Winners were selected … 05/2020 One paper is accepted by KDD’20! In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’20), August 23–27, 2020, Virtual Event, CA, USA. Paper #1: Joint Policy-Value Learning for Recommendation. Publications. KDD-2014 features plenary presentations, paper presentations, poster sessions, workshops, tutorials, exhibits, and the KDD Cup competition. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’20), ∗Equal contribution to this work. We invite submission of papers describing innovative research on all aspects of knowledge discovery and data mining, ranging from theoretical foundations to novel models and algorithms for data mining problems in science, business, medicine, and engineering. The KDD conferences feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD … DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection. The main challenge 04/2020 Accepted by “Rhino-Bird Elite Program”. Applied Data Science Track Paper KDD '20, August 23 27, 2020, Virtual Event, USA 3319. level (i.e. Malicious Attacks against Deep Reinforcement Learning Interpretations 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. (Presentation time: Tuesday August 25 4:00-6:00PM, Poster Time: Tuesday August 25 7:00-8:00PM), Best Student Paper Award The annual KDD conference is the premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data. Visionary papers on new and emerging topics are also welcome, as are application - oriented papers … KDD 2020 Accepted Papers. Barman-Adhikari, and Amulya Yadav. (Presentation time: Tuesday August 25, 10:00am-12:00pm). KDD 2020, the premier interdisciplinary conference in data science (which took place virtually Aug. 23-27, 2020), announced the recipients of the SIGKDD Best Paper Awards, recognizing papers presented at the annual SIGKDD conference that advanced the fundamental understanding of the field of knowledge discovery in data and data mining. 1989 to be exact. We are happy to announce that this year we are partnering with Bloomberg to emphasize our theme of Data Science for Social Good. 17 minute read This post is part of a literature review on misinformation, especially in relation to technology and social media. Important Dates. 01/2020 One paper is accepted by WWW’20! We take a data-driven approach to model the existence of unre-ported cases in terms of probability of a case being reported. Get the latest New Jersey Local News, Sports News & US breaking News. KDD 2020 Opens Call for Papers. Service Award. Mengdi Huai: University of Virginia; Jianhui Sun: University of Virginia; Renqin Cai: University of Virginia; Liuyi Yao: University of New York at Buffalo; Aidong Zhang: University of Virginia Dissertation Award. Please enter the word you see in the image below: Applied Data Science Track Program Committee, A Block Decomposition Algorithm for Sparse Optimization, A causal look at statistical definitions of discrimination, A Data Driven Graph Generative Model for Temporal Interaction Networks, A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks, A Geometric Approach to Predicting Bounds of Downstream Model Performance, A Non-Iterative Quantile Change Detection Method in Mixture Model with Heavy-Tailed Components, A Novel Deep Learning Model by Stacking Conditional Restricted Boltzmann Machine and Deep Neural Network, Adaptive Graph Encoder for Attributed Graph Embedding, Adversarial Infidelity Learning for Model Interpretation, AdvMind: Inferring Adversary Intent of Black-Box Attacks, Algorithmic Aspects of Temporal Betweenness, Algorithmic Decision Making with Conditional Fairness, Aligning Superhuman AI with Human Behavior: Chess as a Model System, ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization, AM-GCN: Adaptive Multi-channel Graph Convolutional Networks, An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph, An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks, ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction, Attackability Characterization of Adversarial Evasion Attack on Discrete Data, Attention and Memory-Augmented Networks for Dual-View Sequential Learning, Attentional Multi-graph Convolutional Network for Regional Economy Prediction with Open Migration Data, AutoGrow: Automatic Layer Growing in Deep Convolutional Networks, AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space, AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks, AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction, Average Sensitivity of Spectral Clustering, BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals, Block Model Guided Unsupervised Feature Selection, BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision, CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data, Catalysis Clustering With GAN By Incorporating Domain Knowledge, Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations, CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams, Combinatorial Black-Box Optimization with Expert Advice, Competitive Analysis for Points of Interest, COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching, Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems, Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks, Context-to-Session Matching: Utilizing Whole Session for Response Selection in Information-Seeking Dialogue Systems, CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring, Correlation Networks for Extreme Multi-label Text Classification, Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions, Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks, CurvaNet: Geometric Deep Learning based on Multi-scale Directional Curvature for 3D Shape Analysis, Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning, Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction, Deep Learning of High-Order Interactions for Protein Interface Prediction, Deep State-Space Generative Model For Correlated Time-to-Event Predictions, DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering, DeepSinger: Singing Voice Synthesis with Data Mined From the Web, DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation, Discovering Approximate Functional Dependencies using Smoothed Mutual Information, Discovering Functional Dependencies from Mixed-Type Data, Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity, Disentangled Self-Supervision in Sequential Recommenders, Dual Channel Hypergraph Collaborative Filtering, Dynamic Knowledge Graph based Multi-Event Forecasting, Edge-consensus Learning: Deep Learning on P2P Networks with Nonhomogeneous Data, Efficient Algorithm for the b-Matching Graph, Enterprise Cooperation and Competition Analysis with Sign-Oriented Preference Network, Estimating Properties of Social Networks via Random Walk considering Private Nodes, Estimating the Percolation Centrality of Large Networks through Pseudo-dimension Theory, Evaluating Conversational Recommender Systems via User Simulation, Evaluating Fairness using Permutation Tests, Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns, Feature-Induced Manifold Disambiguation for Multi-View Partial Multi-label Learning, FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems, Finding Effective Geo-social Group for Impromptu Activities with Diverse Demands, FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents, GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training, Generic Outlier Detection in Multi-Armed Bandit, Geography-Aware Sequential Location Recommendation, GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases, GPT-GNN: Generative Pre-Training of Graph Neural Networks, GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model, Grammatically Recognizing Images with Tree Convolution, Graph Attention Networks over Edge Content-Based Channels, Graph Structure Learning for Robust Graph Neural Networks, Grounding Visual Concepts for Multimedia Event Detection and Multimedia Event Captioning in Zero-shot Setting, Handling Information Loss of Graph Neural Networks for Session-based Recommendation, Heidegger: Interpretable Temporal Causal Discovery, HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification, HGMF: Heterogeneous Graph-based Fusion for Multimodal Data with Incompleteness, Hierarchical Attention Propagation for Healthcare Representation Learning, Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding, High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder, Higher-order Clustering in Complex Heterogeneous Networks, HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records, HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units, HOPS: Probabilistic Subtree Mining for Small and Large Graphs, How to count triangles, without seeing the whole graph, Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder, Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion, Imputing Various Incomplete Attributes via Distance Likelihood Maximization, In and Out: Optimizing Overall Interaction in Probabilistic Graphs under Clustering Constraints, Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams, InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity, InFoRM: Individual Fairness on Graph Mining, INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare, Interactive Path Reasoning on Graph for Conversational Recommendation, Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense, Interpretable Deep Graph Generation with Node-edge Co-disentanglement, Isolation Distributional Kernel: A new tool for kernel based anomaly detection, Joint Policy-Value Learning for Recommendation, Kernel Assisted Learning for Personalized Dose Finding, Laplacian Change Point Detection for Dynamic Graphs, LayoutLM: Pre-training of Text and Layout for Document Image Understanding, Learning Effective Road Network Representation with Hierarchical Graph Neural Networks, Learning Opinion Dynamics From Social Traces, Learning Stable Graphs from Heterogeneous Confounded Environments, Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach, Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling, Leveraging Model Inherent Variable Importance for Stable Online Feature Selection, List-wise Fairness Criterion for Point Processes, Local Community Detection in Multiple Networks, Local Motif Clustering on Time-Evolving Graphs, LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values, Malicious Attacks against Deep Reinforcement Learning Interpretations, MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation, Matrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness, MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining, Measuring Model Complexity of Neural Networks with Curve Activation Functions, Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation, Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks, Minimizing Localized Ratio Cut Objectives in Hypergraphs, Mining large quasi-cliques with quality guarantees from vertex neighborhoods, Mining Persistent Activity in Continually Evolving Networks, MinSearch: An Efficient Algorithm for Similarity Search under Edit Distance, Missing Value Imputation for Mixed Data via Gaussian Copula, MoFlow: An Invertible Flow Model for Generating Molecular Graphs, Multi-class Data Description for Out-of-distribution Detection, Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction, Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data, MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals, Multimodal Learning with Incomplete Modalities by Knowledge Distillation, NetTrans: Neural Cross-Network Transformation, NodeAug: Semi-Supervised Node Classification with Data Augmentation, Non-Linear Mining of Social Activities in Tensor Streams, Off-policy Bandits with Deficient Support, On Sampled Metrics for Item Recommendation, On Sampling Top-K Recommendation Evaluation, Parallel DNN Inference Framework Leveraging a Compact RISC-V ISA-based Multi-core System, Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs, Partial Multi-Label Learning via Probabilistic Graph Matching Mechanism, Personalized PageRank to a Target Node, Revisited, PolicyGNN: Aggregation Optimization for Graph Neural Networks, Predicting Temporal Sets with Deep Neural Networks, Prediction and Profiling of Audience Competition for Online Television Series, Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction, Prioritized Restreaming Algorithms for Balanced Graph Partitioning, Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation, RayS: A Ray Searching Method for Hard-label Adversarial Attack, Re-identification Attack to Privacy-Preserving Data Analysis with Noisy Sample-Mean, REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs, Reciptor: An Effective Pretrained Model for Recipe Representation Learning, RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift, Recurrent Halting Chain for Early Multi-label Classification, Recurrent Networks for Guided Multi-Attention Classification, Redundancy-Free Computation for Graph Neural Networks, Representing Temporal Attributes for Schema Matching, Residual Correlation in Graph Neural Network Regression, Rethinking Pruning for Accelerating Deep Inference At the Edge, Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks, Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC, Robust Spammer Detection by Nash Reinforcement Learning, Scaling choice models of relational social data, SCE: Scalable Newtork Embedding from Sparsest Cut, SEAL: Learning Heuristics for Community Detection with Generative Adversarial Networks, Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation, Semi-Supervised Multi-Label Learning from Crowds via Deep Sequential Generative Model, Sliding Sketches: A Framework using Time Zones for Data Stream Processing in Sliding Windows, Spectrum-Guided Adversarial Disparity Learning, SSumM: Sparse Summarization of Massive Graphs, ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification, Stable Learning via Differentiated Variable Decorrelation, Statistically Significant Pattern Mining with Ordinal Utility, STEAM: Self-Supervised Taxonomy Expansion via Path-Based Multi-View Co-Training, Structural Patterns and Generative Models of Real-world Hypergraphs, TAdaNet: Task-Adaptive Network for Graph-Enriched Meta-Learning, Targeted Data-driven Regularization for Out-of-Distribution Generalization, The NodeHopper: Enabling low latency ranking with constraints via a fast dual solver, The Spectral Zoo of Networks: Embedding and Visualizing Networks with Spectral Moments, Tight Sensitivity Bounds For Smaller Coresets, TinyGNN: Learning Efficient Graph Neural Networks, TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations, Towards Automated Neural Interaction Discovering for Click-Through Rate Prediction, Towards Fair Truth Discovery from Biased Crowdsourced Answers, Towards physics-informed deep learning for turbulent flow prediction, TranSlider: Transfer Ensemble Learning from Exploitation to Exploration, Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes, Truth Discovery against Strategic Sybil Attack in Crowdsourcing, Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping, Understanding Negative Sampling in Graph Representation Learning, Unsupervised Differentiable Multi-aspect Network Embedding, Unsupervised Paraphrasing via Deep Reinforcement Learning, Vamsa: Automated Provenance Tracking in Data Science Scripts, Voronoi Graph Traversal in High Dimensions with Applications to Topological Data Analysis and Piecewise Linear Interpolation, Vulnerability vs.
Soldier Kidd - Thug Cry Lyrics,
Turgut Alp Real Pic,
Apartment Buildings For Sale In Las Vegas,
4age 16v Itb Stock Ecu,
Fifth Third Ceo Email,
Physical Science Fifth Edition Answers,
Btw21 Crime Report 2021,
Dofus Masqueraider Build,
Yasmeen Ghauri Height,
Hunger Games Hand,