BibSLEIGH — Proceedings of the 23rd International Conference on Knowledge Discovery and Data Mining

Proceedings of the 23rd International Conference on Knowledge Discovery and Data Mining
KDD, 2017.

KER

@proceedings{KDD-2017,
	doi           = "10.1145/3097983",
	isbn          = "978-1-4503-4887-4",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 23rd International Conference on Knowledge Discovery and Data Mining}",
	year          = 2017,
}

Contents (232 items)

KDD-2017-Dwork #question #what: What's Fair? (CD), p. 1.
KDD-2017-Miller #future of #integration: The Future of Data Integration (RJM), p. 3.
KDD-2017-Yu #predict: Three Principles of Data Science: Predictability, Stability and Computability (BY), p. 5.
KDD-2017-FayyadSS: Foreword to the Applied Data Science: Invited Talks Track at KDD-2017 (UMF, ES, AS), pp. 7–8.
KDD-2017-Rubia: More than the Sum of its Parts: Building Domino Data Lab (EAdlR), p. 9.
KDD-2017-Berglund #big data #mining: Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy (AB), p. 11.
KDD-2017-Bloom #industrial #machine learning: Industrial Machine Learning (JB), p. 13.
KDD-2017-Cao #behaviour: Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management (LC), pp. 15–16.
KDD-2017-Desai: It Takes More than Math and Engineering to Hit the Bullseye with Data (PD), p. 17.
KDD-2017-How #learning #nondeterminism #theory and practice: Planning and Learning under Uncertainty: Theory and Practice (JPH), p. 19.
KDD-2017-KarpatneK #big data #challenge #machine learning: Big Data in Climate: Opportunities and Challenges for Machine Learning (AK, VK), pp. 21–22.
KDD-2017-Mazumdar #big data #challenge #metric: Addressing Challenges with Big Data for Media Measurement (MM), p. 23.
KDD-2017-Pafka #machine learning #question: Machine Learning Software in Practice: Quo Vadis? (SP), p. 25.
KDD-2017-Parekh #design #scalability: Designing AI at Scale to Power Everyday Life (RP), p. 27.
KDD-2017-Potere: Spaceborne Data Enters the Mainstream (DP), p. 29.
KDD-2017-FayyadCRPCL #benchmark #metric #process #question: Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess? (UMF, AC, EAdlR, SP, AC, JYL), pp. 31–32.
KDD-2017-MuthukrishnanTH #future of: The Future of Artificially Intelligent Assistants (MM, AT, LPH, AG, DA), pp. 33–34.
KDD-2017-AngelinoLASR #learning: Learning Certifiably Optimal Rule Lists (EA, NLS, DA, MS, CR), pp. 35–44.
KDD-2017-AvinLNP #bound #network: Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks (CA, ZL, YN, DP), pp. 45–53.
KDD-2017-BaiWT0D #network: Unsupervised Network Discovery for Brain Imaging Data (ZB, PBW, AET, FW0, ID), pp. 55–64.
KDD-2017-BaytasXZWJZ #network #type system: Patient Subtyping via Time-Aware LSTM Networks (IMB, CX, XZ, FW0, AKJ0, JZ), pp. 65–74.
KDD-2017-ChangYY #multi #recognition #robust #visual notation: Robust Top-k Multiclass SVM for Visual Category Recognition (XC, YY, YY0), pp. 75–83.
KDD-2017-ChenZ #named: KATE: K-Competitive Autoencoder for Text (YC0, MJZ), pp. 85–94.
KDD-2017-Cohen0Y #big data #set: A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection (RC, LK0, AY), pp. 95–103.
KDD-2017-Cohen #sketching #statistics #sublinear: HyperLogLog Hyperextended: Sketches for Concave Sublinear Frequency Statistics (EC), pp. 105–114.
KDD-2017-ConteFMPT #performance #scalability: Fast Enumeration of Large k-Plexes (AC, DF, CM, MP, RT), pp. 115–124.
KDD-2017-DauK #matrix: Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery (HAD, EJK), pp. 125–134.
KDD-2017-DongCS #learning #named #network #representation #scalability: metapath2vec: Scalable Representation Learning for Heterogeneous Networks (YD, NVC, AS), pp. 135–144.
KDD-2017-EpastoLL #clustering #framework: Ego-Splitting Framework: from Non-Overlapping to Overlapping Clusters (AE, SL, RPL), pp. 145–154.
KDD-2017-FoxAJPW #using: Contextual Motifs: Increasing the Utility of Motifs using Contextual Data (IF, LA, MJ, RPB, JW), pp. 155–164.
KDD-2017-FuLTA #integer #programming #recommendation: Unsupervised P2P Rental Recommendations via Integer Programming (YF, GL, MT, CCA), pp. 165–173.
KDD-2017-GuSG #co-evolution #evolution #migration #network #social: The Co-Evolution Model for Social Network Evolving and Opinion Migration (YG, YS, JG), pp. 175–184.
KDD-2017-GuLH #algorithm #automation: Groups-Keeping Solution Path Algorithm for Sparse Regression with Automatic Feature Grouping (BG, GL, HH), pp. 185–193.
KDD-2017-GuidottiMNGP #clustering #transaction: Clustering Individual Transactional Data for Masses of Users (RG, AM, MN, FG, DP), pp. 195–204.
KDD-2017-HallacPBL #network #visual notation: Network Inference via the Time-Varying Graphical Lasso (DH, YP, SPB, JL), pp. 205–213.
KDD-2017-HallacVBL #clustering #multi: Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data (DH, SV, SPB, JL), pp. 215–223.
KDD-2017-HeHBHX #correlation #modelling #performance #topic: Efficient Correlated Topic Modeling with Topic Embedding (JH, ZH, TBK, YH, EPX), pp. 225–233.
KDD-2017-HopeCKS #mining: Accelerating Innovation Through Analogy Mining (TH, JC, AK, DS), pp. 235–243.
KDD-2017-HsiehSD #distributed #kernel: Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines (CJH, SS, ISD), pp. 245–254.
KDD-2017-KobrenMKM #algorithm #clustering: A Hierarchical Algorithm for Extreme Clustering (AK, NM, AK, AM), pp. 255–264.
KDD-2017-KuangCLJY: Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing (KK, PC0, BL0, MJ0, SY), pp. 265–274.
KDD-2017-LakkarajuKLLM #algorithm #predict #problem: The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables (HL, JMK, JL, JL, SM), pp. 275–284.
KDD-2017-0013H #learning #paradigm #predict: Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics (XL0, JH), pp. 285–294.
KDD-2017-LiTWSCB #question: Is the Whole Greater Than the Sum of Its Parts? (LL, HT, YW0, CS, NC, NB), pp. 295–304.
KDD-2017-LiS #collaboration #recommendation: Collaborative Variational Autoencoder for Recommender Systems (XL, JS), pp. 305–314.
KDD-2017-Li #fourier #kernel #normalisation #random: Linearized GMM Kernels and Normalized Random Fourier Features (PL0), pp. 315–324.
KDD-2017-LianLG00C #matrix: Discrete Content-aware Matrix Factorization (DL, RL, YG, KZ0, XX0, LC), pp. 325–334.
KDD-2017-LiuFMRSX #analysis #effectiveness #internet #process #realtime: Effective and Real-time In-App Activity Analysis in Encrypted Internet Traffic Streams (JL, YF, JM, YR, LS, HX), pp. 335–344.
KDD-2017-LuoZQYYWYW #functional #learning #multi: Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning (TL, WZ, SQ, YY, DY, GW, JY, JW0), pp. 345–354.
KDD-2017-MandrosBV #approximate #dependence #functional #reliability: Discovering Reliable Approximate Functional Dependencies (PM, MB, JV), pp. 355–363.
KDD-2017-MautzYPB #towards: Towards an Optimal Subspace for K-Means (DM, WY0, CP, CB), pp. 365–373.
KDD-2017-PerrosPWVSTS #named #scalability: SPARTan: Scalable PARAFAC2 for Large & Sparse Data (IP, EEP, FW0, RWV, ES, MT, JS), pp. 375–384.
KDD-2017-RibeiroSF #learning #named: struc2vec: Learning Node Representations from Structural Identity (LFRR, PHPS, DRF), pp. 385–394.
KDD-2017-SatheA #similarity: Similarity Forests (SS, CCA), pp. 395–403.
KDD-2017-ShahSC #algorithm #constraints #online #ranking #web: Online Ranking with Constraints: A Primal-Dual Algorithm and Applications to Web Traffic-Shaping (PS, AS, TC), pp. 405–414.
KDD-2017-ShenHYSLC #network #on the #online #social: On Finding Socially Tenuous Groups for Online Social Networks (CYS, LHH, DNY, HHS, WCL, MSC), pp. 415–424.
KDD-2017-ShiCZG0 #named #network #probability: PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks (YS, PWC, HZ, HG, JH0), pp. 425–434.
KDD-2017-SongHGCH #multi #streaming: Multi-Aspect Streaming Tensor Completion (QS, XH, HG, JC, XH), pp. 435–443.
KDD-2017-SpringS #learning #random #scalability: Scalable and Sustainable Deep Learning via Randomized Hashing (RS, AS), pp. 445–454.
KDD-2017-Tagami #approximate #classification #multi #named #nearest neighbour: AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification (YT), pp. 455–464.
KDD-2017-TolomeiSHL #predict: Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (GT, FS, AH, ML), pp. 465–474.
KDD-2017-WangHCLYR #mining #network: Structural Deep Brain Network Mining (SW, LH0, BC, CTL, PSY, ABR), pp. 475–484.
KDD-2017-WangAL #kernel #performance #random #re-engineering: Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods (SW, CCA, HL0), pp. 485–494.
KDD-2017-WangFLHA #detection #process: Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes (PW, YF, GL, WH, CCA), pp. 495–503.
KDD-2017-0001YXWY #approximate #effectiveness #named #personalisation #rank: FORA: Simple and Effective Approximate Single-Source Personalized PageRank (SW0, RY, XX, ZW, YY), pp. 505–514.
KDD-2017-WuHS #collaboration #ranking #scalability: Large-scale Collaborative Ranking in Near-Linear Time (LW, CJH, JS), pp. 515–524.
KDD-2017-Xu0TTL #distance #higher-order #named #optimisation #rating #recommendation: HoORaYs: High-order Optimization of Rating Distance for Recommender Systems (JX0, YY0, HT, XT, JL0), pp. 525–534.
KDD-2017-XunLGZ #coordination #topic #word: Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts (GX, YL, JG0, AZ), pp. 535–543.
KDD-2017-YenHDRDX #classification #named #parallel: PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification (IEHY, XH, WD0, PR, ISD, EPX), pp. 545–553.
KDD-2017-YinBLG #clustering #graph #higher-order: Local Higher-Order Graph Clustering (HY, ARB, JL, DFG), pp. 555–564.
KDD-2017-ZangCF0 #memory management #modelling #process #social: Long Short Memory Process: Modeling Growth Dynamics of Microscopic Social Connectivity (CZ, PC0, CF, WZ0), pp. 565–574.
KDD-2017-ZhangC #predict: Weisfeiler-Lehman Neural Machine for Link Prediction (MZ, YC), pp. 575–583.
KDD-2017-Zhang0 #named #performance #similarity: EmbedJoin: Efficient Edit Similarity Joins via Embeddings (HZ, QZ0), pp. 585–594.
KDD-2017-ZhangLLYZH0 #detection #named #online #twitter: TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams (CZ0, LL, DL, QY0, HZ, TH, JH0), pp. 595–604.
KDD-2017-ZhangWLTL #clustering #graph #heuristic: Graph Edge Partitioning via Neighborhood Heuristic (CZ, FW, QL, ZGT, ZL), pp. 605–614.
KDD-2017-ZhangLZXXY #matrix #sketching: Randomization or Condensation?: Linear-Cost Matrix Sketching Via Cascaded Compression Sampling (KZ0, CL, JZ0, HX, EPX, JY), pp. 615–623.
KDD-2017-ZhaoZGLCLW: Tracking the Dynamics in Crowdfunding (HZ, HZ, YG, QL0, EC, HL, LW), pp. 625–634.
KDD-2017-ZhaoYLSL #network #recommendation: Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks (HZ, QY, JL, YS, DLL), pp. 635–644.
KDD-2017-ZhengP #kernel: Coresets for Kernel Regression (YZ, JMP), pp. 645–654.
KDD-2017-ZhouZYATDH #algorithm #graph: A Local Algorithm for Structure-Preserving Graph Cut (DZ, SZ, MYY, SA, HT, HD, JH), pp. 655–664.
KDD-2017-ZhouP #detection #robust: Anomaly Detection with Robust Deep Autoencoders (CZ, RCP), pp. 665–674.
KDD-2017-AgarwalBSJ #effectiveness #evaluation #feedback #multi #using: Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers (AA, SB0, TS, TJ), pp. 687–696.
KDD-2017-AgrawalAKHLCK #named: Tripoles: A New Class of Relationships in Time Series Data (SA, GA, AK, WH, SL, SC, VK), pp. 697–706.
KDD-2017-Antikacioglu0 #recommendation: Post Processing Recommender Systems for Diversity (AA, RR0), pp. 707–716.
KDD-2017-Bauman0T #aspect-oriented #recommendation: Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews (KB, BL0, AT), pp. 717–725.
KDD-2017-BlalockG #data mining #mining #named #performance: Bolt: Accelerated Data Mining with Fast Vector Compression (DWB, JVG), pp. 727–735.
KDD-2017-BojchevskiMG #clustering #modelling #robust #semistructured data: Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings (AB, YM, SG), pp. 737–746.
KDD-2017-CaoZZYPZARL #detection #mobile #modelling #named #type system: DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection (BC, LZ, CZ, PSY, AP, JZ, OA, KR, ADL), pp. 747–755.
KDD-2017-ChenG #optimisation #performance: Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization (JC, QG), pp. 757–766.
KDD-2017-ChenSSH #collaboration #on the: On Sampling Strategies for Neural Network-based Collaborative Filtering (TC0, YS, YS, LH), pp. 767–776.
KDD-2017-ChengLL #feature model #network #social: Unsupervised Feature Selection in Signed Social Networks (KC, JL, HL0), pp. 777–786.
KDD-2017-ChoiBSSS #graph #learning #named #representation: GRAM: Graph-based Attention Model for Healthcare Representation Learning (EC, MTB, LS, WFS, JS), pp. 787–795.
KDD-2017-Corbett-DaviesP #algorithm #cost analysis: Algorithmic Decision Making and the Cost of Fairness (SCD, EP, AF, SG, AH), pp. 797–806.
KDD-2017-DongJXC #case study #network: Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks (YD, RAJ, JX, NVC), pp. 807–816.
KDD-2017-EikmeierG #network: Revisiting Power-law Distributions in Spectra of Real World Networks (NE, DFG), pp. 817–826.
KDD-2017-FuA0TX #automation #detection #interactive #named: REMIX: Automated Exploration for Interactive Outlier Detection (YF, CCA, SP0, DST, HX), pp. 827–835.
KDD-2017-GabelKS #approximate #distributed: Anarchists, Unite: Practical Entropy Approximation for Distributed Streams (MG, DK, AS), pp. 837–846.
KDD-2017-HosseiniAKAFZR #recommendation: Recurrent Poisson Factorization for Temporal Recommendation (SAH, KA, AK, AA, MF, HZ, HRR), pp. 847–855.
KDD-2017-HuangZ #analysis #named: SPOT: Sparse Optimal Transformations for High Dimensional Variable Selection and Exploratory Regression Analysis (QH, MZ), pp. 857–865.
KDD-2017-JiaKNGCWK #incremental #predict: Incremental Dual-memory LSTM in Land Cover Prediction (XJ, AK, GN, JG, KC, PCW, VK), pp. 867–876.
KDD-2017-JiangSCRKH0 #corpus #named: MetaPAD: Meta Pattern Discovery from Massive Text Corpora (MJ0, JS, TC, XR, LMK, TPH, JH0), pp. 877–886.
KDD-2017-KimSYJ: Federated Tensor Factorization for Computational Phenotyping (YK, JS, HY, XJ), pp. 887–895.
KDD-2017-KomiyamaIANM #mining #multi #statistics #testing: Statistical Emerging Pattern Mining with Multiple Testing Correction (JK, MI, HA, TN, SiM), pp. 897–906.
KDD-2017-LabutovHBH #learning #mining: Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites (IL, YH0, PB, DH), pp. 907–915.
KDD-2017-LiGZXZ #analysis #development #perspective: Prospecting the Career Development of Talents: A Survival Analysis Perspective (HL, YG, HZ, HX, HZ), pp. 917–925.
KDD-2017-LiMGK #interactive #network: A Context-aware Attention Network for Interactive Question Answering (HL, MRM, YG, AK), pp. 927–935.
KDD-2017-LiuPH #distributed #learning #multi: Distributed Multi-Task Relationship Learning (SL, SJP, QH), pp. 937–946.
KDD-2017-LiuLLTZX #modelling: Point-of-Interest Demand Modeling with Human Mobility Patterns (YL, CL, XL, MT, HZ, HX), pp. 947–955.
KDD-2017-LiuSLMLX #functional #predict: Functional Zone Based Hierarchical Demand Prediction For Bike System Expansion (JL, LS, QL, JM, YL, HX), pp. 957–966.
KDD-2017-MaMXLGSZ #data flow #semistructured data: Unsupervised Discovery of Drug Side-Effects from Heterogeneous Data Sources (FM, CM, HX, QL0, JG0, LS, AZ), pp. 967–976.
KDD-2017-MaurusP #detection #using: Let's See Your Digits: Anomalous-State Detection using Benford's Law (SM, CP), pp. 977–986.
KDD-2017-QiTWL #process: Mixture Factorized Ornstein-Uhlenbeck Processes for Time-Series Forecasting (GJQ, JT, JW, JL), pp. 987–995.
KDD-2017-QuR0 #automation #knowledge base: Automatic Synonym Discovery with Knowledge Bases (MQ, XR, JH0), pp. 997–1005.
KDD-2017-RaffN #distance #scalability #sequence: An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance (ER, CKN), pp. 1007–1015.
KDD-2017-RozenshteinTG #approach #social: Inferring the Strength of Social Ties: A Community-Driven Approach (PR, NT, AG), pp. 1017–1025.
KDD-2017-SaveskiPSDGXA #detection #network #random: Detecting Network Effects: Randomizing Over Randomized Experiments (MS, JPA, GSJ, WD, SG, YX, EMA), pp. 1027–1035.
KDD-2017-Scholtes #multi #network #visual notation: When is a Network a Network?: Multi-Order Graphical Model Selection in Pathways and Temporal Networks (IS), pp. 1037–1046.
KDD-2017-ShenHGC #comprehension #learning #named: ReasoNet: Learning to Stop Reading in Machine Comprehension (YS, PSH, JG, WC), pp. 1047–1055.
KDD-2017-ShinHKF #detection #incremental #named: DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams (KS, BH, JK, CF), pp. 1057–1066.
KDD-2017-SifferFTL #detection: Anomaly Detection in Streams with Extreme Value Theory (AS, PAF, AT, CL), pp. 1067–1075.
KDD-2017-SinghSGMC #evolution #modelling #network: Relay-Linking Models for Prominence and Obsolescence in Evolving Networks (MS0, RS, PG, AM0, SC), pp. 1077–1086.
KDD-2017-Song0H #clustering #named #parallel #performance: PAMAE: Parallel k-Medoids Clustering with High Accuracy and Efficiency (HS, JGL0, WSH), pp. 1087–1096.
KDD-2017-AmandH #composition #learning #metric: Sparse Compositional Local Metric Learning (JSA, JH), pp. 1097–1104.
KDD-2017-TangW0M #learning: End-to-end Learning for Short Text Expansion (JT, YW, KZ0, QM), pp. 1105–1113.
KDD-2017-TillmanMBG #graph: Construction of Directed 2K Graphs (BT, AM, CTB, MG), pp. 1115–1124.
KDD-2017-UstunR: Optimized Risk Scores (BU, CR), pp. 1125–1134.
KDD-2017-WangFWYDX #recommendation #sentiment: A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users (HW0, YF, QW, HY, CD, HX), pp. 1135–1143.
KDD-2017-WangGZOXLG #detection #network: Adversary Resistant Deep Neural Networks with an Application to Malware Detection (QW, WG, KZ, AGOI, XX, XL, CLG), pp. 1145–1153.
KDD-2017-WangSZTJZ #matrix #modelling #multi: Multi-Modality Disease Modeling via Collective Deep Matrix Factorization (QW, MS, LZ, PT0, SJ, JZ), pp. 1155–1164.
KDD-2017-WuSY #modelling #normalisation: Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models (TW, SS, KY), pp. 1165–1174.
KDD-2017-WuAL #detection: Structural Event Detection from Log Messages (FW0, PA, ZL), pp. 1175–1184.
KDD-2017-WuG #higher-order #markov #process: Retrospective Higher-Order Markov Processes for User Trails (TW, DFG), pp. 1185–1194.
KDD-2017-XieBLZ #distributed #learning #multi #privacy: Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates (LX, IMB, KL, JZ), pp. 1195–1204.
KDD-2017-XingHC #representation #statistics: Evaluating U.S. Electoral Representation with a Joint Statistical Model of Congressional Roll-Calls, Legislative Text, and Voter Registration Data (ZX, SH, LC), pp. 1205–1214.
KDD-2017-YamadaLGCWKKMC #predict: Convex Factorization Machine for Toxicogenomics Prediction (MY, WL, AG, JC, KW, SAK, SK, HM, YC), pp. 1215–1224.
KDD-2017-YanCKR #big data #detection #distributed: Distributed Local Outlier Detection in Big Data (YY, LC, CK, EAR), pp. 1225–1234.
KDD-2017-YanCR #detection #scalability: Scalable Top-n Local Outlier Detection (YY, LC, EAR), pp. 1235–1244.
KDD-2017-YangBZY0 #approach #collaboration #learning #recommendation: Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (CY, LB, CZ0, QY0, JH0), pp. 1245–1254.
KDD-2017-YangTH #multi: Multi-task Function-on-function Regression with Co-grouping Structured Sparsity (PY, QT, JH), pp. 1255–1264.
KDD-2017-YeZMPB #learning #network: Learning from Labeled and Unlabeled Vertices in Networks (WY0, LZ, DM, CP, CB), pp. 1265–1274.
KDD-2017-YinLN #bound #education #scalability: Small Batch or Large Batch?: Gaussian Walk with Rebound Can Teach (PY, PL0, TN), pp. 1275–1284.
KDD-2017-YouX0T #education #learning #multi #network: Learning from Multiple Teacher Networks (SY, CX0, CX0, DT), pp. 1285–1294.
KDD-2017-YuCSZY #behaviour #framework #social: A Temporally Heterogeneous Survival Framework with Application to Social Behavior Dynamics (LY, PC0, CS, TZ, SY), pp. 1295–1304.
KDD-2017-ZhanZ #induction #learning #multi: Inductive Semi-supervised Multi-Label Learning with Co-Training (WZ, MLZ), pp. 1305–1314.
KDD-2017-ZhangCTSS #effectiveness #learning #multi #named: LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity (YZ, RC, JT0, WFS, JS), pp. 1315–1324.
KDD-2017-ZhangWP #graph #visualisation: Visualizing Attributed Graphs via Terrain Metaphor (YZ, YW, SP0), pp. 1325–1334.
KDD-2017-ZhangWW: Achieving Non-Discrimination in Data Release (LZ0, YW, XW), pp. 1335–1344.
KDD-2017-AlbertKG #identification #network #scalability #using: Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale (AA, JK, MCG), pp. 1357–1366.
KDD-2017-AokiAM #predict: Luck is Hard to Beat: The Difficulty of Sports Prediction (RYSA, RMA, POSVdM), pp. 1367–1376.
KDD-2017-BaoHRLZ: Planning Bike Lanes based on Sharing-Bikes' Trajectories (JB0, TH, SR, YL, YZ0), pp. 1377–1386.
KDD-2017-BaylorBCFFHHIJK #framework #machine learning #named #platform: TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (DB, EB, HTC, NF, CYF, ZH, SH, MI, VJ, LK0, CYK, LL, CM, ANM, NP, SR, SR0, SEW, MW, JW, XZ, MZ), pp. 1387–1395.
KDD-2017-BorisyukZK #named #performance #towards: LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace (FB, LZ, KK), pp. 1397–1406.
KDD-2017-ChojnackiDFSWZA #approach #comprehension: A Data Science Approach to Understanding Residential Water Contamination in Flint (AC, CD, AF, GS, JW, DTZ, JDA, EMS), pp. 1407–1416.
KDD-2017-CurtisG #estimation #scalability: Estimation of Recent Ancestral Origins of Individuals on a Large Scale (REC, ARG), pp. 1417–1425.
KDD-2017-DmitrievGKV #metric #online: A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments (PAD, SG, DWK, GJV), pp. 1427–1436.
KDD-2017-DongMSW: A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations (YD, HM, ZS, KW), pp. 1437–1446.
KDD-2017-DuZCT #interactive #named #performance: FIRST: Fast Interactive Attributed Subgraph Matching (BD, SZ, NC, HT), pp. 1447–1456.
KDD-2017-EmraniMX #learning #multi #using: Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task Learning (SE, AM, WX), pp. 1457–1466.
KDD-2017-GanH #data mining #framework #mining #scalability: A Data Mining Framework for Valuing Large Portfolios of Variable Annuities (GG, JXH), pp. 1467–1475.
KDD-2017-GhoshCLMCBMR #automation #named #open source: GELL: Automatic Extraction of Epidemiological Line Lists from Open Sources (SG, PC, BLL, MSM, EC, JSB, MVM, NR), pp. 1477–1485.
KDD-2017-GolovinSMKKS #black box #optimisation: Google Vizier: A Service for Black-Box Optimization (DG, BS, SM, GK, JK, DS), pp. 1487–1495.
KDD-2017-GongNSG #health #predict: Predicting Clinical Outcomes Across Changing Electronic Health Record Systems (JJG, TN, PS, JVG), pp. 1497–1505.
KDD-2017-HouYSA #android #detection #named #network: HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network (SH, YY, YS, MA), pp. 1507–1515.
KDD-2017-JohariKPW #matter #testing #what #why: Peeking at A/B Tests: Why it matters, and what to do about it (RJ, PK, LP, DW0), pp. 1517–1525.
KDD-2017-KoutraDBWIFB #design #named #performance: PNP: Fast Path Ensemble Method for Movie Design (DK, AD, SB, UW, SI, CF, JB), pp. 1527–1536.
KDD-2017-KuangPCMP #scalability: Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data (ZK, PLP, VSC, RM, DP), pp. 1537–1546.
KDD-2017-LiJZXLZZWZWXB #analysis #framework #named #platform: FLAP: An End-to-End Event Log Analysis Platform for System Management (TL0, YJ, CZ, BX0, ZL0, WZ, XZ, WW0, LZ, JW, LX, DB), pp. 1547–1556.
KDD-2017-LiuXOS #e-commerce #ranking: Cascade Ranking for Operational E-commerce Search (SL, FX, WO, LS), pp. 1557–1565.
KDD-2017-McNamaraVY #feature model #framework #multimodal: Developing a Comprehensive Framework for Multimodal Feature Extraction (QM, AdlV, TY), pp. 1567–1574.
KDD-2017-MottiniA #network #pointer #predict #using: Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction (AM, RAA), pp. 1575–1583.
KDD-2017-PaulLTYF #analysis #exclamation #named #sentiment #twitter #what: Compass: Spatio Temporal Sentiment Analysis of US Election What Twitter Says! (DP, FL0, MKT, XY, RF), pp. 1585–1594.
KDD-2017-PortnoffHDAM: Backpage and Bitcoin: Uncovering Human Traffickers (RSP, DYH, PD, SA, DM), pp. 1595–1604.
KDD-2017-PowerRWL: Not All Passes Are Created Equal: Objectively Measuring the Risk and Reward of Passes in Soccer from Tracking Data (PP, HR, XW, PL), pp. 1605–1613.
KDD-2017-QinKWRC #multi #named: MARAS: Signaling Multi-Drug Adverse Reactions (XQ0, TK, SW, EAR, LC), pp. 1615–1623.
KDD-2017-ShahYARSC: A Practical Exploration System for Search Advertising (PS, MY, SA, AR, BS, RC), pp. 1625–1631.
KDD-2017-SybrandtSS #automation #generative #named: MOLIERE: Automatic Biomedical Hypothesis Generation System (JS, MS, IS), pp. 1633–1642.
KDD-2017-TataPNCGGMSGMK #experience: Quick Access: Building a Smart Experience for Google Drive (ST, AP, MN, MC, JG, AG, AM, MS0, DG, CM, RK), pp. 1643–1651.
KDD-2017-TongCZCWYYL #approach #online #platform #predict #scalability: The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms (YT, YC, ZZ, LC0, JW0, QY0, JY, WL), pp. 1653–1662.
KDD-2017-VandalKGMNG #generative #image #named: DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution (TV, EK, SG, ARM, RRN, ARG), pp. 1663–1672.
KDD-2017-WangCWX #safety: No Longer Sleeping with a Bomb: A Duet System for Protecting Urban Safety from Dangerous Goods (JW, CC, JW, ZX), pp. 1673–1681.
KDD-2017-ZhangL #platform: A Quasi-experimental Estimate of the Impact of P2P Transportation Platforms on Urban Consumer Patterns (ZZ, BL), pp. 1683–1692.
KDD-2017-ZhouLZCLYCYCDQ #distributed #learning #named #parametricity: KunPeng: Parameter Server based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial (JZ, XL, PZ, CC, LL, XY, QC, JY, XC, YD, Y(Q), pp. 1693–1702.
KDD-2017-ZhuSMYRZ: Deep Embedding Forest: Forest-based Serving with Deep Embedding Features (JZ0, YS, JCM, DY, HR, YZ), pp. 1703–1711.
KDD-2017-AhmedLSW #algorithm #industrial #modelling #problem #topic: A Practical Algorithm for Solving the Incoherence Problem of Topic Models In Industrial Applications (AA, JL, DS, YW), pp. 1713–1721.
KDD-2017-AndersonM #classification #machine learning: Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity (BA, DAM), pp. 1723–1732.
KDD-2017-BifetZFHZQHP #data type #evolution #mining #performance: Extremely Fast Decision Tree Mining for Evolving Data Streams (AB, JZ, WF0, CH, JZ, JQ, GH0, BP), pp. 1733–1742.
KDD-2017-ChahuaraGJR #optimisation #realtime #web: Real-Time Optimization of Web Publisher RTB Revenues (PC, NG, GJ, JMR), pp. 1743–1751.
KDD-2017-ChamberlainCLPD #predict #using: Customer Lifetime Value Prediction Using Embeddings (BPC, ÂC, CHBL, RP, MPD), pp. 1753–1762.
KDD-2017-ChengHHIMPRSSST #flexibility #framework #machine learning: TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks (HTC, ZH, LH, MI, CM, IP, GR, DS, JS, DS, YT, PT, MW, CX, JX), pp. 1763–1771.
KDD-2017-DadkhahiM #detection #embedded #learning #network: Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices (HD, BMM), pp. 1773–1781.
KDD-2017-DebGIPVYY #automation #learning #named #network #policy #predict: AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments (SD, ZG, SI, SCP, SV, HY, JY), pp. 1783–1792.
KDD-2017-GhoshDPYG #automation #categorisation #ecosystem: Automated Categorization of Onion Sites for Analyzing the Darkweb Ecosystem (SG, AD, PAP, VY, AG), pp. 1793–1802.
KDD-2017-HassanALT #automation #detection #towards: Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster (NH, FA, CL, MT), pp. 1803–1812.
KDD-2017-HillNLIV #algorithm #multi #optimisation #performance #realtime: An Efficient Bandit Algorithm for Realtime Multivariate Optimization (DNH, HN, YL0, AI, SVNV), pp. 1813–1821.
KDD-2017-IosifidisN #learning #scalability #sentiment: Large Scale Sentiment Learning with Limited Labels (VI, EN), pp. 1823–1832.
KDD-2017-ItoF #optimisation #predict: Optimization Beyond Prediction: Prescriptive Price Optimization (SI, RF), pp. 1833–1841.
KDD-2017-JanakiramanMO: Finding Precursors to Anomalous Drop in Airspeed During a Flight's Takeoff (VMJ, BLM, NCO), pp. 1843–1852.
KDD-2017-KittsKYZBPTTJ #multi: Ad Serving with Multiple KPIs (BK, MK, IY, YZ, GB, AP, ST, ET, SRJ), pp. 1853–1861.
KDD-2017-LiCCC: Discovering Pollution Sources and Propagation Patterns in Urban Area (XL, YC, GC, LC), pp. 1863–1872.
KDD-2017-LiHG #concept #enterprise #spreadsheet #using: Discovering Enterprise Concepts Using Spreadsheet Tables (KL, YH, KG), pp. 1873–1882.
KDD-2017-LiuJDJ #clustering #normalisation: Supporting Employer Name Normalization at both Entity and Cluster Level (QL, FJ, VSD, AJ), pp. 1883–1892.
KDD-2017-LouO #named #performance: BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency (YL, MO), pp. 1893–1901.
KDD-2017-MaCZYSG #bidirectional #named #network #predict: Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks (FM, RC, JZ, QY, TS, JG0), pp. 1903–1911.
KDD-2017-MalloyBAKJ #graph #internet: Internet Device Graphs (MM, PB, ECA, JK, AJ), pp. 1913–1921.
KDD-2017-ManzoorA #exclamation: RUSH!: Targeted Time-limited Coupons via Purchase Forecasts (EAM, LA), pp. 1923–1931.
KDD-2017-OkuraTOT #recommendation: Embedding-based News Recommendation for Millions of Users (SO, YT, SO, AT), pp. 1933–1942.
KDD-2017-OvadiaHKLNPZS #learning: Learning to Count Mosquitoes for the Sterile Insect Technique (YO, YH, DK, JL, DN, RP, TZ, DS), pp. 1943–1949.
KDD-2017-PanZLCHHZ #network #scalability: An Intelligent Customer Care Assistant System for Large-Scale Cellular Network Diagnosis (LP, JZ, PPCL, HC, CH, CH, KZ), pp. 1951–1959.
KDD-2017-PanBHGP #design: Deep Design: Product Aesthetics for Heterogeneous Markets (YP, AB, JH, RG, PYP), pp. 1961–1970.
KDD-2017-QuiselFSK #data type: Collecting and Analyzing Millions of mHealth Data Streams (TQ, LF, AS, DCK), pp. 1971–1980.
KDD-2017-RistovskiGHT #integration #machine learning #optimisation #simulation: Dispatch with Confidence: Integration of Machine Learning, Optimization and Simulation for Open Pit Mines (KR, CG0, KH, HKT), pp. 1981–1989.
KDD-2017-RuizPWL #performance #quote #tool support: “The Leicester City Fairytale?”: Utilizing New Soccer Analytics Tools to Compare Performance in the 15/16 & 16/17 EPL Seasons (HR, PP, XW, PL), pp. 1991–2000.
KDD-2017-SalehianHL #approach #crowdsourcing #machine learning #scalability: Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach (HS, PDH, CL), pp. 2001–2009.
KDD-2017-SharmaSKS #machine learning #problem: The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue (AS, VS, VK, LS), pp. 2011–2019.
KDD-2017-SoskaGRC #automation #identification: Automatic Application Identification from Billions of Files (KS, CSG, KAR, NC), pp. 2021–2030.
KDD-2017-TongKIYKSV #learning #multi: Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention (BT, MK, MI, TY, YK, AS, RV), pp. 2031–2040.
KDD-2017-UesakaMSKMAY #learning #multi #visual notation: Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes (TU, KM, HS, TK, HM, RA, KY), pp. 2041–2050.
KDD-2017-WangYRTZYW #editing #learning #recommendation: Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration (XW, LY, KR, GT, WZ0, YY0, JW0), pp. 2051–2059.
KDD-2017-WangJY #framework #hybrid #modelling: A Hybrid Framework for Text Modeling with Convolutional RNN (CW, FJ, HY), pp. 2061–2069.
KDD-2017-WoodsAMM #feedback #modelling #predict #using: Formative Essay Feedback Using Predictive Scoring Models (BW, DA, SM, EM), pp. 2071–2080.
KDD-2017-XiaoGVT #behaviour #learning: Learning Temporal State of Diabetes Patients via Combining Behavioral and Demographic Data (HX, JG0, LHV, DST), pp. 2081–2089.
KDD-2017-YangZH #algorithm #predict #towards: Local Algorithm for User Action Prediction Towards Display Ads (HY, YZ, JH), pp. 2091–2099.
KDD-2017-YangKBSWKP #visual notation: Visual Search at eBay (FY, AK, YB, LS, QW, MHK, RP), pp. 2101–2110.
KDD-2017-YangDSZFXBM #data-driven #framework #process #recommendation: A Data-driven Process Recommender Framework (SY, XD, LS, YZ, RAF, HX, RSB, IM), pp. 2111–2120.
KDD-2017-YilmazEF #predict: Predicting Optimal Facility Location without Customer Locations (EY, SE, HF), pp. 2121–2130.
KDD-2017-YinCZ #comprehension #design #named #network #sequence: DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks (ZY, KhC, RZ), pp. 2131–2139.
KDD-2017-ZhangAQ #multi #predict: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (LZ, CCA, GJQ), pp. 2141–2149.
KDD-2017-ZhangHMWZFGY #combinator #modelling #optimisation #order: A Taxi Order Dispatch Model based On Combinatorial Optimization (LZ, TH, YM, GW, JZ, PF, PG, JY), pp. 2151–2159.
KDD-2017-ZhengBLL #detection #learning #metric: Contextual Spatial Outlier Detection with Metric Learning (GZ, SLB, TL, ZL), pp. 2161–2170.
KDD-2017-ZhengGNOY #bias: Resolving the Bias in Electronic Medical Records (KZ, JG, KYN, BCO, JWLY), pp. 2171–2180.
KDD-2017-ZhouXBWZLXLSG #analysis #named: STAR: A System for Ticket Analysis and Resolution (WZ, WX, RB, QW, CZ, TL0, JX0, ZL0, LS, GYG), pp. 2181–2190.
KDD-2017-ZhuJTPZLG: Optimized Cost per Click in Taobao Display Advertising (HZ, JJ, CT, FP, YZ, HL, KG), pp. 2191–2200.

Proceedings of the 23rd International Conference on Knowledge Discovery and Data MiningKDD, 2017.

Contents (232 items)

Proceedings of the 23rd International Conference on Knowledge Discovery and Data Mining
KDD, 2017.