BibSLEIGH — Proceedings of the 25th International Conference on Knowledge Discovery and Data Mining

Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, George Karypis
Proceedings of the 25th International Conference on Knowledge Discovery and Data Mining
KDD, 2019.

KER

@proceedings{KDD-2019,
	doi           = "10.1145/3292500",
	editor        = "Ankur Teredesai and Vipin Kumar and Ying Li and Rómer Rosales and Evimaria Terzi and George Karypis",
	isbn          = "978-1-4503-6201-6",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 25th International Conference on Knowledge Discovery and Data Mining}",
	year          = 2019,
}

Contents (374 items)

KDD-2019-Rudin #how #modelling #question: Do Simpler Models Exist and How Can We Find Them? (CR), pp. 1–2.
KDD-2019-Lee #effectiveness: The Unreasonable Effectiveness, and Difficulty, of Data in Healthcare (PL), pp. 3–4.
KDD-2019-InabaFKZ #approach #distance #energy #learning #metric: A Free Energy Based Approach for Distance Metric Learning (SI, CTF, RVK, KZ), pp. 5–13.
KDD-2019-MengZXZX #network #predict: A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction (QM, HZ, KX, LZ, HX), pp. 14–24.
KDD-2019-WangQZZWLG #set #sketching #streaming: A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets (PW, YQ, YZ, QZ, CW, JCSL, XG), pp. 25–33.
KDD-2019-WangQWLGZHZCZ #game studies #learning: A Minimax Game for Instance based Selective Transfer Learning (BW, MQ, XW, YL, YG, XZ, JH0, BZ, DC, JZ), pp. 34–43.
KDD-2019-LiuA #adaptation #locality #multi #statistics: A Multiscale Scan Statistic for Adaptive Submatrix Localization (YL, EAC), pp. 44–53.
KDD-2019-NetoPPTBMO #approach #health #machine learning #permutation: A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health (ECN, AP, TMP, MT, BMB, LM, LO), pp. 54–64.
KDD-2019-HouCLCY #framework #graph #learning #representation: A Representation Learning Framework for Property Graphs (YH, HC, CL, JC, MCY), pp. 65–73.
KDD-2019-YangZZX0 #adaptation #capacity #incremental #learning #modelling #scalability: Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (YY, DWZ, DCZ, HX, YJ0), pp. 74–82.
KDD-2019-WangLZ #adaptation #ambiguity #graph #learning: Adaptive Graph Guided Disambiguation for Partial Label Learning (DBW, LL0, MLZ), pp. 83–91.
KDD-2019-LiGL0 #adaptation #feature model #network: Adaptive Unsupervised Feature Selection on Attributed Networks (JL, RG, CL, HL0), pp. 92–100.
KDD-2019-HartvigsenSKR #adaptation #classification #network #policy: Adaptive-Halting Policy Network for Early Classification (TH, CS, XK, EAR), pp. 101–110.
KDD-2019-WangYCZ #convergence #learning #performance: ADMM for Efficient Deep Learning with Global Convergence (JW, FY, XC0, LZ0), pp. 111–119.
KDD-2019-HuFS #learning #network: Adversarial Learning on Heterogeneous Information Networks (BH, YF0, CS), pp. 120–129.
KDD-2019-WangFXL #learning #mobile #profiling #representation: Adversarial Substructured Representation Learning for Mobile User Profiling (PW, YF, HX, XL), pp. 130–138.
KDD-2019-ZhangYY #learning #robust: Adversarial Variational Embedding for Robust Semi-supervised Learning (XZ0, LY, FY), pp. 139–147.
KDD-2019-AvdiukhinMYZ #constraints #robust: Adversarially Robust Submodular Maximization under Knapsack Constraints (DA, SM, GY, SZ), pp. 148–156.
KDD-2019-YuSJ #interactive #recommendation #visual notation: A Visual Dialog Augmented Interactive Recommender System (TY, YS, HJ), pp. 157–165.
KDD-2019-GoodrichRLS: Assessing The Factual Accuracy of Generated Text (BG, VR, PJL, MS), pp. 166–175.
KDD-2019-FuZCR #gpu #named #optimisation #performance #robust #visualisation: AtSNE: Efficient and Robust Visualization on GPU through Hierarchical Optimization (CF, YZ, DC, XR), pp. 176–186.
KDD-2019-GuanMW: Attribute-Driven Backbone Discovery (SG, HM, YW), pp. 187–195.
KDD-2019-SongS #modelling: Auditing Data Provenance in Text-Generation Models (CS, VS), pp. 196–206.
KDD-2019-LiuFWWBL #automation #learning #multi: Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning (KL, YF, PW, LW, RB, XL), pp. 207–215.
KDD-2019-TuM0P0 #named #network #optimisation: AutoNE: Hyperparameter Optimization for Massive Network Embedding (KT, JM, PC0, JP, WZ0), pp. 216–225.
KDD-2019-ZhangTKLCC #axiom #modelling #multi: Axiomatic Interpretability for Multiclass Additive Models (XZ, ST, PK, YL, UC, RC), pp. 226–234.
KDD-2019-XiaoZPSZ0 #personalisation #recommendation #similarity #social: Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction (WX, HZ, HP, YS, VWZ, QY0), pp. 235–245.
KDD-2019-ZugnerG #graph #network #robust: Certifiable Robustness and Robust Training for Graph Convolutional Networks (DZ, SG), pp. 246–256.
KDD-2019-ChiangLSLBH #algorithm #clustering #graph #named #network #performance #scalability: Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (WLC, XL, SS, YL0, SB, CJH), pp. 257–266.
KDD-2019-AhmadianE0M #clustering: Clustering without Over-Representation (SA, AE, RK0, MM), pp. 267–275.
KDD-2019-GaoPH #graph #network #random: Conditional Random Field Enhanced Graph Convolutional Neural Networks (HG, JP, HH), pp. 276–284.
KDD-2019-HuangLK00M0 #ranking #synthesis: Contextual Fact Ranking and Its Applications in Table Synthesis and Compression (SH, JL, FK, XW0, YW0, DM, CY0), pp. 285–293.
KDD-2019-BendimeradLPRB #anti: Contrastive Antichains in Hierarchies (AB, JL, MP, CR, TDB), pp. 294–304.
KDD-2019-YeSDFTX #multi #network #predict: Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network (JY, LS, BD, YF, XT, HX), pp. 305–313.
KDD-2019-WangLT: Coresets for Minimum Enclosing Balls over Sliding Windows (YW, YL, KLT), pp. 314–323.
KDD-2019-LiuLTL #named: CoSTCo: A Neural Tensor Completion Model for Sparse Tensors (HL, YL, MT, YL0), pp. 324–334.
KDD-2019-SongCH #collaboration: Coupled Variational Recurrent Collaborative Filtering (QS, SC, XH), pp. 335–343.
KDD-2019-LiuLDCG #learning #named #recommendation: DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation (DL, JL0, BD, JC, RG), pp. 344–352.
KDD-2019-PangSH #detection #network: Deep Anomaly Detection with Deviation Networks (GP, CS, AvdH), pp. 353–362.
KDD-2019-RenQZY00 #realtime: Deep Landscape Forecasting for Real-time Bidding Advertising (KR, JQ, LZ, ZY, WZ0, YY0), pp. 363–372.
KDD-2019-OkawaIK0TU #information management #predict #process: Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information (MO, TI, TK, YT0, HT, NU), pp. 373–383.
KDD-2019-KeXZBL #framework #learning #named #online #predict: DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks (GK, ZX, JZ, JB0, TYL), pp. 384–394.
KDD-2019-ShuCW0L #detection #named: dEFEND: Explainable Fake News Detection (KS, LC, SW, DL0, HL0), pp. 395–405.
KDD-2019-WuHX #classification #graph #named #network: DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification (JW, JH, JX), pp. 406–415.
KDD-2019-WuZ #ambiguity #analysis #linear #reduction: Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction (JHW, MLZ), pp. 416–424.
KDD-2019-DoronSS #black box #interactive #modelling: Discovering Unexpected Local Nonlinear Interactions in Scientific Black-box Models (MD, IS, DS), pp. 425–435.
KDD-2019-Zhou0Y #online: Dual Averaging Method for Online Graph-structured Sparsity (BZ, FC0, YY), pp. 436–446.
KDD-2019-WuGGWC #modelling #predict #recommendation: Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination (QW, YG, XG, PW, GC), pp. 447–457.
KDD-2019-MatsubaraS #data type #modelling: Dynamic Modeling and Forecasting of Time-evolving Data Streams (YM, YS), pp. 458–468.
KDD-2019-ZangC0W: Dynamical Origins of Distribution Functions (CZ, PC, WZ0, FW), pp. 469–478.
KDD-2019-LiHWL #approach #community #detection #named: EdMot: An Edge Enhancement Approach for Motif-aware Community Detection (PZL, LH, CDW, JHL), pp. 479–487.
KDD-2019-ChenSJ0Z0 #behaviour #effectiveness #performance #recommendation #reuse #using: Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation (LC, SS, CSJ, BY0, ZZ, LS0), pp. 488–498.
KDD-2019-WangLCJ #effectiveness #game studies #learning #performance #representation #retrieval: Effective and Efficient Sports Play Retrieval with Deep Representation Learning (ZW, CL, GC, CJ), pp. 499–509.
KDD-2019-LiZY #effectiveness #learning #performance: Efficient and Effective Express via Contextual Cooperative Reinforcement Learning (YL, YZ, QY), pp. 510–519.
KDD-2019-WuYHZX0JA #kernel #performance #random #string: Efficient Global String Kernel with Random Features: Beyond Counting Substructures (LW, IEHY, SH, LZ0, KX, LM0, SJ, CCA), pp. 520–528.
KDD-2019-Chang #clique #graph #performance #scalability: Efficient Maximum Clique Computation over Large Sparse Graphs (LC), pp. 529–538.
KDD-2019-WangWZPL #algorithm #network #personalisation #recommendation: Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation (JW, NW, WXZ, FP, XL), pp. 539–547.
KDD-2019-WangYWNHC #collaboration #generative: Enhancing Collaborative Filtering with Generative Augmentation (QW, HY, HW, QVHN, ZH, LC), pp. 548–556.
KDD-2019-YaoYX #semantics #word: Enhancing Domain Word Embedding via Latent Semantic Imputation (SY, DY, KX), pp. 557–565.
KDD-2019-ShangYLQMY #learning #re-engineering #recommendation: Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation (WS, YY, QL, ZQ, YM, JY), pp. 566–576.
KDD-2019-AdhikariXRP #named: EpiDeep: Exploiting Embeddings for Epidemic Forecasting (BA, XX, NR, BAP), pp. 577–586.
KDD-2019-ParamonovSS #statistics: Estimating Graphlet Statistics via Lifting (KP, DS, JS), pp. 587–595.
KDD-2019-ParkKDZF #graph #network #using: Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks (NP, AK, XLD, TZ, CF), pp. 596–606.
KDD-2019-YangWZK #named #performance: ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data (SY, JW, XZ, DK), pp. 607–616.
KDD-2019-GongZDLGSOZ #clique #optimisation #recommendation: Exact-K Recommendation via Maximal Clique Optimization (YG, YZ, LD, QL, ZG, FS, WO, KQZ), pp. 617–626.
KDD-2019-LiuTLZCMW #adaptation #learning: Exploiting Cognitive Structure for Adaptive Learning (QL0, ST, CL, HZ, EC, HM, SW), pp. 627–635.
KDD-2019-WuLWCW #online: Factorization Bandits for Online Influence Maximization (QW, ZL, HW, WC, HW), pp. 636–646.
KDD-2019-YoonHSF #approach #detection #graph #performance: Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach (MY, BH, KS, CF), pp. 647–657.
KDD-2019-WangD #approximate #empirical #performance: Fast Approximation of Empirical Entropy via Subsampling (CW, BD), pp. 658–667.
KDD-2019-LiCZZ0L #ecosystem #question #social: Fates of Microscopic Social Ecosystems: Keep Alive or Dead? (HL, PC, CZ, TZ, WZ0, YL), pp. 668–676.
KDD-2019-AmelkinS #network #recommendation #social: Fighting Opinion Control in Social Networks via Link Recommendation (VA, AKS), pp. 677–685.
KDD-2019-KlyuchnikovMKMK #multi: Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging (NK, DM, GK, EM, PK), pp. 686–695.
KDD-2019-ZouKCC0 #evaluation #policy #robust: Focused Context Balancing for Robust Offline Policy Evaluation (HZ, KK, BC, PC, PC0), pp. 696–704.
KDD-2019-HanYZSLZ0K #graph #identification #matrix #named #network: GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization (PH, PY, PZ, SS, YL0, JZ, XG0, PK), pp. 705–713.
KDD-2019-MonathZSMA #clustering #using: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space (NM, MZ, DS, AM, AA), pp. 714–722.
KDD-2019-0001WAT #graph #network: Graph Convolutional Networks with EigenPooling (YM0, SW, CCA, JT), pp. 723–731.
KDD-2019-HuangSLH #graph #network #random: Graph Recurrent Networks With Attributed Random Walks (XH, QS, YL, XH), pp. 732–740.
KDD-2019-GaoJ #graph #learning #network #representation: Graph Representation Learning via Hard and Channel-Wise Attention Networks (HG, SJ), pp. 741–749.
KDD-2019-Do0V #graph transformation #network #policy #predict: Graph Transformation Policy Network for Chemical Reaction Prediction (KD, TT0, SV), pp. 750–760.
KDD-2019-JiaSSB #graph #learning: Graph-based Semi-Supervised & Active Learning for Edge Flows (JJ, MTS, SS, ARB), pp. 761–771.
KDD-2019-YanZDSSK #classification #named #network: GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data (YY, JZ, MD, ES, CSS, DK), pp. 772–782.
KDD-2019-MengY0N #framework #induction #named: HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings (CM, JY, BR0, JN), pp. 783–792.
KDD-2019-ZhangSHSC #graph #network: Heterogeneous Graph Neural Network (CZ, DS, CH0, AS, NVC), pp. 793–803.
KDD-2019-0001S #markov: Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications (ZJ0, AMS), pp. 804–813.
KDD-2019-CuiDZYZ0 #crowdsourcing #ranking: Hidden POI Ranking with Spatial Crowdsourcing (YC, LD, YZ0, BY0, VWZ, KZ0), pp. 814–824.
KDD-2019-MaKL #network #recommendation: Hierarchical Gating Networks for Sequential Recommendation (CM, PK, XL), pp. 825–833.
KDD-2019-FeiTL #learning #multi #predict #word: Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction (HF, ST, PL0), pp. 834–842.
KDD-2019-JhaXWZ #co-evolution #concept #generative: Hypothesis Generation From Text Based On Co-Evolution Of Biomedical Concepts (KJ, GX, YW, AZ), pp. 843–851.
KDD-2019-MarxV #using: Identifiability of Cause and Effect using Regularized Regression (AM, JV), pp. 852–861.
KDD-2019-Jia0RLH #quality: Improving the Quality of Explanations with Local Embedding Perturbations (YJ, JB0, KR, CL, MEH), pp. 875–884.
KDD-2019-ChengSHZ #analysis #modelling #performance: Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis (WC, YS, LH, YZ), pp. 885–893.
KDD-2019-LiYZBQL #optimisation: Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding (ZL, DY, LZ, JB0, TQ, TYL), pp. 894–902.
KDD-2019-MingXQR #learning #prototype #sequence: Interpretable and Steerable Sequence Learning via Prototypes (YM, PX, HQ, LR), pp. 903–913.
KDD-2019-YanLSZZ0 #profiling: Interview Choice Reveals Your Preference on the Market: To Improve Job-Resume Matching through Profiling Memories (RY0, RL, YS, TZ, XZ0, DZ0), pp. 914–922.
KDD-2019-LiuMLZM #user satisfaction: Investigating Cognitive Effects in Session-level Search User Satisfaction (ML, JM, YL, MZ0, SM), pp. 923–931.
KDD-2019-LiuTLYZH #network: Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding (NL, QT, YL, HY, JZ, XH), pp. 932–940.
KDD-2019-XuTZ #kernel #learning #multi: Isolation Set-Kernel and Its Application to Multi-Instance Learning (BCX, KMT, ZHZ), pp. 941–949.
KDD-2019-Wang00LC #graph #named #network #recommendation: KGAT: Knowledge Graph Attention Network for Recommendation (XW, XH0, YC0, ML0, TSC), pp. 950–958.
KDD-2019-NieWL #clustering #multi #named: K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters (FN, CLW, XL), pp. 959–967.
KDD-2019-WangZZLZLW #graph #network #recommendation: Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems (HW, FZ, MZ, JL, MZ, WL, ZW), pp. 968–977.
KDD-2019-ChenC0GLLW #modelling #named #recommendation: λOpt: Learn to Regularize Recommender Models in Finer Levels (YC, BC, XH0, CG, YL0, JGL, YW), pp. 978–986.
KDD-2019-JinRKKRK #network #summary: Latent Network Summarization: Bridging Network Embedding and Summarization (DJ, RAR, EK, SK, AR, DK), pp. 987–997.
KDD-2019-XieH #learning: Learning Class-Conditional GANs with Active Sampling (MKX, SJH), pp. 998–1006.
KDD-2019-DengRN #graph #learning #predict #social: Learning Dynamic Context Graphs for Predicting Social Events (SD, HR, YN), pp. 1007–1016.
KDD-2019-ZhangZJZ #learning: Learning from Incomplete and Inaccurate Supervision (ZyZ, PZ, YJ0, ZHZ), pp. 1017–1025.
KDD-2019-YoshidaTK #graph #learning #metric #mining: Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining (TY, IT, MK), pp. 1026–1036.
KDD-2019-HeLLH #learning #network: Learning Network-to-Network Model for Content-rich Network Embedding (ZH, JL0, NL, YH), pp. 1037–1045.
KDD-2019-XuH0D #network #predict #social: Link Prediction with Signed Latent Factors in Signed Social Networks (PX, WH, JW0, BD), pp. 1046–1054.
KDD-2019-Tao0WFYZ0 #memory management #modelling #named #semantics #towards: Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit (ZT, SL0, ZW, CF, LY, HZ, YF0), pp. 1055–1063.
KDD-2019-WangXLLCDWS #framework #learning #multi #named #network #social: MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network (HW, TX, QL0, DL, EC, DD, HW, WS), pp. 1064–1072.
KDD-2019-LeeIJCC #named #recommendation: MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation (HL, JI, SJ, HC, SC), pp. 1073–1082.
KDD-2019-ZhaCLY #algorithm #mining #roadmap: Mining Algorithm Roadmap in Scientific Publications (HZ, WC, KL, XY), pp. 1083–1092.
KDD-2019-Zhang0 #named #performance #similarity: MinJoin: Efficient Edit Similarity Joins via Local Hash Minima (HZ, QZ0), pp. 1093–1103.
KDD-2019-LambaS #modelling #multi #visual notation: Modeling Dwell Time Engagement on Visual Multimedia (HL, NS), pp. 1104–1113.
KDD-2019-DingZP0H #modelling #predict: Modeling Extreme Events in Time Series Prediction (DD, MZ, XP, MY0, XH0), pp. 1114–1122.
KDD-2019-ZhaoDSZLX #learning #multi #network #relational: Multiple Relational Attention Network for Multi-task Learning (JZ, BD, LS, FZ, WL, HX), pp. 1123–1131.
KDD-2019-RashedGS #classification #multi: Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings (AR, JG, LST), pp. 1132–1140.
KDD-2019-LiST #classification #higher-order #markov #multi #network #predict #random: Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction: Multi-task RNN and Higer-order MRFs for Stock Price Classification (CL, DS, DT), pp. 1141–1151.
KDD-2019-DongBB #network: Network Density of States (KD, ARB, DB), pp. 1152–1161.
KDD-2019-YangRLC #graph #named #recursion #sketching: NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching (DY, PR, BL, PCM), pp. 1162–1172.
KDD-2019-YangAKU #collaboration #named: OBOE: Collaborative Filtering for AutoML Model Selection (CY, YA, DWK, MU), pp. 1173–1183.
KDD-2019-HeXZMZY #learning #multi: Off-policy Learning for Multiple Loggers (LH, LX, WZ, ZMM, YZ, DY), pp. 1184–1193.
KDD-2019-ChenBF #modelling #network #on the: On Dynamic Network Models and Application to Causal Impact (YCC, ASB, JLF), pp. 1194–1204.
KDD-2019-ZhangLBMZ #optimisation: Optimizing Impression Counts for Outdoor Advertising (YZ, YL, ZB, SM, PZ), pp. 1205–1215.
KDD-2019-EsfandiariWAR #learning #online #optimisation: Optimizing Peer Learning in Online Groups with Affinities (ME, DW, SAY, SBR), pp. 1216–1226.
KDD-2019-WangYCW00 #graph #matrix #modelling #predict: Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling (YW, HY, HC, TW, JX0, KZ0), pp. 1227–1235.
KDD-2019-MaystreKG #flexibility: Pairwise Comparisons with Flexible Time-Dynamics (LM, VK, MG), pp. 1236–1246.
KDD-2019-KobrenSM #constraints: Paper Matching with Local Fairness Constraints (AK, BS, AM), pp. 1247–1257.
KDD-2019-DeyZSN #effectiveness #named #personalisation #predict: PerDREP: Personalized Drug Effectiveness Prediction from Longitudinal Observational Data (SD, PZ, DS, KN), pp. 1258–1268.
KDD-2019-KumarZL #interactive #network #predict: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (SK, XZ, JL), pp. 1269–1278.
KDD-2019-LiHCSWZP #graph #predict: Predicting Path Failure In Time-Evolving Graphs (JL, ZH, HC, JS, PW, JZ, LP), pp. 1279–1289.
KDD-2019-WeiCZWGXL #coordination #learning #named #network: PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network (HW, CC, GZ, KW, VVG, KX, ZL), pp. 1290–1298.
KDD-2019-LiX #data mining #mining #named #privacy #scalability: PrivPy: General and Scalable Privacy-Preserving Data Mining (YL, WX), pp. 1299–1307.
KDD-2019-GaoPH19a #generative #named #network #proximity: ProGAN: Network Embedding via Proximity Generative Adversarial Network (HG, JP, HH), pp. 1308–1316.
KDD-2019-BellettiCC #behaviour #dependence: Quantifying Long Range Dependence in Language and User Behavior to improve RNNs (FB, MC, EHC), pp. 1317–1327.
KDD-2019-YinLHCTW0 #named #representation: QuesNet: A Unified Representation for Heterogeneous Test Questions (YY, QL0, ZH, EC, WT, SW, YS0), pp. 1328–1336.
KDD-2019-TaiebK: Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions (SBT, BK), pp. 1337–1347.
KDD-2019-DiSC #learning: Relation Extraction via Domain-aware Transfer Learning (SD, YS, LC), pp. 1348–1357.
KDD-2019-CenZZYZ0 #learning #multi #network #representation: Representation Learning for Attributed Multiplex Heterogeneous Network (YC, XZ, JZ, HY, JZ, JT0), pp. 1358–1368.
KDD-2019-TangXWZL #learning #multi: Retaining Privileged Information for Multi-Task Learning (FT, CX, FW, JZ, LWHL), pp. 1369–1377.
KDD-2019-RamS #nearest neighbour: Revisiting kd-tree for Nearest Neighbor Search (PR, KS), pp. 1378–1388.
KDD-2019-ZhaoGS #keyword #mining #named: Riker: Mining Rich Keyword Representations for Interpretable Product Question Answering (JZ, ZG, HS), pp. 1389–1398.
KDD-2019-ZhuZ00 #graph #network #robust: Robust Graph Convolutional Networks Against Adversarial Attacks (DZ, ZZ, PC0, WZ0), pp. 1399–1407.
KDD-2019-YaoCC #clustering #learning #multi #robust: Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning (YY, JC0, HC), pp. 1408–1417.
KDD-2019-WuYZXZPXA #graph #kernel #random #scalability #using: Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding (LW, IEHY, ZZ0, KX, LZ0, XP0, YX, CCA), pp. 1418–1428.
KDD-2019-YinW #graph #scalability: Scalable Graph Embeddings via Sparse Transpose Proximities (YY, ZW), pp. 1429–1437.
KDD-2019-MonathKKGM #clustering #scalability: Scalable Hierarchical Clustering with Tree Grafting (NM, AK, AK, MRG, AM), pp. 1438–1448.
KDD-2019-FoucheKB #algorithm #multi #scalability: Scaling Multi-Armed Bandit Algorithms (EF, JK, KB), pp. 1449–1459.
KDD-2019-RamanSMZYV #hybrid #parallel #scalability: Scaling Multinomial Logistic Regression via Hybrid Parallelism (PR, SS, SM, XZ, HY, SVNV), pp. 1460–1470.
KDD-2019-ZouZCW0 #optimisation #policy #trust: Separated Trust Regions Policy Optimization Method (LZ, ZZ, YC, XW, WZ0), pp. 1471–1479.
KDD-2019-OhI #detection #learning #using: Sequential Anomaly Detection using Inverse Reinforcement Learning (MhO, GI), pp. 1480–1490.
KDD-2019-HuH #learning #named #network #set: Sets2Sets: Learning from Sequential Sets with Neural Networks (HH, XH0), pp. 1491–1499.
KDD-2019-HulsebosHBZSKDH #approach #data type #detection #learning #named #semantics: Sherlock: A Deep Learning Approach to Semantic Data Type Detection (MH, KZH, MAB, EZ, AS, TK, ÇD, CAH), pp. 1500–1508.
KDD-2019-SavvidesHOP #visualisation: Significance of Patterns in Data Visualisations (RS, AH, EO, KP), pp. 1509–1517.
KDD-2019-Wang0L #recommendation #social: Social Recommendation with Optimal Limited Attention (XW0, WZ0, CL), pp. 1518–1527.
KDD-2019-PellegrinaRV #mining #named #testing: SPuManTE: Significant Pattern Mining with Unconditional Testing (LP, MR, FV), pp. 1528–1538.
KDD-2019-VermaZ #graph #network: Stability and Generalization of Graph Convolutional Neural Networks (SV, ZLZ), pp. 1539–1548.
KDD-2019-ShiZYLSJ #markov #modelling #personalisation #sequence: State-Sharing Sparse Hidden Markov Models for Personalized Sequences (HS, CZ0, QY, YL0, FS, DJ), pp. 1549–1559.
KDD-2019-DeshpandeS #adaptation #modelling #streaming #using: Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units (PD, SS), pp. 1560–1568.
KDD-2019-GuoYWCZH #recommendation #streaming: Streaming Session-based Recommendation (LG0, HY, QW, TC, AZ, NQVH), pp. 1569–1577.
KDD-2019-WangYMHLS #named #privacy: SurfCon: Synonym Discovery on Privacy-Aware Clinical Data (ZW, XY, SM, YH, SML, HS), pp. 1578–1586.
KDD-2019-YuGNCPH #constraints #incremental #learning: Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning (SY, BG, KN, HC, JP, HH), pp. 1587–1595.
KDD-2019-YangTTH: Task-Adversarial Co-Generative Nets (PY, QT, HT, JH), pp. 1596–1604.
KDD-2019-WarlopMG #process #recommendation: Tensorized Determinantal Point Processes for Recommendation (RW, JM, MG), pp. 1605–1615.
KDD-2019-DengL #testing: Testing Dynamic Incentive Compatibility in Display Ad Auctions (YD, SL), pp. 1616–1624.
KDD-2019-SunZZSHX #approach #network: The Impact of Person-Organization Fit on Talent Management: A Structure-Aware Convolutional Neural Network Approach (YS, FZ, HZ, XS, QH, HX), pp. 1625–1633.
KDD-2019-JiangZ00C0 #graph #novel #representation: The Role of: A Novel Scientific Knowledge Graph Representation and Construction Model (TJ, TZ, BQ0, TL0, NVC, MJ0), pp. 1634–1642.
KDD-2019-LiC0W0 #3d #crowdsourcing #platform #problem: Three-Dimensional Stable Matching Problem for Spatial Crowdsourcing Platforms (BL, YC, YY0, GW, LC0), pp. 1643–1653.
KDD-2019-RizzoVC #policy: Time Critic Policy Gradient Methods for Traffic Signal Control in Complex and Congested Scenarios (SGR, GV, SC), pp. 1654–1664.
KDD-2019-JiaLZKK #how #learning #question #robust #towards: Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training? (XJ, SL0, HZ, SK, VK), pp. 1665–1673.
KDD-2019-FawazKPSM #network #quantum: Training and Meta-Training Binary Neural Networks with Quantum Computing (AF, PK, SP, SS, PM), pp. 1674–1681.
KDD-2019-WangJCJ #behaviour #named #predict: TUBE: Embedding Behavior Outcomes for Predicting Success (DW, TJ, NVC, MJ0), pp. 1682–1690.
KDD-2019-Zang0S0W #data flow: Uncovering Pattern Formation of Information Flow (CZ, PC0, CS, WZ0, FW0), pp. 1691–1699.
KDD-2019-ZhangFWL0 #learning: Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning (YZ, YF, PW, XL, YZ0), pp. 1700–1708.
KDD-2019-HaoCYSW #concept #knowledge base #learning #ontology #representation: Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts (JH, MC, WY, YS, WW), pp. 1709–1719.
KDD-2019-PanLW00Z #learning #predict #using: Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (ZP, YL, WW, YY0, YZ0, JZ), pp. 1720–1730.
KDD-2019-BernardiME #lessons learnt #machine learning #modelling: 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com (LB, TM, PE), pp. 1743–1751.
KDD-2019-ZhouGHZXJLX #collaboration #framework #learning #refinement: A Collaborative Learning Framework to Tag Refinement for Points of Interest (JZ, SG, RH, DZ, JX, AJ, YL, HX), pp. 1752–1761.
KDD-2019-ChenTYZC #approach #data-driven #industrial #multi #problem: A Data-Driven Approach for Multi-level Packing Problems in Manufacturing Industry (LC0, XT, MY, JZ, LC0), pp. 1762–1770.
KDD-2019-HanNCLHX #approach #generative #recommendation: A Deep Generative Approach to Search Extrapolation and Recommendation (FXH, DN, HC, KL, YH, YX), pp. 1771–1779.
KDD-2019-TangQZWXMZY #approach #multi #order: A Deep Value-network Based Approach for Multi-Driver Order Dispatching (XT, Z(Q, FZ, ZW, ZX, YM, HZ, JY), pp. 1780–1790.
KDD-2019-LiSPDJGBHG #framework: A Generalized Framework for Population Based Training (AL, OS, SP, VD, MJ, CG, DB, TH, PG), pp. 1791–1799.
KDD-2019-ChakrabortyF #framework #identification #robust: A Robust Framework for Accelerated Outcome-driven Risk Factor Identification from EHR (PC, FF), pp. 1800–1808.
KDD-2019-TianGKOCCDEI: A Severity Score for Retinopathy of Prematurity (PT, YG, JKC, SO, JPC, MFC, JGD, DE, SI), pp. 1809–1819.
KDD-2019-ZhaoHYZXY #framework: A Unified Framework for Marketing Budget Allocation (KZ, JH, LY, QZ, HX, CY), pp. 1820–1830.
KDD-2019-LiuGNWXLLX #comprehension #concept #documentation #mining #query: A User-Centered Concept Mining System for Query and Document Understanding at Tencent (BL, WG, DN, CW, SX, JL, KL, YX), pp. 1831–1841.
KDD-2019-LuoHHLZ #named #predict #quality: AccuAir: Winning Solution to Air Quality Prediction for KDD Cup 2018 (ZL, JH, KH, XL, PZ), pp. 1842–1850.
KDD-2019-DecroosBHD: Actions Speak Louder than Goals: Valuing Player Actions in Soccer (TD, LB, JVH, JD), pp. 1851–1861.
KDD-2019-HossainR #learning #process #recognition: Active Deep Learning for Activity Recognition with Context Aware Annotator Selection (HMSH, NR), pp. 1862–1870.
KDD-2019-TaiSC: Adversarial Matching of Dark Net Market Vendor Accounts (XHT, KS, NC), pp. 1871–1880.
KDD-2019-YangSZDGDQZ #automation #named: AiAds: Automated and Intelligent Advertising System for Sponsored Search (XY, DS, RZ, TD, ZG, ZD, SQ, YZ0), pp. 1881–1890.
KDD-2019-TangWYS #named #recommendation: AKUPM: Attention-Enhanced Knowledge-Aware User Preference Model for Recommendation (XT, TW, HY, HS), pp. 1891–1899.
KDD-2019-WangZTWX #named #network #using: AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks (JW, YZ0, KT, JW, ZX), pp. 1900–1908.
KDD-2019-ShenVAAHN #learning #monitoring #smarttech #using: Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning (YS, MV, AA, AA, AYH, AYN), pp. 1909–1916.
KDD-2019-RamakrishnanSLS #detection #e-commerce: Anomaly Detection for an E-commerce Pricing System (JR, ES, CL, MAS), pp. 1917–1926.
KDD-2019-HaldarARXYDZBTC #learning: Applying Deep Learning to Airbnb Search (MH, MA, PR, TX, SY, HD, QZ, NBW, BCT, BMC, TL), pp. 1927–1935.
KDD-2019-LuoWZYTCDY #automation #named: AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications (YL, MW, HZ, QY, WWT, YC, WD, QY0), pp. 1936–1945.
KDD-2019-JinSH #architecture #named #performance: Auto-Keras: An Efficient Neural Architecture Search System (HJ, QS, XH), pp. 1946–1956.
KDD-2019-LiuWXLY #automation #generative #summary: Automatic Dialogue Summary Generation for Customer Service (CL, PW, JX, ZL, JY), pp. 1957–1965.
KDD-2019-YangLWWTXG #multi #optimisation: Bid Optimization by Multivariable Control in Display Advertising (XY, YL, HW, DW, QT, JX, KG), pp. 1966–1974.
KDD-2019-SinghWWWKMDLK #migration #predict #social #social media: Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration (LS, LW, YW, YW, CK, SM, KD, YL, KK), pp. 1975–1983.
KDD-2019-GuoHJZW0 #behaviour #multi #network #predict #realtime #using: Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior (LG, LH, RJ, BZ, XW, BC0), pp. 1984–1992.
KDD-2019-AharonSSSTVD #optimisation: Carousel Ads Optimization in Yahoo Gemini Native (MA, OS, AS, AS, BT, HV, DD), pp. 1993–2001.
KDD-2019-TokuiOANOSSUVV #framework #learning #named #research: Chainer: A Deep Learning Framework for Accelerating the Research Cycle (ST, RO, TA, YN, TO, SS, SS, KU, BV, HYV), pp. 2002–2011.
KDD-2019-XiaLGLCS #case study #detection #mobile #network #privacy #social: Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study (ZX, CL, NZG, QL0, YC0, DS), pp. 2012–2022.
KDD-2019-LiuSPR #case study #graph: Characterizing and Forecasting User Engagement with In-App Action Graph: A Case Study of Snapchat (YL, XS, LP, XR), pp. 2023–2031.
KDD-2019-LiQWM #network #ranking: Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search (PL0, ZQ, XW, DM), pp. 2032–2040.
KDD-2019-ZheSX #community #detection #network #scalability: Community Detection on Large Complex Attribute Network (CZ, AS, XX), pp. 2041–2049.
KDD-2019-KobrenBYHL #knowledge base #precise: Constructing High Precision Knowledge Bases with Subjective and Factual Attributes (AK, PB0, OY, JH, IL), pp. 2050–2058.
KDD-2019-SchulzeMRLB #identification #using: Context by Proxy: Identifying Contextual Anomalies Using an Output Proxy (JPS, AM, ER, HAL, KB), pp. 2059–2068.
KDD-2019-KitadaIS #effectiveness #multi #network #predict #using: Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives (SK, HI, YS), pp. 2069–2077.
KDD-2019-OuyangZLZXLD #network #predict: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction (WO, XZ, LL, HZ, XX, ZL, YD), pp. 2078–2086.
KDD-2019-WangLYLLZ0 #approach #machine learning #nondeterminism #quantifier: Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting (BW, JL0, ZY0, HL, TL, YZ0, GZ0), pp. 2087–2095.
KDD-2019-SiciliaPG #named #using: DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data (AS, KP, KG), pp. 2096–2104.
KDD-2019-LeeIFSM #approach #data-driven #estimation #named #using: DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery (SL, SI, MF, PJS, SM), pp. 2105–2113.
KDD-2019-JiangSHSXCWKS #named #predict: DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events (RJ, XS, DH, XS, TX, ZC, ZW, KSK, RS), pp. 2114–2122.
KDD-2019-TariqLSLJCW #detection #multi #probability #using: Detecting Anomalies in Space using Multivariate Convolutional LSTM with Mixtures of Probabilistic PCA (ST, SL, YS, MSL, OJ, DC, SSW), pp. 2123–2133.
KDD-2019-YelundurCM #composition #detection #multi #overview: Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition (ARY, VC, BM), pp. 2134–2144.
KDD-2019-ChenJMFKSPSYMSS #metric #multimodal: Developing Measures of Cognitive Impairment in the Real World from Consumer-Grade Multimodal Sensor Streams (RC, FJ, NM, LF, LK, AS, MP, JS, RY, VM, MS, HHS, HJJ, BT, AT), pp. 2145–2155.
KDD-2019-FabijanGGOQVD #online #taxonomy: Diagnosing Sample Ratio Mismatch in Online Controlled Experiments: A Taxonomy and Rules of Thumb for Practitioners (AF, JG, SG, JO, WQ, LV, PAD), pp. 2156–2164.
KDD-2019-QinZZXZMZX #named #personalisation #perspective #recommendation: DuerQuiz: A Personalized Question Recommender System for Intelligent Job Interview (CQ, HZ, CZ, TX, FZ, CM, JZ, HX), pp. 2165–2173.
KDD-2019-ShuklaKOMY: Dynamic Pricing for Airline Ancillaries with Customer Context (NS, AK, KO, LM, KY), pp. 2174–2182.
KDD-2019-BabaevSTU #learning: E.T.-RNN: Applying Deep Learning to Credit Loan Applications (DB, MS, AT, DU), pp. 2183–2190.
KDD-2019-WagstaffDDACCDP #detection: Enabling Onboard Detection of Events of Scientific Interest for the Europa Clipper Spacecraft (KLW, GD, AD, SA, SC, MC, ID, CAP), pp. 2191–2201.
KDD-2019-YangSLPB #biology: Estimating Cellular Goals from High-Dimensional Biological Data (LY, MAS, JCL, BOP, JB), pp. 2202–2211.
KDD-2019-BeutelCDQWWHZHC #ranking #recommendation: Fairness in Recommendation Ranking through Pairwise Comparisons (AB, JC, TD, HQ, LW, YW, LH, ZZ, LH, EHC, CG), pp. 2212–2220.
KDD-2019-GeyikAK #ranking #recommendation: Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search (SCG, SA, KK), pp. 2221–2231.
KDD-2019-HuNYZ #collaboration #distributed #framework #machine learning #named: FDML: A Collaborative Machine Learning Framework for Distributed Features (YH, DN, JY, SZ), pp. 2232–2240.
KDD-2019-TuLYC #approach #feedback #modelling: Feedback Shaping: A Modeling Approach to Nurture Content Creation (YT, CL, YY, SC), pp. 2241–2250.
KDD-2019-deWetO #learning: Finding Users Who Act Alike: Transfer Learning for Expanding Advertiser Audiences (Sd, JO), pp. 2251–2259.
KDD-2019-SahooHKWLALH #image #learning #named #recognition: FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging (DS, WH, SK, XW, HL, PA, EPL, SCHH), pp. 2260–2268.
KDD-2019-HughesCZ #generative #learning: Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning (JWH, KhC, RZ), pp. 2269–2277.
KDD-2019-ZhengXKWMAY #linear #predict #using: Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression (YZ, LX, TK, JW0, HM, RA, KY), pp. 2278–2286.
KDD-2019-ChenLBCZLTWDCSW #realtime: Gmail Smart Compose: Real-Time Assisted Writing (MXC, BNL, GB, YC, SZ, JL, JT, YW, AMD, ZC, TS, YW), pp. 2287–2295.
KDD-2019-AroraC0KLLMTTW #scalability: Hard to Park?: Estimating Parking Difficulty at Scale (NA, JC, RK0, IK, YL, HJL, AM, AT, IT, YW), pp. 2296–2304.
KDD-2019-ZhangHDV #how #lessons learnt: How to Invest my Time: Lessons from Human-in-the-Loop Entity Extraction (SZ, LH, ECD, SV), pp. 2305–2313.
KDD-2019-LiuTZLDX #multi #named #personalisation #recommendation: Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System (HL, YT, PZ, XL, JD, HX), pp. 2314–2324.
KDD-2019-HwangOCPM #machine learning: Improving Subseasonal Forecasting in the Western U.S. with Machine Learning (JH, PO, JC, KP, LM), pp. 2325–2335.
KDD-2019-Ding0LXZSJS #realtime #recommendation: Infer Implicit Contexts in Real-time Online-to-Offline Recommendation (XD, JT0, TXL, CX, YZ, FS, QJ, DS), pp. 2336–2346.
KDD-2019-0009ZGZNQH #framework #graph #named #recommendation #scalability: IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation (JZ0, ZZ, ZG, WZ0, WN, GQ, XH), pp. 2347–2357.
KDD-2019-GuptaCY #optimisation: Internal Promotion Optimization (RG, GC, SY), pp. 2358–2366.
KDD-2019-LuSPHM #mining: Investigate Transitions into Drug Addiction through Text Mining of Reddit Data (JL, SS, RP, MAH, GM), pp. 2367–2375.
KDD-2019-ChenZBXL #behaviour #predict #what: Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction (CC, LZ, JB0, CX, TYL), pp. 2376–2384.
KDD-2019-JhaWYWFLCA #framework #named: IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery (DJ, LTW, ZY, CW, ITF, WkL, ANC, AA), pp. 2385–2393.
KDD-2019-HwangLVGXN #framework #scalability #video: Large-Scale Training Framework for Video Annotation (SJH, JL, BV, AG, ZX, AN), pp. 2394–2402.
KDD-2019-MaZXLCXWW #comprehension #framework #scalability: Large-scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework (XM, LZ, LX, ZL, GC, ZX, YW, ZW), pp. 2403–2411.
KDD-2019-ZhaiWTPR #learning #visual notation: Learning a Unified Embedding for Visual Search at Pinterest (AZ, HYW, ET, DHP, CR), pp. 2412–2420.
KDD-2019-ParkLHHLC #learning #quality: Learning Sleep Quality from Daily Logs (SP, CTL, SH, CH, SWL, MC), pp. 2421–2429.
KDD-2019-KillianWSCDT #learning #using: Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data (JAK, BW, AS, VC, BD, MT), pp. 2430–2438.
KDD-2019-GengLLJXZYLZ #named #network #predict: LightNet: A Dual Spatiotemporal Encoder Network Model for Lightning Prediction (YaG, QL, TL, LJ, LX, DZ, WY, WL, YZ), pp. 2439–2447.
KDD-2019-AhmedABCCDDEFFG #machine learning #ml: Machine Learning at Microsoft with ML.NET (ZA, SA, MB, RC, WSC, YD, XD, VE, SF, TF, AG, MH, SI, MI, NK, GK, PL, IM, SM, SM, GN, JO, GO, AP, JP, PR, MZS, MW, SZ, YZ), pp. 2448–2458.
KDD-2019-SrivastavaHK #approach #machine learning: Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning (MS, HH, AK), pp. 2459–2468.
KDD-2019-YeS #named #online #ranking: MediaRank: Computational Ranking of Online News Sources (JY, SS), pp. 2469–2477.
KDD-2019-FanZHSHML #graph #network #recommendation: Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation (SF, JZ, XH, CS, LH, BM, YL), pp. 2478–2486.
KDD-2019-ZhangTDZW #health #named #predict #risk management: MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records (XSZ, FT, HHD, JZ, FW), pp. 2487–2495.
KDD-2019-FanGZMSL #generative #named #towards: MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search (MF, JG, SZ, SM, MS, PL0), pp. 2509–2517.
KDD-2019-TangBMLLK #classification #e-commerce #image #named #scalability: MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data (YT, FB, SM, YL, YL, SK), pp. 2518–2526.
KDD-2019-FanZPLZYWWPH #learning #multi: Multi-Horizon Time Series Forecasting with Temporal Attention Learning (CF, YZ, YP, XL, CZ0, RY, DW, WW, JP, HH), pp. 2527–2535.
KDD-2019-TaoLZZWFC #detection #game studies #multi #named #network #online: MVAN: Multi-view Attention Networks for Real Money Trading Detection in Online Games (JT, JL, SZ, SZ, RW, CF, PC), pp. 2536–2546.
KDD-2019-RawatLY #learning #multi #using: Naranjo Question Answering using End-to-End Multi-task Learning Model (BPSR, FL, HY0), pp. 2547–2555.
KDD-2019-LiuCGD #parametricity #predict: Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data - An Application to Climate Prediction (YL, JC, ARG, JGD), pp. 2556–2564.
KDD-2019-KapoorLK #3d #image #modelling #named: Nostalgin: Extracting 3D City Models from Historical Image Data (AK, HL, RK), pp. 2565–2575.
KDD-2019-WuWAHHX #named #personalisation #recommendation: NPA: Neural News Recommendation with Personalized Attention (CW, FW, MA, JH, YH, XX0), pp. 2576–2584.
KDD-2019-ZhangLTDYZGWSLW #graph #named #scalability #towards: OAG: Toward Linking Large-scale Heterogeneous Entity Graphs (FZ, XL, JT, YD, PY, JZ, XG, YW, BS, RL, KW), pp. 2585–2595.
KDD-2019-WengZBT #named #performance: OCC: A Smart Reply System for Efficient In-App Communications (YW, HZ, FB, GT), pp. 2596–2603.
KDD-2019-Yeh0DDNK #monitoring #online #realtime: Online Amnestic DTW to allow Real-Time Golden Batch Monitoring (CCMY, YZ0, HAD, AD, MN, EJK), pp. 2604–2612.
KDD-2019-HuangWZZZYC #behaviour #modelling #multi #online #predict: Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics (CH0, XW, XZ, CZ, JZ, DY, NVC), pp. 2613–2622.
KDD-2019-AkibaSYOK #framework #named #optimisation: Optuna: A Next-generation Hyperparameter Optimization Framework (TA, SS, TY, TO, MK), pp. 2623–2631.
KDD-2019-ZhaoZWGGQNCL #learning #multi #personalisation: Personalized Attraction Enhanced Sponsored Search with Multi-task Learning (WZ0, BZ, BW, ZG, WG, GQ, WN, JC, HL), pp. 2632–2642.
KDD-2019-KrausF #personalisation #predict #sequence: Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching (MK, SF), pp. 2643–2652.
KDD-2019-ZhuangL #multi #named: PinText: A Multitask Text Embedding System in Pinterest (JZ, YL), pp. 2653–2661.
KDD-2019-ChenHXGGSLPZZ #generative #named #personalisation #recommendation: POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion (WC, PH, JX, XG, CG, FS, CL, AP, HZ, BZ), pp. 2662–2670.
KDD-2019-PiBZZG #behaviour #modelling #predict: Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction (QP, WB, GZ, XZ, KG), pp. 2671–2679.
KDD-2019-LebedevIRGMOGBS: Precipitation Nowcasting with Satellite Imagery (VL, VI, IR, AG, AM, SO, RG, IB, DS), pp. 2680–2688.
KDD-2019-PanMRSF #learning #multi #online #predict: Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising (JP, YM, ALR, YS, AF), pp. 2689–2697.
KDD-2019-SheehanMTUJBLE #development #predict #using #wiki: Predicting Economic Development using Geolocated Wikipedia Articles (ES, CM, MT, BU, NJ, MB, DBL, SE), pp. 2698–2706.
KDD-2019-YabeTSSU #behaviour #predict #using #web: Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior (TY, KT, TS, YS, SVU), pp. 2707–2717.
KDD-2019-NosakhareP #behaviour #modelling #probability: Probabilistic Latent Variable Modeling for Assessing Behavioral Influences on Well-Being (EN, RWP), pp. 2718–2726.
KDD-2019-SvyatkovskiyZFS #code completion #named: Pythia: AI-assisted Code Completion System (AS, YZ, SF, NS), pp. 2727–2735.
KDD-2019-ZhaoWSNHJSNBRPL #detection #power management #smarttech: Raise to Speak: An Accurate, Low-power Detector for Activating Voice Assistants on Smartwatches (SZ, BW, SS, HN, RH, MJ, KS, BN, MB, SR, TP, KL, CG), pp. 2736–2744.
KDD-2019-RolnickAPKMN #clustering #design #random: Randomized Experimental Design via Geographic Clustering (DR, KA, JPA, SK, VSM, AN), pp. 2745–2753.
KDD-2019-JiangYBLWM #ranking: Ranking in Genealogy: Search Results Fusion at Ancestry (PJ, YY, GB, FAL, RW, AM), pp. 2754–2764.
KDD-2019-LiuGZL #realtime #recommendation: Real-time Attention Based Look-alike Model for Recommender System (YL, KG, XZ, LL), pp. 2765–2773.
KDD-2019-FedoryszakFRZ #data type #detection #realtime #social: Real-time Event Detection on Social Data Streams (MF, BF, VR, CZ), pp. 2774–2782.
KDD-2019-OchiaiSYTF #realtime #recommendation #smarttech: Real-time On-Device Troubleshooting Recommendation for Smartphones (KO, KS, NY, YT, YF), pp. 2783–2791.
KDD-2019-OkoshiTT #adaptation #deployment #scheduling #smarttech: Real-World Product Deployment of Adaptive Push Notification Scheduling on Smartphones (TO, KT, HT), pp. 2792–2800.
KDD-2019-GrislainPT #network #probability #realtime: Recurrent Neural Networks for Stochastic Control in Real-Time Bidding (NG, NP0, AT), pp. 2801–2809.
KDD-2019-ZouXDS0Y #learning #recommendation: Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems (LZ, LX, ZD, JS, WL0, DY), pp. 2810–2818.
KDD-2019-KalraWB0 #predict: Reserve Price Failure Rate Prediction with Header Bidding in Display Advertising (AK, CW, CB, YC0), pp. 2819–2827.
KDD-2019-SuZNLSP #detection #multi #network #probability #robust: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network (YS, YZ, CN, RL, WS, DP), pp. 2828–2837.
KDD-2019-LinSQL0ZJ #precise #process #realtime #robust: Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement (ML, XS, QQ, HL, LS0, SZ, RJ), pp. 2838–2847.
KDD-2019-Li0WGYK #adaptation #kernel #learning #multi #predict: Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points (ZL, JZ0, QW0, YG, JY, CK), pp. 2848–2856.
KDD-2019-TranS #feature model #predict #scalability: Seasonal-adjustment Based Feature Selection Method for Predicting Epidemic with Large-scale Search Engine Logs (TQT, JS), pp. 2857–2866.
KDD-2019-BiswasPVSN #interactive #named #realtime: Seeker: Real-Time Interactive Search (AB, TTP, MV, BS, HN), pp. 2867–2875.
KDD-2019-NigamSMLDSTGY #semantics: Semantic Product Search (PN, YS, VM, VL, WAD, AS, CHT, HG, BY), pp. 2876–2885.
KDD-2019-SpathisRFMR #learning #multi #self #sequence: Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data (DS, SSR, KF, CM, JR), pp. 2886–2894.
KDD-2019-DuWYZT #online #recommendation: Sequential Scenario-Specific Meta Learner for Online Recommendation (ZD, XW, HY, JZ, JT0), pp. 2895–2904.
KDD-2019-MoosaviS00R #scalability: Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data (SM, MHS, AN0, SP0, RR), pp. 2905–2913.
KDD-2019-DimmeryBS #online: Shrinkage Estimators in Online Experiments (DD, EB, JS), pp. 2914–2922.
KDD-2019-JinHSWLSK #email #network: Smart Roles: Inferring Professional Roles in Email Networks (DJ, MH, TS, MW, WL, LS, DK), pp. 2923–2933.
KDD-2019-ChenreddyPNCA #learning #named #optimisation: SMOILE: A Shopper Marketing Optimization and Inverse Learning Engine (ARC, PP, SN, RC, RA), pp. 2934–2942.
KDD-2019-YanYOZBWH #social #validation: Social Skill Validation at LinkedIn (XY, JY, MO, LZ, JB, SW, QH), pp. 2943–2951.
KDD-2019-ChowdhurySM #detection: Structured Noise Detection: Application on Well Test Pressure Derivative Data (FAC, SS, AM), pp. 2952–2960.
KDD-2019-SheetritNKS #predict #probability: Temporal Probabilistic Profiles for Sepsis Prediction in the ICU (ES, NN, DK, YS), pp. 2961–2969.
KDD-2019-PasumarthiBWLBN #library #named #ranking #scalability: TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank (RKP, SB, XW, CL0, MB, MN, JP, NG, RA, SW), pp. 2970–2978.
KDD-2019-SchonD0 #fault #how #predict #using: The Error is the Feature: How to Forecast Lightning using a Model Prediction Error (CS, JD, RM0), pp. 2979–2988.
KDD-2019-YinH #analysis #estimation #identification #testing: The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis (XY, LH), pp. 2989–2999.
KDD-2019-YeS19a #social #social media: The Secret Lives of Names?: Name Embeddings from Social Media (JY, SS), pp. 3000–3008.
KDD-2019-RenXWYHKXYTZ #detection: Time-Series Anomaly Detection Service at Microsoft (HR, BX, YW, CY, CH, XK, TX, MY, JT, QZ), pp. 3009–3017.
KDD-2019-ZhouMZ #memory management #network #personalisation #recommendation #topic: Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation (XZ, CM, ZZ), pp. 3018–3028.
KDD-2019-Venkataraman0 #identification #towards: Towards Identifying Impacted Users in Cellular Services (SV, JW0), pp. 3029–3039.
KDD-2019-ChenLZYZ0 #e-commerce #generative #knowledge-based #personalisation #towards: Towards Knowledge-Based Personalized Product Description Generation in E-commerce (QC, JL, YZ, HY, JZ, JT0), pp. 3040–3050.
KDD-2019-FauvelMFFT #detection #machine learning #towards: Towards Sustainable Dairy Management - A Machine Learning Enhanced Method for Estrus Detection (KF, VM, ÉF, PF, AT), pp. 3051–3059.
KDD-2019-Pan0Z00 #named: TrajGuard: A Comprehensive Trajectory Copyright Protection Scheme (ZP, JB0, WZ0, YY0, YZ0), pp. 3060–3070.
KDD-2019-SuzukiWN #learning #scheduling: TV Advertisement Scheduling by Learning Expert Intentions (YS, WMW, IN), pp. 3071–3081.
KDD-2019-SuhrBZGC #case study #framework #platform: Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform (TS, AJB, MZ, KPG, AC), pp. 3082–3092.
KDD-2019-LuY0ZXLL0 #social: Uncovering the Co-driven Mechanism of Social and Content Links in User Churn Phenomena (YL, LY, PC0, CZ, RX, YL, LL, WZ0), pp. 3093–3101.
KDD-2019-ZhouMGBB #comprehension #network #using: Understanding Consumer Journey using Attention based Recurrent Neural Networks (YZ, SM, JG, TB, NB), pp. 3102–3111.
KDD-2019-JiangSHHH #comprehension #e-commerce: Understanding the Role of Style in E-commerce Shopping (HJ, AS, AH, DH, LH), pp. 3112–3120.
KDD-2019-WengCS: Unsupervised Clinical Language Translation (WHW, YAC, PS), pp. 3121–3131.
KDD-2019-LiangOJRLZRZ #fine-grained #named: UrbanFM: Inferring Fine-Grained Urban Flows (YL, KO, LJ, SR, YL0, JZ, DSR, YZ0), pp. 3132–3142.
KDD-2019-ChenLPS #predict #twitter #using: Using Twitter to Predict When Vulnerabilities will be Exploited (HC0, RL, NP, VSS), pp. 3143–3152.
KDD-2019-DingGV #constraints #optimisation: Whole Page Optimization with Global Constraints (WD, DG, SVNV), pp. 3153–3161.
KDD-2019-Guestrin #machine learning: 4 Perspectives in Human-Centered Machine Learning (CG), p. 3162.
KDD-2019-Bradley #challenge #complexity #set: Addressing Challenges in Data Science: Scale, Skill Sets and Complexity (JB), p. 3163.
KDD-2019-Srivastava: AI for Small Businesses and Consumers: Applications and Innovations (AS), p. 3164.
KDD-2019-Yang #framework #graph #named #network #platform: AliGraph: A Comprehensive Graph Neural Network Platform (HY), pp. 3165–3166.
KDD-2019-Thondikulam #approach #ml #scalability: Analytics Journey Map: An Approach Enable to ML at Scale (GT), p. 3167.
KDD-2019-Sawaf #ml: Applications of AI/ML in Established and New Industries (HS), p. 3168.
KDD-2019-Chellapilla #hardware #self: Building a Better Self-Driving Car: Hardware, Software, and Knowledge (KC), p. 3169.
KDD-2019-Xu #challenge: Data Science Challenges @ LinkedIn (YX), p. 3170.
KDD-2019-Nemani #generative: Earth Observations from a New Generation of Geostationary Satellites (RRN), p. 3171.
KDD-2019-Heckerman #big data: Exploiting High Dimensionality in Big Data (DH), p. 3172.
KDD-2019-Maas #facebook: Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery (PM), p. 3173.
KDD-2019-Caruana #black box #machine learning #modelling: Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning (RC), p. 3174.
KDD-2019-Sundaresan #developer #scalability: From Code to Data: AI at Scale for Developer Productivity (NS), p. 3175.
KDD-2019-Salakhutdinov #learning: Integrating Domain-Knowledge into Deep Learning (RS), p. 3176.
KDD-2019-LiakhovichD #machine learning: Preventing Rhino Poaching through Machine Learning (OL, GDC), p. 3177.
KDD-2019-Rosales #ecosystem #optimisation: Product Ecosystem Optimization at LinkedIn (RR), p. 3178.
KDD-2019-Xia #analysis #graph #platform: Roll of Unified Graph Analysis Platforms (YX), p. 3179.
KDD-2019-Grewal: Seven Years of Data Science at Airbnb (EG), p. 3180.
KDD-2019-Ranganathan: Spinning the AI Pinwheel (JR), p. 3181.
KDD-2019-KatsiapisH #ml #towards: Towards ML Engineering with TensorFlow Extended (TFX) (KK, KH), p. 3182.
KDD-2019-Ye #approach #data-driven #named: Transportation: A Data Driven Approach (JY), p. 3183.
KDD-2019-Gollapudi #online: Welfare Maximization in Online Two-sided Marketplaces (SG), p. 3184.
KDD-2019-CautisMT #adaptation: Adaptive Influence Maximization (BC, SM, NT), pp. 3185–3186.
KDD-2019-Lin #classification #multi #roadmap: Advances in Cost-sensitive Multiclass and Multilabel Classification (HTL), pp. 3187–3188.
KDD-2019-ShiDGF #challenge #online: Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments (XS, PAD, SG, XF), pp. 3189–3190.
KDD-2019-ShangSL0 #mining #network: Constructing and Mining Heterogeneous Information Networks from Massive Text (JS, JS, LL, JH0), pp. 3191–3192.
KDD-2019-DongR #integration #machine learning: Data Integration and Machine Learning: A Natural Synergy (XLD, TR), pp. 3193–3194.
KDD-2019-XiaoS #data mining #development #mining #named #tutorial: Tutorial: Data Mining Methods for Drug Discovery and Development (CX, JS), pp. 3195–3196.
KDD-2019-Chien #comprehension #learning #mining: Deep Bayesian Mining, Learning and Understanding (JTC), pp. 3197–3198.
KDD-2019-GuoGSLZCA #natural language #recommendation: Deep Natural Language Processing for Search and Recommender Systems (WG, HG, JS, BL, LZ, BCC, DA), pp. 3199–3200.
KDD-2019-Qin0Y #learning: Deep Reinforcement Learning with Applications in Transportation (Z(Q, JT0, JY), pp. 3201–3202.
KDD-2019-GadeGKMT #industrial: Explainable AI in Industry (KG, SCG, KK, VM, AT), pp. 3203–3204.
KDD-2019-BirdHKKM #challenge #lessons learnt #machine learning: Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned (SB, BH, KK, EK, MM), pp. 3205–3206.
KDD-2019-ZafaraniZSL #detection #problem #research: Fake News Research: Theories, Detection Strategies, and Open Problems (RZ, XZ, KS, HL0), pp. 3207–3208.
KDD-2019-FaloutsosFGJW #theory and practice: Forecasting Big Time Series: Theory and Practice (CF, VF, JG, TJ, YW), pp. 3209–3210.
KDD-2019-Ramdas #scalability: Foundations of Large-Scale Sequential Experimentation (AR), pp. 3211–3212.
KDD-2019-ZhouH #representation: Gold Panning from the Mess: Rare Category Exploration, Exposition, Representation, and Interpretation (DZ, JH), pp. 3213–3214.
KDD-2019-PellegrinaRV19a #mining #statistics #testing: Hypothesis Testing and Statistically-sound Pattern Mining (LP, MR, FV), pp. 3215–3216.
KDD-2019-Eliassi-RadCL #bias #network: Incompleteness in Networks: Biases, Skewed Results, and Some Solutions (TER, RSC, TL), pp. 3217–3218.
KDD-2019-Kovalerchuk #information management #visual notation: Interpretable Knowledge Discovery Reinforced by Visual Methods (BK), pp. 3219–3220.
KDD-2019-Huang0DLLPST0Y0 #algorithm #learning #network #theory and practice: Learning From Networks: Algorithms, Theory, and Applications (XH, PC0, YD, JL, HL0, JP, LS, JT0, FW0, HY, WZ0), pp. 3221–3222.
KDD-2019-SpiliopoulouP #comprehension #mining: Mining and Model Understanding on Medical Data (MS, PP), pp. 3223–3224.
KDD-2019-RozenshteinG #mining #network: Mining Temporal Networks (PR, AG), pp. 3225–3226.
KDD-2019-YanXL #modelling #process: Modeling and Applications for Temporal Point Processes (JY, HX, LL), pp. 3227–3228.
KDD-2019-VreekenY #data mining #mining #theory and practice: Modern MDL meets Data Mining Insights, Theory, and Practice (JV, KY), pp. 3229–3230.
KDD-2019-ZhouM0H #education #learning #optimisation: Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching (YZ, FM, JG0, JH), pp. 3231–3232.
KDD-2019-Chen0 #data mining #machine learning #mining #optimisation #order #robust: Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning (PYC, SL0), pp. 3233–3234.
KDD-2019-ZarrinkalamFB #mining #social: Social User Interest Mining: Methods and Applications (FZ, HF, EB), pp. 3235–3236.
KDD-2019-Ning00LR #identification: Spatio-temporal Event Forecasting and Precursor Identification (YN, LZ0, FC0, CTL, HR), pp. 3237–3238.
KDD-2019-MartinM #network #quality #statistics: Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks (CHM, MWM), pp. 3239–3240.
KDD-2019-AnastasiuRT #named #order #problem #tutorial: Tutorial: Are You My Neighbor?: Bringing Order to Neighbor Computing Problems (DCA, HR, AT), pp. 3241–3242.

Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, George KarypisProceedings of the 25th International Conference on Knowledge Discovery and Data MiningKDD, 2019.

Contents (374 items)

Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, George Karypis
Proceedings of the 25th International Conference on Knowledge Discovery and Data Mining
KDD, 2019.