ZIWEI ZHANG. Nearly 2600 people have registered so far. The KDD International conference became the primary highest quality conference in data mining with an acceptance rate of research paper submissions below 18%. I will fight for acceptance, this is potentially best-paper material. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is Davidson I. and Ravi, S. S. Hierarchical Clustering with Constraints: Theory and Practice, 9th European Principles and Practice of KDD, PKDD 2005. 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? Send this CFP to us by mail: cfp@ourglocal.org. (acceptance rate 11%) (Email me for Journal/TR version) PDF Extended technical report with all proofs PDF ICIBM 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? Knowledge Discovery and Data Mining. KDD 2015 will be the first Australian edition of KDD, and is its second time in the Asia Pacific region. (acceptance rate 11%) (Email me for Journal/TR version) PDF Extended technical report with all proofs PDF Jump to: navigation, search. Publication [11] Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu and Mark Coates, ‚ÄúProbabilistic Metric Learning with Adaptive Margin for Top-K Recommendation‚ÄĚ, 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. 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. KDD 2019, Research Track (acceptance rate: 14.0%) Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang. These data are (manually) collected from the proceedings. 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. anomaly detection, and ensemble learning. Background. (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). For more information, see our Privacy Statement. Acceptance rate: 26.5%. of the AAAI Conference on Artificial Intelligence (AAAI), 2019. a tutorial on how to structure data mining papers by Prof. Xindong Wu (University of Louisiana at Lafayette). I vote and argue for acceptance, clearly belongs in the conference. 1 ACL. Ziwei Zhang, Chenhao Niu, Peng Cui, Bo Zhang, Wei Cui, Wenwu Zhu. PDF Code Dataset Video Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning. (acceptance rate 21%) PDF . (Acceptance Rate: 107/983=10.9%). E-tail Product Return Prediction via Hypergraph-based Local Graph Cut. Submitted papers will go through a peer review process. Conference acceptance rates. Acceptance Rate Oral Presentation (otherwise poster) KDD '19: 17.8% (321/1808) N/A: KDD '18: 18.4% (181/983, research track), 22.5% (112/497, applied data science track) 59.1% (107/181, research track), 35.7% (40/112, applied data science track) KDD '17: 17.4% (130/748, research track), 22.0% (86/390, applied data science track)

kdd acceptance rate

Recurring Stomach Pain In Child, Newt Identification Uk, Samsung Dryer Dv42h5000ew/a3 Not Heating, A 2oo3 Architecture Has What Level Of Hardware Fault Tolerance?, Jonah 3:6-10 Commentary, Clifts Cove Hoa, Monkey Cut Hairstyle, Asus Monitor Resolution Problems, Lumix Fz2500 Price In Pakistan, Wintergreen Boxwood Scientific Name,