest Minds in Data Science and AI, ACM Special Interest Group for Knowledge Discovery from Data (SIGKDD), ACM, the Association for Computing Machinery. [Learn More about ACM's Commitment to Diversity & Inclusion...]. Topics of interest include, but are not limited to: All submitted papers and posters will be single-blind and will be peer reviewed by an international program Time series arising for studies of physical, biological, economic and sociological systems are an important data class. Best Paper Runner Up: “Malicious Attacks against Deep Reinforcement Learning Interpretations” “A Look at State-Space Multi-Taper Time-Frequency Analysis” Christian Wardlaw, Independent Expert | Jun 29, 2020. Mapping textual mentions to entities in a knowledge graph is a key step in assistants, called Named Entity Disambiguation (NED). A key challenge in NED is generalizing to rarely seen (tail) entities. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. conversational systems by businesses and consumers alike for everyday Although graphs have been ubiquitous in AI and knowledge discovery … However, how to scale the graph similarity search to databases that have hundreds of thousands or even millions of graphs remains a challenging problem. The ACM SIGKDD Dissertation Award recognizes outstanding work done by graduate students in the areas of data science, machine learning and data mining. 2020 Proceeding. A partial listing of highlights follows. Solution to the Debiasing Track of KDD CUP 2020 . Abstract: Jan. 10, 2021 >> Paper submission is open for both research track and applied data science track! Can one build a knowledge graph (KG) for all products in the world? Virtual Conference. Alessandro Vespignani, Northeastern University The deadline for paper submission is June 15, 2020 (11: 59 P.M. AoE). KDD-2020-Tutorial: Automated Recommender System. General Chairs: Rajesh Gupta. representing original research, preliminary research results, proposals for new work, position and opinion papers. The collaborative research teams from Harvard University and University of Massachusetts Boston present a novel deep learning model to tackle functionally interactive features by stacking a Conditional Restricted Boltzmann Machine and a Deep Neural Network (CRBM-DNN). They demonstrate that their neural sequence model improves over DeepTTE, the state-of-the-art baseline, both in performance (-30% MAPE) and training stability. Emery Brown Massachusetts Institute of Technology. “KDD is a ‘must attend’ conference, where the theory and practice in data science, machine learning and artificial intelligence come together in industry-defining innovations,” explained KDD 2020 General Co-chair Rajesh K. Gupta, University of California, San Diego. Then, we discuss some complexities of modeling trading conversations, as well as our work on entity recognition and slot filling, intent recognition and conversation disentanglement. As the recommendation tasks are getting more diverse and the recommending models are growing more complicated, it is increasingly challenging to develop a proper recommendation system that can adapt well to a new recommendation task. We discuss the importance of conversation modeling in assisting traders and in tracking compliance. technologies and personnel. Best Student Paper: “TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations” The University of Virginia team investigates the vulnerability of DRL interpretation methods in the malicious environment. Check back as we get closer to the conference for more detailed program information. 2020 ACM SIGKDD Service Award Advances in conversational technologies including speech recognition, “GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases” Georgia Tech, USA. Keynote Talks Walid Krichene, Steffen Rendle, Google Submissions must describe work that is not previously published, not accepted for publication elsewhere, and not currently under review elsewhere. Collaborative Topic Modeling for Recommending Scientific Articles. The data science revolution is finally enabling the development of large-scale data-driven models that provide scenarios, forecasts and risk analysis for infectious disease threats. Bootleg improves tail generalization through a new inverse regularization scheme to favor more generalizable signals automatically. Wir weisen darauf hin, dass wir keine Kenntnis vom Inhalt der übermittelten Daten sowie deren Nutzung durch [NETWORK NAME] erhalten.. Wenn Sie nicht wünschen, dass [NETWORK NAME] Daten über den "Empfehlen-Button" erhebt, klicken Sie auf "nicht einverstanden". In machine learning and data mining these interactions are usually formulated as dependency and correlation among system variables. http://www.acm.org/publications/proceedings-template, https://easychair.org/conferences/?conf=kddconverse20, End-to-end learning approaches for conversational systems, Information extraction and knowledge graphs, Dialogue state tracking and policy learning, Natural language processing in conversational systems, Natural language understanding in conversational systems, Design of conversational systems and agents, User-interaction and user-experience design, Cold-start issues in conversational systems, Question answering in conversational systems, Personalization in conversational systems, Recommendations-explanations in conversational systems, Dialogue management for conversational systems, Interaction methods in conversational systems, Challenges associated with general domain conversational systems, Challenges associated with domain specific conversational systems, Sarcasm and humor detection and generation in conversational systems. Dr. Emery Brown, Massachusetts Institute of Technology, Harvard Medical School, Massachusetts General Hospital Inspired by the recent success of deep learning-based supervised hashing, called semantic hashing, in image and document retrieval, this paper proposes a novel graph neural network (GNN) based pruning approach, GHashing, for graph similarity search. Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text. No one has more exclusive content. Chong et al. In this talk, I will overview several research directions we are pursuing in engagement with the lines of business, ranging from data and knowledge, learning from experience, reasoning and planning, multi agent systems, and secure and private AI. Workshop Program. “Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs” Click to play video . Anyone, from any background, should feel encouraged to participate and contribute to ACM. Note that we will be using Pacific Time (3 hours behind Eastern Time) in our program schedule. … Or provides a more comprehensive learning center. Our new deadline for submission will be June 5th, 2020. The invited talks and speakers include: Panels Unfortunately, there is no definitive data-driven study on analyzing factors associated with SUD among homeless youth. There is also a growing concern that technical skill alone is insufficient for long-term data science career success, partially due to the fact that many data science tasks are being automated. This workshop aims to bring researchers and practitioners together to discuss issues, learnings, challenges and Panelists include Danielle Gewurz, Deloitte Consulting; Shubha Nabar, Faras AI; Monica Rogati, Data Science and AI Advisor; Horst Samulowitz, IBM Watson Research Center. Klicken Sie auf "einverstanden", verlassen Sie unserer Seite und werden auf eine externe Seite weitergeleitet. KDD 2020 will feature four keynote talks, 18 applied data science invited talks, 217 accepted research papers grouped into 43 sessions for oral presentations, workshops and tutorials. Research Track Papers Applied Data Science Track Papers. In this paper, the authors describe AutoKnow, their automatic (self-driving) system that addresses these challenges. The panel “The Near Future of Automated Data Science” will explore the demand for creative, technically-skilled data scientists in the United States. tutorials. Submissions to KDD Converse should be made at https://easychair.org/conferences/?conf=kddconverse20 They also demonstrate significant generalization gains over simpler models, evaluated on longitudinal data to cope with a constantly evolving world. Submissions Due - June 15, 2020 (11: 59 P.M. AoE) Notification - June 30, 2020 Camera Ready Version of Papers Due - July 10, 2020 KDD Converse half day Workshop - August 24, 2020 We invite quality research contributions, position and opinion papers addressing relevant challenges in the domain of conversational systems. of conversational systems. “AI for Intelligent Financial Services: Examples and Discussion” We do not tolerate harassment of workshop participants in any form. Submissions Due - June 15, 2020 (11: 59 P.M. AoE) Notification - June 30, 2020 Camera Ready Version of Papers Due - July 10, 2020 KDD Converse half day Workshop - August 24, 2020 Workshop Website-KDD Converse 2020 “Computational Epidemiology at the Time of COVID-19” The SIGKDD Test of Time award recognizes outstanding KDD papers, at least ten years old, which have had a lasting impact on the data mining research community and continue to be cited as the foundation for new branches of research. ACM, the Association for Computing Machinery, is the world's largest educational and scientific computing society, uniting educators, researchers and professionals to inspire dialogue, share resources and address the field's challenges. One of the long-standing questions in search systems is the role of diversity in results. However, this intuition is at odds with common machine learning approaches to ranking which directly optimize the relevance of each individual item without a holistic view of the result set. Big and small stories surrounding a police station in Berlin-Kreuzberg. Contribute to xuetf/KDD_CUP_2020_Debiasing_Rush development by creating an account on GitHub. Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang and Zhong Su received the inaugural Test of Time Award for Applied Science in recognition of their study of mining academic social networks published in the 2008 peer-reviewed paper, "ArnetMiner: Extraction and Mining of Academic Social Networks." Animaros a venir que os lo pasareis genial!!! All submissions must be formatted according to the latest ACM SIG proceedings template available at http://www.acm.org/publications/proceedings-template (LaTeX users use sample-sigconf.tex as a template). Traditionally NED uses hand-tuned patterns to capture rare, but reliable, signals. The ACM Learning Center offers ACM members access to lifelong learning tools and resources. Existing methods are capable of managing databases with thousands or tens of thousands of graphs. Dr. Amanda Stent works on text analytics and discourse processing as NLP architect at Bloomberg LP. New York, NY, August 21, 2020 – The Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD) will hold its flagship annual conference, KDD 2020, virtually, August 23-27. Jingbo Shang, assistant professor of Computer Science at University of California at San Diego, earned runner-up for his thesis, "Constructing and Mining Heterogeneous Information Networks from Massive Text." Sign up for the free insideBIGDATA newsletter. Motivated by applications in community detection and dense subgraph discovery, this paper considers new clustering objectives in hypergraphs and bipartite graphs. No one presents more forward-looking events. In this lecture, Brown will discuss his recent work on the development of a state-space multi-taper (SS-MT) framework for the analysis of non-stationary time series. Danai Koutra, Morris Wellman assistant professor of Computer Science and Engineering at University of Michigan, and Jiliang Tang, assistant professor of Computer Science and Engineering at Michigan State University, both received the first annual ACM SIGKDD Rising Star Award.
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