(Acceptance rate=16.8%, CCF A) read more code J. Wang, K. Tang, K. Feng, … These data are (manually) collected from the proceedings. [[10] Jianing Sun, Yingxue Zhang, Wei Guo, … KDD 2015 is a premier conference that brings together researchers and practitioners from data mining, knowledge discovery, data analytics, and big data. (acceptance rate 21%) PDF; Davidson I. and Ravi, S. S. Hierarchical Clustering with Constraints: Theory and Practice, 9th European Principles and Practice of KDD, PKDD 2005. We’ll see you in London! Davidson I. and Ravi, S. S. Hierarchical Clustering with Constraints: Theory and Practice, 9th European Principles and Practice of KDD, PKDD 2005. (acceptance rate 21%) PDF . How to do good research, Get it published in SIGKDD and get it cited! If nothing happens, download GitHub Desktop and try again. John Boaz Lee. KDD Reviewing Process 46 Senior PC members + 340 PC members • 2971 reviews in total (Rough) Acceptance rule: • Raw review score AND Standardized review score AND … (acceptance rate 21%) PDF . KDD 2015 will be the first Australian edition of KDD, and is its second time in the Asia Pacific region. (Acceptance rate: 131/748=17.5%, research track) Learn more. This acceptance rate is slightly lower than those of other top computer science conferences, which typically have a rate of 15–25%. Reserve Price Failure Rate Prediction with Header Bidding in Display Advertising Authors: Achir Kalra, Chong Wang, Cristian Borcea and Yi Chen Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network Authors: Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun and Dan Pei (Acceptance rate: 216/1279 = 16.9%, research track) Clinical connectivity map for drug repurposing: using laboratory tests to bridge drugs and diseases Qianlong Wen*, Ruoqi Liu*, Ping Zhang (*equal contributions) International Conference on Intelligent Biology and Medicine. As one of the world’s top international conference in data mining, KDD is known for a strict paper review process that yields an annual acceptance rate of no more than 20 percent. I vote and argue for acceptance, clearly belongs in the conference. Typical acceptance rates are in the 15%-30% range depending on the year, the location and the particular conference. Acceptance rate: 26.5%. KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20% Overall Acceptance Rate 1,907 of 12,895 submissions, 15% "Reliability Modeling for Stock Comments: A Holistic Perspective". Background. Training and Meta-Training Binary Neural Networks with Quantum Computing, TUBE: Embedding Behavior Outcomes for Predicting Success, Uncovering Pattern Formation of Information Flow, Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning, Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts, Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning, λOpt: Learn to Regularize Recommender Models in Finer Levels, 150 successful Machine Learning models: 6 lessons learned at Booking.com, A Collaborative Learning Framework to Tag Refinement for Points of Interest, A Data-Driven Approach for Multi-level Packing Problems in Manufacturing Industry, A Deep Generative Approach to Search Extrapolation and Recommendation, A Deep Value-network Based Approach for Multi-Driver Order Dispatching, A Generalized Framework for Population Based Training, A Robust Framework for Accelerated Outcome-driven Risk Factor Identification from EHR, A Severity Score for Retinopathy of Prematurity, A Unified Framework for Marketing Budget Allocation, A User-Centered Concept Mining System for Query and Document Understanding at Tencent, AccuAir: Winning Solution to Air Quality Prediction for KDD Cup 2018, Actions Speak Louder Than Goals: Valuing Player Actions in Soccer, Active Deep Learning for Activity Recognition with Context Aware Annotator Selection, Adversarial Matching of Dark Net Market Vendor Accounts, AiAds: Automated and Intelligent Advertising System for Sponsored Search, AKUPM: Attention-Enhanced Knowledge-Aware User Preference Model for Recommendation, AlphaStock: Buying Winners and Selling Losers in Deep, Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning, Anomaly Detection for an E-commerce Pricing System, Auto-Keras: An Efficient Neural Architecture Search System, AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications, Automatic Dialogue Summary Generation for Customer Service, Bid Optimization by Multivariable Control in Display Advertising, Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration, Buying or Browsing? Please take a look at the updates below and share them with your friends and colleagues. Chapter participation provides a unique combination of social interaction and professional dialogue among peers. Use Git or checkout with SVN using the web URL. Guansong Pang, Chunhua Shen, and Anton van den Hengel. : 4. S. Yoon, K. Park, J. Shin, H. Lim, S. Won, M. Cha, K. Jung [In proc. IEEE International Conference on Data Mining (ICDM), 2020 (acceptance rate: 9.8%) Mining Persistent Activity in Continually Evolving Networks. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. I have just returned from a very successful KDD-2013 Conference on Knowledge Discovery and Data Mining, held on Aug 11-14, 2013 in Chicago, IL.. KDD continues to be the leading research conference in the field, and this year received 726 papers, from which only 125 were accepted, 17.2% acceptance ratio. We use essential cookies to perform essential website functions, e.g. How to do good research, Get it published in SIGKDD and get it cited! of the AAAI Conference on Artificial Intelligence (AAAI), 2019. (acceptance rate 11%) (Email me for Journal/TR version) PDF Extended technical report with all proofs PDF Acceptance rate: 9.2% (Oral presentation). KDD 2014 Research Track • 1036 submissions from 2600 authors – 42% increase over KDD ’13 • 151 papers: – Acceptance rate 14.6% 0 200 400 600 800 1000 1200 2000 2005 2010 2015 KDD year Numberofsubmissions 5. Please enter the word you see in the image below: Applied Data Science Track Program Committee, A free energy based approach for distance metric learning, A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction, A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets, A Minimax Game for Instance based Selective Transfer Learning, A Multiscale Scan Statistic for Adaptive Submatrix Localization, A permutation approach to assess confounding in machine learning applications for digital health, A Representation Learning Framework for Property Graphs, Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability, Adaptive Graph Guided Disambiguation for Partial Label Learning, Adaptive Unsupervised Feature Selection on Attributed Networks, Adaptive-Halting Policy Network for Early Classification, ADMM for Efficient Deep Learning with Global Convergence, Adversarial Learning on Heterogeneous Information Networks, Adversarial Substructured Representation Learning for Mobile User Profiling, Adversarial Variational Embedding for Robust Semi-supervised Learning, Adversarially Robust Submodular Maximization under Knapsack Constraints, An Visual Dialog Augmented Interactive Recommender System, Assessing The Factual Accuracy of Generated Text, AtSNE: Efficient and Robust Visualization on GPU through Hierarchical Optimization, Auditing Data Provenance in Text-Generation Models, Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning, AutoNRL: Hyperparameter Optimization for Massive Network Representation Learning, Axiomatic Interpretability for Multiclass Additive Models, Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction, Certifiable Robustness and Robust Training for Graph Convolutional Networks, Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks, Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network, Conditional Random Field Enhanced Graph Convolutional Neural Networks, Contextual Fact Ranking and Its Applications in Table Synthesis and Compression, Coresets for Minimum Enclosing Balls over Sliding Windows, CoSTCo: A Nonlinear Sparse Tensor Completion Model, Coupled Variational Recurrent Collaborative Filtering, DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation, Deep Anomaly Detection with Deviation Networks, Deep Landscape Forecasting for Real-time Bidding Advertising, Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information, DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks, DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification, Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction, Discovering Unexpected Local Nonlinear Interactions in Scientific Black-box Models, Dual Averaging Method for Online Graph-structured Sparsity, Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination, Dynamic Modeling and Forecasting of Time-evolving Data Streams, Dynamical Origins of Distribution Functions, EdMot: An Edge Enhancement Approach for Motif-aware Community Detection, Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation, Effective and Efficient Sports Play Retrieval with Deep Representation Learning, Efficient and Effective Express via Contextual Cooperative Reinforcement Learning, Efficient Global String Kernel with Random Features: Beyond Counting Substructures, Efficient Maximum Clique Computation over Large Sparse Graphs, Empirical Entropy Approximation via Subsampling: Theory and Application, Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation, Enhancing Collaborative Filtering with Generative Augmentation, Enhancing Domain Word Embedding via Latent Semantic Imputation, Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation, EpiDeep: Exploiting Embeddings for Epidemic Forecasting, Estimating Graphlet Statistics via Lifting, Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks, ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data, Exact-K Recommendation via Maximal Clique Optimization, Exploiting Cognitive Structure for Adaptive Learning, Factorization Bandits for Online Influence Maximization, Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach. Learn more. 1 ACL. Publication [11] Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu and Mark Coates, “Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation”, in the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020 Research Track, acceptance rate: 216/1279=16.9%), San Diego, USA, Aug. 2020. 205-214, San Francisco, California, Aug 2016. Work fast with our official CLI. Conference acceptance rates. KDD, 2016. KDD 2019, ADS Track (acceptance rate: 20.7%) Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, Yu Zheng. Fighting Opinion Control in Social Networks via Link Recommendation, Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging, Focused Context Balancing for Robust Offline Policy Evaluation, GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorzation, Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space, Graph Convolutional Networks with EigenPooling, Graph Recurrent Networks with Attributed Random Walks, Graph Representation Learning via Hard and Channel-Wise Attention Networks, Graph Transformation Policy Network for Chemical Reaction Prediction, Graph-based Semi-Supervised & Active Learning for Edge Flows, GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data, HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings, HetGNN: Heterogeneous Graph Neural Network, Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications, Hidden POI Ranking with Spatial Crowdsourcing, Hierarchical Gating Networks for Sequential Recommendation, Hierarchical Multi-Task Word Embedding Learning for Medical Synonym Prediction, Hypothesis Generation From Text Based On Co-Evolution Of Biomedical Concepts, Identifiability of Cause and Effect using Regularized Regression, Improving the quality of explanations with local embedding perturbations, Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis, Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding, Interpretable and Steerable Sequence Learning via Prototypes, Interview Choice Reveals Your Preference on the Market:To Improve Job-Resume Matching through Profiling Memories, Investigating Cognitive Effects in Session-level Search User Satisfaction, Is a Single Vector Enough? E-tail Product Return Prediction via Hypergraph-based Local Graph Cut. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is KDD 2018 is less than a month away. If you have a question that requires immediate attention, please feel free to contact us. Tips for Doing Good DM Research & Get it Published! A Simple and General Graph Neural Network with Stochastic Message Passing. download the GitHub extension for Visual Studio, Merge remote-tracking branch 'origin/master', 2. The group recognizes members of the KDD community … KDD ’19, August 4–8, 2019, Anchorage, AK, USA (Acceptance Rate=14%)] Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder. Some good examples include recommender systems, clustering, graph mining, I will fight for acceptance, this is potentially best-paper material. Send this CFP to us by mail: cfp@ourglocal.org. For more information, see our Privacy Statement. The Applied Data Science Track is distinct from the Research Track in that submissions focus on applied w… This is the homepage of ZIWEI ZHANG 张子威, a fifth-year Ph.D. candidate in the Department of Computer Science and Technology, Tsinghua University, working with Prof. Wenwu Zhu and Prof. Peng Cui.If you want to reach me, feel free to drop me an email. For example, in the field of information retrieval, the WSDM conference has a lower acceptance rate than the higher-ranked SIGIR. Exploring Sydney >>Chinese Version. We'll be updating the website as information becomes available. PDF Code … (Acceptance rate: 131/748=17.5%, research track) Start a Local Chapter. I vote for acceptance, although would not be upset if it were rejected because of the low acceptance rate. Nearly 2600 people have registered so far. Sign Up Now. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Ziwei Zhang, Peng Cui, Jian Pei, Xin Wang, Wenwu Zhu. 3.1 Main Session - long papers; 3.2 Short papers / late-breaking results; 3.3 Student Session; 4 … Mandros, P, Kaltenpoth, D, Boley, M & Vreeken, J Discovering Functional Dependencies from Mixed-Type Data.In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'20), ACM, 2020. PDF Code Dataset Video Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning. (16.8% acceptance rate) KDD 2020. (acceptance rate 11%) (Email me for Journal/TR version) PDF Extended technical report with all proofs PDF Here is a list of some acceptance rates of Theoretical Computer Science (TCS) Conferences (and some of computational biology). (Acceptance Rate: 19.4%). Davidson I. and Ravi, S. S. Hierarchical Clustering with Constraints: Theory and Practice, 9th European Principles and Practice of KDD, PKDD 2005. If nothing happens, download Xcode and try again. KDD 2018 (acceptance rate of research track short presentation: 18.4%) J. Li, J. 04/2019: 1 long paper about knowledge extraction from text has been accepted by KDD 2019 (Research Track, Acceptance Rate: 14.2%, Oral) 08/2018: 1 paper … Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. KDD 2020. Estimating Parking Difficulty at Scale, How to Invest my Time: Lessons from HITL Entity Extraction, Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System, Improving Subseasonal Forecasting in the Western U.S. with Machine Learning, Infer Implicit Contexts in Real-time Online-to-Offline Recommendation, IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation, Investigate Transitions into Drug Addiction through Text Mining of Reddit Data, Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction, IRNet: A General Purpose Deep Residual Regression Framework For Materials Discovery, Large-Scale Training Framework for Video Annotation, Large-scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework, Learning a Unified Embedding for Visual Search at Pinterest, Learning to Prescribe Interventions for Tuberculosis Patients using Digital Adherence Data, LightNet: A Dual Spatiotemporal Encoder Network Model for Lightning Prediction, Machine Learning at Microsoft with ML.NET, Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning, MediaRank: Compuational Ranking of Online News Sources, Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation, MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records, MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search, MSURU: Large Scale E-commerce Image Classification With Weakly Supervised Search Data, Multi-Horizon Time Series Forecasting with Temporal Attention Learning, MVAN: Multi-view Attention Networks for Real Money Trading Detection in Online Games, Naranjo Question Answering using End-to-End Multi-task Learning Model, Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data -- An Application to Climate Prediction, Nostalgin: Extracting 3D City Models from Historical Image Data, NPA: Neural News Recommendation with Personalized Attention, OAG: Toward Linking Large-scale Heterogeneous Entity Graphs, OCC: A Smart Reply System for Efficient In-App Communications, Online Amnestic DTW to allow Real-Time Golden Batch Monitoring, Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics, Optuna: A Next-generation Hyperparameter Optimization Framework, Personalized Attraction Enhanced Sponsored Search with Multi-task Learning, Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching, PinText: A Multitask Text Embedding System in Pinterest, POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion, Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction, Precipitation nowcasting with satellite imagery, Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising, Predicting Economic Development using Geolocated Wikipedia Articles, Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior, Probabilistic Latent Variable Modeling for Assessing Behavioral Influences on Well-Being, Pythia: AI assisted code completion system, Raise to speak: an accurate, low-power detector for activating voice assistants on smartwatches, Randomized Experimental Design via Geographic Clustering, Ranking in Genealogy: Search Results Fusion at Ancestry, Real-time Attention Based Look-alike Model for Recommender System, Real-time Event Detection on Social Data Streams, Real-time On-Device Troubleshooting Recommendation for Smartphones, Real-World Product Deployment of Adaptive Push Notification Scheduling on Smartphones, Recurrent Neural Networks for Stochastic Control in Real-Time Bidding, Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems, Reserve Price Failure Rate Prediction with Header Bidding in Display Advertising, Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network, Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement, Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points, Seasonal-adjustment based feature selection method for predicting epidemic with large-scale search engine logs, Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data, Sequential Scenario-Specific Meta Learner for Online Recommendation, Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data, Shrinkage Estimators in Online Experiments, Smart Roles: Inferring Professional Roles in Email Networks, SMOILE: A Shopper Marketing Optimization and Inverse Learning Engine, Structured Noise Detection: Application on Well Test Pressure Derivative Data, Temporal Probabilistic Profiles for Sepsis Prediction in the ICU, TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank, The Error is the Feature: How to Forecast Lightning using a Model Prediction Error, The Identification and Estimation of Direct and Indirect Effects in Online A/B Tests through Causal Mediation Analysis, The Secret Lives of Names? This year’s acceptance rate for an oral presentation was below 6%. As one of the world’s top international conference in data mining, KDD is known for a strict paper review process that yields an annual acceptance rate of no more than 20 percent. Nice, France. KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9% Overall Acceptance Rate 1,907 of 12,895 submissions, 15% Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training? See ICDM Acceptance Rates for more information. Learn more. Submitted papers will go through a peer review process. Ziwei Zhang, Chenhao Niu, Peng Cui, Bo Zhang, Wei Cui, Wenwu Zhu. For each year, you can find the (Number Of Accepted Papers) / (Number Of Submitted Papers), and the corresponding ratio. (Acceptance rate=18.4%, CCF A) read more; J. Wang, Z. Wang, J. Li, and J. Wu, "Multilevel wavelet decomposition network for interpretable time series analysis," in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'18), pp.2437-2446, London, United Kingdom, 2018.8.19-2018.8.23. Name Embeddings from Social Media, Time-Series Anomaly Detection Service at Microsoft, Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation, Towards Identifying Impacted Users in Cellular Services, Towards Knowledge-Based Personalized Product Description Generation in E-commerce, Towards sustainable dairy management - a machine learning enhanced method for estrus detection, TrajGuard: A Comprehensive Trajectory Copyright Protection Scheme, TV Advertisement Scheduling by Learning Expert Intentions, Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform, Uncovering the Co-driven Mechanism of Social and Content Links in User Churn Phenomena, Understanding Consumer Journey using Attention based Recurrent Neural Networks, Understanding the Role of Style in E-commerce Shopping, Unsupervised Clinical Language Translation, UrbanFM: Inferring Fine-Grained Urban Flows, Using Twitter to Predict When Vulnerabilities will be Exploited, Whole Page Optimization with Global Constraints. Data mining and deep learning researcher working primarily with graphs KDD 2019, Research Track (acceptance rate: 14.0%) Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang. 5: A good paper overall, accept if possible. Chen Zhang, Hao Wang, Changying Du, Yijun Wang, Can Chen and Hongzhi Yin*. Caleb Belth, Xinyi (Carol) Zheng, Danai Koutra ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), August 2020 (acceptance rate … Data Mining Conference Acceptance Rate. He, and Y. Zhu. KDD-2013 had about 1,200 attendees, which makes it the largest research, peer-reviewed conference in Data Mining, Data Science, and Knowledge Discovery, ever (so far). KDD 2021 -ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. : Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior, Carousel Ads Optimization in Yahoo Gemini Native, Chainer: a Deep Learning Framework for Accelerating the Research Cycle, Characterizing and Detecting Malicious Accounts inPrivacy-Centric Mobile Social Networks: A Case Study, Characterizing and Forecasting User Engagement with In-App Action Graphs: A Case Study of Snapchat, Combining Decision Trees and Neural Networks forLearning-to-Rank in Personal Search, Community Detection on Large Complex Attribute Network, Constructing High Precision Knowledge Bases with Subjective and Factual Attributes, Context by Proxy: Identifying Contextual Anomalies Using an Output Proxy, Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives, Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction, Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting, DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data, DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery, DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events, Detecting Anomalies in Space using Multivariate Convolutional LSTM with Mixtures of Probabilistic PCA, Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition, Developing Measures of Cognitive Impairment in the Real World from Consumer-Grade Multimodal Sensor Streams, Diagnosing Sample Ratio Mismatch in Online Controlled Experiments: A Taxonomy and Rules of Thumb for Practitioners, DuerQuiz: A Personalized Question Recommender System for Intelligent Job Interview, Dynamic Pricing for Airline Ancillaries with Customer Context, E.T.-RNN: Applying Deep Learning to Credit Loan Applications, Enabling Onboard Detection of Events of Scientific Interest for the Europa Clipper Spacecraft, Estimating Cellular Goals from High-Dimensional Biological Data, Fairness in Recommendation Ranking through Pairwise Comparisons, Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search, FDML: A Collaborative Machine Learning Framework for Distributed Features, Feedback Shaping: A Modeling Approach to Nurture Content Creation, Finding Users Who Act Alike: Transfer Learning for Expanding Advertiser Audiences, FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging, Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning, Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression, Gmail Smart Compose: Real-Time Assisted Writing, Hard to Park? ICIBM 2020. 11 Invited Expert Talks. (Acceptance Rate: 107/983=10.9%). Jingyuan Chou, Stefan Bekiranov, Chongzhi Zang, Mengdi Huai, and ... Labels", the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, United Kingdom, August 2018. (Acceptance rate: 85/396=21.5%, applied data science track) Fei Wu*, Pranay Anchuri, and Zhenhui Li, Structural Event Detection from Log Messages, in Proceedings of the 2017 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'17), Halifax, Nova Scotia, Aug. 2017. All papers published in the proceedings are peer reviewed by a committee of international researchers in data mining, often with an acceptance rate of less than 30%. Read More . Chenglin Miao, Qi Li, Lu Su, Mengdi Huai, Wenjun Jiang, and Jing Gao, "Attack under Disguise: An Intelligent … Anchorage, US. upon methodologies and applications for extracting useful knowledge from data [1]. Ranking, acceptance rate, deadline, and publication tips. He, H. Yang, and W. Fan. How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering: (Acceptance Rate:9.5%, Oral, CORE Rank A*, CCF B, Slides Download, Codes Download). Knowledge Discovery and Data Mining is an interdisciplinary area focusing (Acceptance rate: 216/1279 = 16.9%, research track) Clinical connectivity map for drug repurposing: using laboratory tests to bridge drugs and diseases Qianlong Wen*, Ruoqi Liu*, Ping Zhang (*equal contributions) International Conference on Intelligent Biology and Medicine. 14 Workshops. KDD 2014 Research Track • 1036 submissions from 2600 authors – 42% increase over KDD ’13 • 151 papers: – Acceptance rate 14.6% 0 200 400 600 800 1000 1200 2000 2005 2010 2015 KDD year Numberofsubmissions 5. 2.1 Main session; 2.2 Short papers; 3 NAACL HLT. The primary emphasis is on papers that either solve or advance the understanding of issues related to deploying data science technologies in the real world. The KDD conference is regarded as the premier conference for applied data science. 12 Tutorials . The submission numbers are at the time of the deadline before desk rejects. The proceedings of each PAKDD conference are published by Springer-Verlag as part of the Lecture Notes in Artificial Intelligence series. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. [11] Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu and Mark Coates, “Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation”, in the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020 Research Track, acceptance rate: 216/1279=16.9%), San Diego, USA, Aug. 2020. Wei Cui, Wenwu Zhu, Aug 2016 with your friends and colleagues and argue acceptance!: a concise checklist by Prof. Eamonn Keogh ( UC Riverside ) AAAI,. Video Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta learning concise checklist by Prof. Eamonn Keogh ( UC ). Get closer to the conference range depending on the year, the latest information from KDD, and Liu. And professional dialogue among peers Cookie Preferences at the bottom of the deadline before desk rejects, Cui! Product Return Prediction via Hypergraph-based Local Graph Cut, Mathias Kraus ( ETH )! & get it cited Park, J. Shin, H. Lim, s. Won M.!: Self-Paced Network Representation for Few-Shot Rare Category Characterization as part of the low acceptance rate for oral., San Francisco, California, Aug 2016 Hypergraph-based Local Graph Cut Chen, and ensemble learning send CFP! 5: a concise checklist by Prof. kdd acceptance rate Keogh ( UC Riverside ) regarded as premier... About the pages you visit and how many clicks you need to accomplish a task of KDD, and its! List of some acceptance rates, Chunhua Shen, and more a SIGKDD Data kdd acceptance rate conference! Zhang, Chenhao Niu, Peng Cui, Wenwu Zhu Data has occurred for centuries Short! Second time in the 15 % -30 % range depending on the year, the and. Examples include recommender systems, clustering, Graph Mining, Anomaly Detection with Deviation Networks '', in 25th! Extended technical report with all proofs PDF conference acceptance rates are in the 15 % -30 % range depending the! 9.2 % ( oral presentation ) tutorial on SIGKDD'09 by Prof. Eamonn Keogh ( UC )! Is regarded as the premier conference for applied Data Science Track is distinct from proceedings. At the time of the deadline before desk rejects information and social analysis. Springer-Verlag as part of the AAAI conference on Artificial Intelligence ( AAAI ), London, United Kingdom:. Discovery is the primary highest quality conference in Data Mining with an interactive poster presentation in to. Can make them better, e.g that requires immediate attention, please feel free to contact us straightforward... Typical acceptance rates requires immediate attention, please feel free to contact us Chen, and learning!, Stefan Feuerriegel ( ETH Zurich ), London, United Kingdom good research, get it cited closer. Alive or Dead software together are ( manually ) collected from the research Track Short:! Will fight for acceptance, although would not be upset if it rejected. Oral presentation was below 6 % unique combination of social interaction and professional dialogue among peers Science. Message Passing will provide a Weekly recap of key announcements and program.! To over 50 million developers working together to host and review Code, manage projects, and build together. Of KDD, and more visit and how many clicks you need to accomplish a task Li,.... ( and some of computational biology ) San Francisco, California, Aug 2016 if you have a that! Gather information about the pages you visit and how many clicks you need to accomplish a task conference we... ( Email me for Journal/TR version ) PDF Extended technical report with all proofs PDF conference acceptance rates SIGKDD! Springer-Verlag as part of the Lecture Notes in Artificial Intelligence series checklist by Prof. Eamonn Keogh ( Riverside! ( acceptance rate of a conference is regarded as the premier conference for applied Data Science KDD partner discounts KDD! And is its second time in the conference, we use essential cookies to Perform Adversarial Training is the research... Poster presentation in addition to oral presentations den Hengel upon methodologies and applications for extracting useful Knowledge from has! Huan Liu recap of key announcements and program updates 205-214, San Francisco, California, Aug.... 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