BibSLEIGH — Proceedings of the 24th International Conference on Knowledge Discovery and Data Mining

Yike Guo, Faisal Farooq
Proceedings of the 24th International Conference on Knowledge Discovery and Data Mining
KDD, 2018.

KER

@proceedings{KDD-2018,
	doi           = "10.1145/3219819",
	editor        = "Yike Guo and Faisal Farooq",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 24th International Conference on Knowledge Discovery and Data Mining}",
	year          = 2018,
}

Contents (310 items)

KDD-2018-Hand: Data Science for Financial Applications (DJH), p. 1.
KDD-2018-Roth #design: Market Design and Computerized Marketplaces (AER), p. 2.
KDD-2018-Teh #big data #learning #on the #problem: On Big Data Learning for Small Data Problems (YWT), p. 3.
KDD-2018-Wing: Data for Good: Abstract (JMW), p. 4.
KDD-2018-AbernethyCFSW #named #pipes and filters: ActiveRemediation: The Search for Lead Pipes in Flint, Michigan (JDA, AC, AF, EMS, JW), pp. 5–14.
KDD-2018-AckermannWUNRLB #framework #machine learning #modelling #policy: Deploying Machine Learning Models for Public Policy: A Framework (KA, JW, ADU, HN, ANR, SJL, JB, MD, CC, LH, RG), pp. 15–22.
KDD-2018-AgarwalBGXYZ #online #parametricity #problem #ranking: Online Parameter Selection for Web-based Ranking Problems (DA, KB0, SG, YX, YY, LZ), pp. 23–32.
KDD-2018-AyhanCS #predict: Predicting Estimated Time of Arrival for Commercial Flights (SA, PC, HS), pp. 33–42.
KDD-2018-BaiZEV #learning #representation: Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (TB, SZ, BLE, SV), pp. 43–51.
KDD-2018-BaiOZFRST #n-gram #query #scalability: Scalable Query N-Gram Embedding for Improving Matching and Relevance in Sponsored Search (XB0, EO, YZ, AF, AR, RS, AT), pp. 52–61.
KDD-2018-BhagatMLV #modelling #recommendation: Buy It Again: Modeling Repeat Purchase Recommendations (RB, SM, AL, SV), pp. 62–70.
KDD-2018-BorisyukGS #detection #image #named #recognition #scalability: Rosetta: Large Scale System for Text Detection and Recognition in Images (FB, AG, VS), pp. 71–79.
KDD-2018-CardosoDV #learning #personalisation #recommendation #semistructured data #towards: Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products (ÂC, FD, SV), pp. 80–89.
KDD-2018-ChenCL #dataset #open data: Rotation-blended CNNs on a New Open Dataset for Tropical Cyclone Image-to-intensity Regression (BC, BFC, HTL), pp. 90–99.
KDD-2018-ChenLZLZL #collaboration #distributed: Distributed Collaborative Hashing and Its Applications in Ant Financial (CC, ZL, PZ, LL, JZ, XL), pp. 100–109.
KDD-2018-ChenHNLLWX #multi #named: MIX: Multi-Channel Information Crossing for Text Matching (HC, FXH, DN, DL, KL, CW, YX), pp. 110–119.
KDD-2018-ChenLZK #graph #how: How LinkedIn Economic Graph Bonds Information and Product: Applications in LinkedIn Salary (XC, YL, LZ, KK), pp. 120–129.
KDD-2018-ChenCYY #optimisation #scalability: Scalable Optimization for Embedding Highly-Dynamic and Recency-Sensitive Data (XC, PC0, LY, SY), pp. 130–138.
KDD-2018-Christakopoulou #approach #interactive #recommendation #towards: Q&R: A Two-Stage Approach toward Interactive Recommendation (KC, AB, RL, SJ, EHC), pp. 139–148.
KDD-2018-ChungCHLE #behaviour #detection #visual notation: Detection of Apathy in Alzheimer Patients by Analysing Visual Scanning Behaviour with RNNs (JC, SAC, NH, KLL, ME), pp. 149–157.
KDD-2018-ComarelaDBCC #web: Assessing Candidate Preference through Web Browsing History (GC, RD, PB, DDC, MC), pp. 158–167.
KDD-2018-ConoverHBSS #named #performance: Pangloss: Fast Entity Linking in Noisy Text Environments (MDC, MH, SB, PS, SS), pp. 168–176.
KDD-2018-DabrowskiRGAM #modelling #quality: State Space Models for Forecasting Water Quality Variables: An Application in Aquaculture Prawn Farming (JJD, AR, AG, SA, JM), pp. 177–185.
KDD-2018-DaltayanniDA #automation #segmentation #using: Automated Audience Segmentation Using Reputation Signals (MD, AD, LdA), pp. 186–195.
KDD-2018-DasSCHLCKC #learning #named #performance #using: SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression (ND, MS, STC, FH, SL, LC, MEK, DHC), pp. 196–204.
KDD-2018-DavoudiAZE #adaptation: Adaptive Paywall Mechanism for Digital News Media (HD, AA, MZ, GE), pp. 205–214.
KDD-2018-RouxPMVF #approach #detection #machine learning #using: Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach (DdR, BP, AM, MDPV, CF), pp. 215–222.
KDD-2018-DecroosHD #automation: Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data (TD, JVH, JD), pp. 223–232.
KDD-2018-DengKL #metric #novel: Applying the Delta Method in Metric Analytics: A Practical Guide with Novel Ideas (AD, UK, JL), pp. 233–242.
KDD-2018-DietheHKPSSTTF #lessons learnt: Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project (TD, MH, MK, MPN, KS, HS, ET, NT, PAF), pp. 243–252.
KDD-2018-FanHZYA #detection #exclamation: Gotcha - Sly Malware!: Scorpion A Metagraph2vec Based Malware Detection System (YF, SH, YZ, YY, MA), pp. 253–262.
KDD-2018-FirmaniMMN #information management #towards: Towards Knowledge Discovery from the Vatican Secret Archives. In Codice Ratio - Episode 1: Machine Transcription of the Manuscripts (DF, MM, PM, EN), pp. 263–272.
KDD-2018-FunkhouserMAPB: Device Graphing by Example (KF, MM, ECA, PP, PB), pp. 273–282.
KDD-2018-GaoGYSTXWYRMC #optimisation #realtime: Near Real-time Optimization of Activity-based Notifications (YG, VG, JY, CS, ZT, PJX, CW, SY, RR, AM, SC), pp. 283–292.
KDD-2018-GittensRWMGPKRM #data analysis #library #scalability #using: Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist (AG, KR, SW, MWM, LG, P, JK, MFR, KJM), pp. 293–301.
KDD-2018-GohSVH #learning #predict #rule-based #using: Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (GBG, CS, AV, NOH), pp. 302–310.
KDD-2018-GrbovicC #personalisation #ranking #realtime #using: Real-time Personalization using Embeddings for Search Ranking at Airbnb (MG, HC), pp. 311–320.
KDD-2018-HangPN #behaviour #predict #student: Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction (MH, IP, JN), pp. 321–330.
KDD-2018-HarelR #network #prototype #using: Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks (SH, KR), pp. 331–339.
KDD-2018-He0LRLTZ #detection #using: Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories (TH, JB0, RL, SR, YL, CT, YZ0), pp. 340–349.
KDD-2018-HuF #analysis #multimodal #sentiment: Multimodal Sentiment Analysis To Explore the Structure of Emotions (AH, SRF), pp. 350–358.
KDD-2018-HuWYKHCHWMS #visual notation: Web-Scale Responsive Visual Search at Bing (HH, YW, LY, PK, LH, X(C, JH, YW, MM, AS), pp. 359–367.
KDD-2018-HuDZ0X #analysis #e-commerce #formal method #learning #rank: Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application (YH, QD, AZ, YY0, YX), pp. 368–377.
KDD-2018-HulotAJ #effectiveness #predict #towards: Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems (PH, DA, SDJ), pp. 378–386.
KDD-2018-HundmanCLCS #detection #parametricity #using: Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding (KH, VC, CL, IC, TS), pp. 387–395.
KDD-2018-IyengarLISW #approach #data-driven #energy #named #performance: WattHome: A Data-driven Approach for Energy Efficiency Analytics at City-scale (SI, SL, DEI, PJS, BW), pp. 396–405.
KDD-2018-Janakiraman #learning #multi #safety #using: Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning (VMJ), pp. 406–415.
KDD-2018-JauvionG #realtime: Optimal Allocation of Real-Time-Bidding and Direct Campaigns (GJ, NG), pp. 416–424.
KDD-2018-JauvionGSGG #optimisation #using: Optimization of a SSP's Header Bidding Strategy using Thompson Sampling (GJ, NG, PDS, AG, SG), pp. 425–432.
KDD-2018-KhuranaASV #using: Resolving Abstract Anaphora Implicitly in Conversational Assistants using a Hierarchically stacked RNN (PK, PA, GMS, LV), pp. 433–442.
KDD-2018-KochGGWGX #framework #named #optimisation: Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning (PK, OG, SG, BW, JG, YX), pp. 443–452.
KDD-2018-Kokkodis #online #recommendation: Dynamic Recommendations for Sequential Hiring Decisions in Online Labor Markets (MK), pp. 453–461.
KDD-2018-KoptelovZBBC #identification #named: PrePeP: A Tool for the Identification and Characterization of Pan Assay Interference Compounds (MK, AZ, PB, RB, BC), pp. 462–471.
KDD-2018-KumarRBVWKEFMZG #machine learning #using: Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks (AK, SAAR, BB, RAV, KHW, CK, SE, AF, AM, JZ, RG), pp. 472–480.
KDD-2018-LeeAVN #collaboration #comprehension #learning #metric #video: Collaborative Deep Metric Learning for Video Understanding (JL, SAEH, BV, AN), pp. 481–490.
KDD-2018-LeeS #bias #estimation #online: Winner's Curse: Bias Estimation for Total Effects of Features in Online Controlled Experiments (MRL, MS), pp. 491–499.
KDD-2018-LeeGZ #generative #network #query: Rare Query Expansion Through Generative Adversarial Networks in Search Advertising (MCL, BG, RZ), pp. 500–508.
KDD-2018-LiRMLKC #named #predict: TATC: Predicting Alzheimer's Disease with Actigraphy Data (JL, YR, HM, ZL0, TK, HC), pp. 509–518.
KDD-2018-LiHZ #graph #predict: E-tail Product Return Prediction via Hypergraph-based Local Graph Cut (JL, JH, YZ), pp. 519–527.
KDD-2018-LiYCYZ #3d #algorithm #constraints #data-driven #delivery #problem: A Data-Driven Three-Layer Algorithm for Split Delivery Vehicle Routing Problem with 3D Container Loading Constraint (XL, MY, DC, JY, JZ), pp. 528–536.
KDD-2018-LiaoZWMCYGW #learning #predict #sequence: Deep Sequence Learning with Auxiliary Information for Traffic Prediction (BL, JZ, CW0, DM, TC, SY, YG, FW), pp. 537–546.
KDD-2018-LinKLZSXZQZ #big data #identification #interactive #multi #named: BigIN4: Instant, Interactive Insight Identification for Multi-Dimensional Big Data (QL, WK, JGL, HZ0, KS, YX, ZZ, BQ, DZ), pp. 547–555.
KDD-2018-LiuCMCMJJ #lessons learnt #normalisation #online #scalability: Lessons Learned from Developing and Deploying a Large-Scale Employer Name Normalization System for Online Recruitment (QL, JC, TM, AC, CM, FJ, VJ), pp. 556–565.
KDD-2018-LiuSZ #detection: Where Will Dockless Shared Bikes be Stacked?: - Parking Hotspots Detection in a New City (ZL, YS, YZ), pp. 566–575.
KDD-2018-MiloS #data analysis #interactive #platform: Next-Step Suggestions for Modern Interactive Data Analysis Platforms (TM, AS), pp. 576–585.
KDD-2018-MolinoZW #named #network #ranking: COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks (PM, HZ, YCW), pp. 586–595.
KDD-2018-NiOLLOZS #e-commerce #learning #multi: Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks (YN, DO, SL, XL, WO, AZ, LS), pp. 596–605.
KDD-2018-BeeckMSVD #machine learning #predict: Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion (TODB, WM, KS0, BV, JD), pp. 606–615.
KDD-2018-OshriHACDWBLE #assessment #framework #learning #quality #using: Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning (BO, AH, PA, XC, PD, JW, MB, DBL, SE), pp. 616–625.
KDD-2018-PetroniRNNPSL: An Extensible Event Extraction System With Cross-Media Event Resolution (FP, NR, TN, AN, ZP, SS, JLL), pp. 626–635.
KDD-2018-RaoTL #comprehension #framework #learning #multi #network #platform #query: Multi-Task Learning with Neural Networks for Voice Query Understanding on an Entertainment Platform (JR, FT, JL), pp. 636–645.
KDD-2018-RongXYM #big data #named #realtime: Du-Parking: Spatio-Temporal Big Data Tells You Realtime Parking Availability (YR, ZX, RY, XM), pp. 646–654.
KDD-2018-Ruhrlander0U #modelling #predict #using: Improving Box Office Result Predictions for Movies Using Consumer-Centric Models (RPR, MB0, MU), pp. 655–664.
KDD-2018-SadrediniGBRSW #hardware #novel #rule-based #scalability: A Scalable Solution for Rule-Based Part-of-Speech Tagging on Novel Hardware Accelerators (ES, DG, CB, RR, KS, HW), pp. 665–674.
KDD-2018-SafaviDK #case study: Career Transitions and Trajectories: A Case Study in Computing (TS, MD, DK), pp. 675–684.
KDD-2018-SamelM #learning: Active Deep Learning to Tune Down the Noise in Labels (KS, XM), pp. 685–694.
KDD-2018-SatoNHMHAM #detection #learning: Managing Computer-Assisted Detection System Based on Transfer Learning with Negative Transfer Inhibition (IS, YN, SH, SM, NH, OA, YM), pp. 695–704.
KDD-2018-Schlosser0 #approach #contest #data-driven #online: Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach (RS, MB0), pp. 705–714.
KDD-2018-ShashikumarSCN #bidirectional #detection #network #using: Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks (SPS, AJS, GDC, SN), pp. 715–723.
KDD-2018-ShenLOLZC #framework #named #network #novel #predict: StepDeep: A Novel Spatial-temporal Mobility Event Prediction Framework based on Deep Neural Network (BS, XL, YO, ML, WZ, KMC), pp. 724–733.
KDD-2018-ShengTWXZN #email #information management #privacy #scalability: Anatomy of a Privacy-Safe Large-Scale Information Extraction System Over Email (YS0, ST, JBW, JX0, QZ, MN), pp. 734–743.
KDD-2018-ShiPHMWMMLDC #performance: Audience Size Forecasting: Fast and Smart Budget Planning for Media Buyers (YS, CP, RH, WM, MHW, JM, PM, PL, RDW, RC), pp. 744–753.
KDD-2018-SilvisSL #named #student: PittGrub: A Frustration-Free System to Reduce Food Waste by Notifying Hungry College Students (MS, AS, AL), pp. 754–763.
KDD-2018-WaliaHCCLKNBAM #pipes and filters #predict #risk management: A Dynamic Pipeline for Spatio-Temporal Fire Risk Prediction (BSW, QH, JC, FC, JL, NK, PN, JB, GA, MM), pp. 764–773.
KDD-2018-StaarDAB #corpus #documentation #framework #machine learning #platform #scalability: Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale (PWJS, MD, CA, CB), pp. 774–782.
KDD-2018-SugiuraKYMAY #visual notation #visualisation: Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and Visualization (HS, TK, SY, HM, RA, KY), pp. 783–792.
KDD-2018-SunTYWZ #identification #modelling #predict: Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models (MS, FT, JY, FW, JZ), pp. 793–801.
KDD-2018-SureshGG #learning #multi: Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU (HS, JJG, JVG), pp. 802–810.
KDD-2018-TaoXGLFZ #detection #framework #game studies #named: NGUARD: A Game Bot Detection Framework for NetEase MMORPGs (JT, JX, LG, YL, CF, ZZ), pp. 811–820.
KDD-2018-Valdez-VivasGKF #detection #distributed #framework #performance #realtime: A Real-time Framework for Detecting Efficiency Regressions in a Globally Distributed Codebase (MVV, CG, AK, EF, KG, SC), pp. 821–829.
KDD-2018-WangWW #network: Inferring Metapopulation Propagation Network for Intra-city Epidemic Control and Prevention (JW, XW, JW), pp. 830–838.
KDD-2018-WangHZZZL #e-commerce #recommendation: Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (JW, PH, HZ, ZZ, BZ, DLL), pp. 839–848.
KDD-2018-WangMJYXJSG #detection #multi #named #network: EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (YW, FM, ZJ, YY0, GX, KJ, LS, JG0), pp. 849–857.
KDD-2018-WangFY #learning: Learning to Estimate the Travel Time (ZW, KF, JY), pp. 858–866.
KDD-2018-WongPKFJ #biology #community #named #network #performance: SDREGION: Fast Spotting of Changing Communities in Biological Networks (SWHW, CP, MK, CF, IJ), pp. 867–875.
KDD-2018-XieCS #detection #online: False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments (YX, NC, XS), pp. 876–885.
KDD-2018-ShenYXEBW0 #graph #mobile #scalability: Mobile Access Record Resolution on Large-Scale Identifier-Linkage Graphs (XS, HY, WX, ME, JB, ZW, CW0), pp. 886–894.
KDD-2018-XuDH #named #online #quality: SQR: Balancing Speed, Quality and Risk in Online Experiments (YX, WD, SH), pp. 895–904.
KDD-2018-XuLGZLNLBY #approach #learning #on-demand #order #platform #scalability: Large-Scale Order Dispatch in On-Demand Ride-Hailing Platforms: A Learning and Planning Approach (ZX, ZL, QG, DZ, QL, JN, CL, WB, JY), pp. 905–913.
KDD-2018-YangSJ0 #clustering #ll #mobile #predict #social: I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application (CY, XS, LJ, JH0), pp. 914–922.
KDD-2018-YangZTWCH #case study #contest #image #learning #recognition: Deep Learning for Practical Image Recognition: Case Study on Kaggle Competitions (XY, ZZ, SGT, LW, VC0, SCHH), pp. 923–931.
KDD-2018-YeQCWZMYZ: Customized Regression Model for Airbnb Dynamic Pricing (PY, JQ, JC, CHW, YZ, SDM, FY, LZ), pp. 932–940.
KDD-2018-YeZZGZ #evaluation #named #parallel #performance: RapidScorer: Fast Tree Ensemble Evaluation by Maximizing Compactness in Data Level Parallelization (TY, HZ, WYZ, BG, RZ), pp. 941–950.
KDD-2018-YiZWLZ #distributed #network #predict #quality: Deep Distributed Fusion Network for Air Quality Prediction (XY, JZ, ZW, TL, YZ0), pp. 965–973.
KDD-2018-YingHCEHL #graph #network #recommendation: Graph Convolutional Neural Networks for Web-Scale Recommender Systems (RY, RH, KC, PE, WLH, JL), pp. 974–983.
KDD-2018-YuanZY #approach #learning #named #predict: Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data (ZY, XZ, TY), pp. 984–992.
KDD-2018-ZhangPZZZRJ #visual notation: Visual Search at Alibaba (YZ, PP, YZ, KZ, YZ, XR, RJ), pp. 993–1001.
KDD-2018-ZhangZY0 #ambiguity #clustering #maintenance: Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop (YZ, FZ, PY, JT0), pp. 1002–1011.
KDD-2018-ZhaoNOE #optimisation: Notification Volume Control and Optimization System at Pinterest (BZ, KN, BO, JE), pp. 1012–1020.
KDD-2018-0009QG0H #learning #realtime: Deep Reinforcement Learning for Sponsored Search Real-time Bidding (JZ0, GQ, ZG, WZ0, XH), pp. 1021–1030.
KDD-2018-ZhaoLSY #e-commerce #learning #representation: Learning and Transferring IDs Representation in E-commerce (KZ, YL, ZS, CY), pp. 1031–1039.
KDD-2018-ZhaoZDXTY #feedback #learning #recommendation: Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (XZ, LZ, ZD, LX, JT, DY), pp. 1040–1048.
KDD-2018-ZhengMD0 #named: OpenTag: Open Attribute Value Extraction from Product Profiles (GZ, SM, XLD, FL0), pp. 1049–1058.
KDD-2018-ZhouZSFZMYJLG #network #predict: Deep Interest Network for Click-Through Rate Prediction (GZ, XZ, CS, YF, HZ, XM, YY, JJ, HL, KG), pp. 1059–1068.
KDD-2018-ZhouNMZ #interactive: Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning (XZ, AN, CM, ZZ), pp. 1069–1078.
KDD-2018-ZhuLZLHLG #learning #recommendation: Learning Tree-based Deep Model for Recommender Systems (HZ, XL, PZ, GL, JH, HL, KG), pp. 1079–1088.
KDD-2018-AbebeKPT #persuasion: Opinion Dynamics with Varying Susceptibility to Persuasion (RA, JMK, DCP, CET), pp. 1089–1098.
KDD-2018-AcharyaGZ #markov #topic: A Dual Markov Chain Topic Model for Dynamic Environments (AA, JG, MZ), pp. 1099–1108.
KDD-2018-Anagnostopoulos #algorithm #online #outsourcing: Algorithms for Hiring and Outsourcing in the Online Labor Market (AA, CC0, AF, SL, ET), pp. 1109–1118.
KDD-2018-BachemL0 #clustering #lightweight #scalability: Scalable k -Means Clustering via Lightweight Coresets (OB, ML, AK0), pp. 1119–1127.
KDD-2018-BaiQD #behaviour #modelling: Discovering Models from Structural and Behavioral Brain Imaging Data (ZB, BQ, ID), pp. 1128–1137.
KDD-2018-BateniEM #distributed #optimisation #sketching: Optimal Distributed Submodular Optimization via Sketching (MB, HE, VSM), pp. 1138–1147.
KDD-2018-Benson0T #sequence #set: Sequences of Sets (ARB, RK0, AT), pp. 1148–1157.
KDD-2018-CaiWGSJ #learning #multi: Deep Adversarial Learning for Multi-Modality Missing Data Completion (LC, ZW, HG, DS, SJ), pp. 1158–1166.
KDD-2018-0022PYT #algorithm #effectiveness #network #optimisation: Network Connectivity Optimization: Fundamental Limits and Effective Algorithms (CC0, RP, LY, HT), pp. 1167–1176.
KDD-2018-ChenYWWNL #metric #named #network #predict: PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction (HC, HY, WW0, HW0, QVHN, XL), pp. 1177–1186.
KDD-2018-Chen0DTHT #learning #online #recommendation: Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation (SYC, YY0, QD, JT, HKH, HHT), pp. 1187–1196.
KDD-2018-ChenLB #network #social: Quantifying and Minimizing Risk of Conflict in Social Networks (XC, JL, TDB), pp. 1197–1205.
KDD-2018-ChenHNHYH #clustering #normalisation #scalability: Spectral Clustering of Large-scale Data by Directly Solving Normalized Cut (XC0, WH, FN, DH, MY0, JZH), pp. 1206–1215.
KDD-2018-ChenCDLW0C #information management #named: Learning-to-Ask: Knowledge Acquisition via 20 Questions (YC, BC, XD, JGL, YW, WZ0, YC), pp. 1216–1225.
KDD-2018-ChenGCSSJ #3d #image #network: Voxel Deconvolutional Networks for 3D Brain Image Labeling (YC, HG, LC, MS, DS, SJ), pp. 1226–1234.
KDD-2018-Christakopoulou18a #modelling #recommendation: Local Latent Space Models for Top-N Recommendation (EC, GK), pp. 1235–1243.
KDD-2018-ChuHHWP #consistency #linear #network: Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution (LC, XH, JH, LW, JP), pp. 1244–1253.
KDD-2018-CobbEMR #calculus #identification #multi #process: Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus (ADC, RE, AM, SJR), pp. 1254–1262.
KDD-2018-Cohen-SteinerKS #approximate #graph: Approximating the Spectrum of a Graph (DCS, WK, CS, GV), pp. 1263–1271.
KDD-2018-ConteMSGMV #community #detection #named #network #scalability: D2K: Scalable Community Detection in Massive Networks via Small-Diameter k-Plexes (AC, TDM, DDS, RG, AM, LV), pp. 1272–1281.
KDD-2018-ConteFGMSU #network #similarity: Node Similarity with q -Grams for Real-World Labeled Networks (AC, GF, RG, AM, KS, TU), pp. 1282–1291.
KDD-2018-DahiyaKW #algebra #empirical #evaluation #linear #sketching: An Empirical Evaluation of Sketching for Numerical Linear Algebra (YD, DK, DPW), pp. 1292–1300.
KDD-2018-DiPSC #learning #morphism: Transfer Learning via Feature Isomorphism Discovery (SD, JP, YS, LC), pp. 1301–1309.
KDD-2018-DingLBZL #framework #named: Investor-Imitator: A Framework for Trading Knowledge Extraction (YD, WL, JB0, DZ, TYL), pp. 1310–1319.
KDD-2018-DonnatZHL #learning: Learning Structural Node Embeddings via Diffusion Wavelets (CD, MZ, DH, JL), pp. 1320–1329.
KDD-2018-DuTZTZ: Demand-Aware Charger Planning for Electric Vehicle Sharing (BD, YT, ZZ, QT, WZ), pp. 1330–1338.
KDD-2018-DuT #equation #graph #mining #named #performance: FASTEN: Fast Sylvester Equation Solver for Graph Mining (BD, HT), pp. 1339–1347.
KDD-2018-DuDXZW #multi: Multi-view Adversarially Learned Inference for Cross-domain Joint Distribution Matching (CD, CD, XX, CZ, HW), pp. 1348–1357.
KDD-2018-DuLSH #predict #towards: Towards Explanation of DNN-based Prediction with Guided Feature Inversion (MD, NL, QS, XH), pp. 1358–1367.
KDD-2018-Ertl #algorithm #set: BagMinHash - Minwise Hashing Algorithm for Weighted Sets (OE), pp. 1368–1377.
KDD-2018-EswaranFGM #detection #graph #named #streaming: SpotLight: Detecting Anomalies in Streaming Graphs (DE, CF, SG, NM), pp. 1378–1386.
KDD-2018-FoxAJPW #learning #multi #predict: Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories (IF, LA, MJ, RPB, JW), pp. 1387–1395.
KDD-2018-FuWHW #approximate #fault #learning #reduction #scalability: Scalable Active Learning by Approximated Error Reduction (WF, MW, SH, XW0), pp. 1396–1405.
KDD-2018-GaoH #network #self: Self-Paced Network Embedding (HG, HH), pp. 1406–1415.
KDD-2018-GaoWJ #graph #network #scalability: Large-Scale Learnable Graph Convolutional Networks (HG, ZW, SJ), pp. 1416–1424.
KDD-2018-GargR #exclamation #recommendation: Route Recommendations for Idle Taxi Drivers: Find Me the Shortest Route to a Customer! (NG, SR), pp. 1425–1434.
KDD-2018-DizajiWH #generative #network: Semi-Supervised Generative Adversarial Network for Gene Expression Inference (KGD, XW, HH), pp. 1435–1444.
KDD-2018-GiesekeI #random: Training Big Random Forests with Little Resources (FG, CI), pp. 1445–1454.
KDD-2018-GongW #analysis #behaviour #modelling #network #sentiment #social: When Sentiment Analysis Meets Social Network: A Holistic User Behavior Modeling in Opinionated Data (LG, HW), pp. 1455–1464.
KDD-2018-GorovitsGPB #community #learning #named: LARC: Learning Activity-Regularized Overlapping Communities Across Time (AG, EG, EEP, PB), pp. 1465–1474.
KDD-2018-GuYCH #algorithm #incremental #learning: New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine (BG, XTY, SC, HH), pp. 1475–1484.
KDD-2018-GuiL #robust: R 2 SDH: Robust Rotated Supervised Discrete Hashing (JG, PL0), pp. 1485–1493.
KDD-2018-HanSSZ #collaboration #learning #multi #semistructured data: Multi-label Learning with Highly Incomplete Data via Collaborative Embedding (YH, GS, YS, XZ0), pp. 1494–1503.
KDD-2018-HauserEM #optimisation: PCA by Determinant Optimisation has no Spurious Local Optima (RAH, AE, HFM), pp. 1504–1511.
KDD-2018-HerlandsMWN #automation #design: Automated Local Regression Discontinuity Design Discovery (WH, EMI, AGW, DBN), pp. 1512–1520.
KDD-2018-HongCL #kernel #learning: Disturbance Grassmann Kernels for Subspace-Based Learning (JH, HC, FL), pp. 1521–1530.
KDD-2018-HuSZY #recommendation: Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model (BH, CS, WXZ, PSY), pp. 1531–1540.
KDD-2018-HuaiMLSSZ #learning #metric #probability: Metric Learning from Probabilistic Labels (MH, CM, YL, QS, LS, AZ), pp. 1541–1550.
KDD-2018-Huang0LSG: Generalized Score Functions for Causal Discovery (BH, KZ0, YL, BS, CG), pp. 1551–1560.
KDD-2018-HuangMFFT #performance #symmetry: Accurate and Fast Asymmetric Locality-Sensitive Hashing Scheme for Maximum Inner Product Search (QH, GM, JF, QF, AKHT), pp. 1561–1570.
KDD-2018-HuangXXSNC #matrix: Active Feature Acquisition with Supervised Matrix Completion (SJH, MX, MKX, MS, GN, SC), pp. 1571–1579.
KDD-2018-HuangZL #adaptation #effectiveness: Cost-Effective Training of Deep CNNs with Active Model Adaptation (SJH, JWZ, ZYL), pp. 1580–1588.
KDD-2018-JeongJ #learning #multi: Variable Selection and Task Grouping for Multi-Task Learning (JYJ, CHJ), pp. 1589–1598.
KDD-2018-JhaXWGZ #concept #evolution #named: Concepts-Bridges: Uncovering Conceptual Bridges Based on Biomedical Concept Evolution (KJ, GX, YW, VG, AZ), pp. 1599–1607.
KDD-2018-0001YSLQT #predict: A Treatment Engine by Predicting Next-Period Prescriptions (BJ0, HY, LS, CL, YQ, JT), pp. 1608–1616.
KDD-2018-KuangCAXL #predict: Stable Prediction across Unknown Environments (KK, PC0, SA, RX, BL0), pp. 1617–1626.
KDD-2018-KumagaiI #bound #learning: Learning Dynamics of Decision Boundaries without Additional Labeled Data (AK, TI), pp. 1627–1636.
KDD-2018-Le0V #learning #memory management: Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning (HL, TT0, SV), pp. 1637–1645.
KDD-2018-LeeLW #algorithm #distributed #empirical: A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization (CpL, CHL, SJW), pp. 1646–1655.
KDD-2018-LeeK #adaptation #privacy: Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget (JL, DK), pp. 1656–1665.
KDD-2018-LeeRK #classification #graph #using: Graph Classification using Structural Attention (JBL, RAR, XK), pp. 1666–1674.
KDD-2018-Li0ZQHG0 #information management #named #reliability: TruePIE: Discovering Reliable Patterns in Pattern-Based Information Extraction (QL0, MJ0, XZ, MQ, TPH, JG0, JH0), pp. 1675–1684.
KDD-2018-LiAKMVW #evaluation #modelling #policy #ranking: Offline Evaluation of Ranking Policies with Click Models (SL, YAY, BK, SM, VV, ZW), pp. 1685–1694.
KDD-2018-LiFWSYL #estimation #learning #multi #representation: Multi-task Representation Learning for Travel Time Estimation (YL, KF, ZW, CS, JY, YL0), pp. 1695–1704.
KDD-2018-LiMSGLDQ0 #performance #privacy: An Efficient Two-Layer Mechanism for Privacy-Preserving Truth Discovery (YL, CM, LS, JG0, QL0, BD, ZQ, KR0), pp. 1705–1714.
KDD-2018-LiY #classification #learning #network #policy: Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient (YL, JY), pp. 1715–1723.
KDD-2018-LiZY #approach #learning: Dynamic Bike Reposition: A Spatio-Temporal Reinforcement Learning Approach (YL, YZ, QY), pp. 1724–1733.
KDD-2018-LiZLHMC #behaviour #learning #recommendation: Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors (ZL, HZ, QL0, ZH, TM, EC), pp. 1734–1743.
KDD-2018-Lian0ZGCT0 #higher-order #network #proximity: High-order Proximity Preserving Information Network Hashing (DL, KZ0, VWZ, YG, LC, IWT, XX0), pp. 1744–1753.
KDD-2018-LianZZCXS #feature model #interactive #named #recommendation: xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (JL, XZ, FZ, ZC, XX0, GS), pp. 1754–1763.
KDD-2018-LiangZRK #profiling #twitter: Dynamic Embeddings for User Profiling in Twitter (SL, XZ0, ZR, EK), pp. 1764–1773.
KDD-2018-LinZXZ #learning #multi #performance #scalability: Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning (KL, RZ, ZX, JZ), pp. 1774–1783.
KDD-2018-LiuTZ #modelling #predict: Enhancing Predictive Modeling of Nested Spatial Data through Group-Level Feature Disaggregation (BL, PNT, JZ), pp. 1784–1793.
KDD-2018-LiuHWH #network #self: Content to Node: Self-Translation Network Embedding (JL0, ZH, LW, YH), pp. 1794–1802.
KDD-2018-LiuYH #detection: Adversarial Detection with Model Interpretation (NL, HY, XH), pp. 1803–1811.
KDD-2018-LiuHLH #induction #network #on the #taxonomy: On Interpretation of Network Embedding via Taxonomy Induction (NL, XH, JL, XH), pp. 1812–1820.
KDD-2018-LiuHHLCSH #education #online: Finding Similar Exercises in Online Education Systems (QL0, ZH, ZH, CL, EC, YS0, GH), pp. 1821–1830.
KDD-2018-LiuZMZ #memory management #named #recommendation: STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation (QL, YZ, RM, HZ), pp. 1831–1839.
KDD-2018-LiuKY #network #social: Active Opinion Maximization in Social Networks (XL, XK, PSY), pp. 1840–1849.
KDD-2018-LiuZC #learning #metric #performance: Efficient Similar Region Search with Deep Metric Learning (YL, KZ0, GC), pp. 1850–1859.
KDD-2018-LiuZZLYWY #graph #interactive #proximity #semantics: Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs (ZL, VWZ, ZZ, ZL, HY, MW, JY), pp. 1860–1869.
KDD-2018-LiuXC #recommendation: Context-aware Academic Collaborator Recommendation (ZL0, XX, LC0), pp. 1870–1879.
KDD-2018-LuJZDZW #learning #named #semantics #visual notation: R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering (PL, LJ, WZ0, ND, MZ0, JW), pp. 1880–1889.
KDD-2018-LuoCTSLCY #information management #invariant #learning #named #network: TINET: Learning Invariant Networks via Knowledge Transfer (CL, ZC, LAT, AS, ZL, HC, JY), pp. 1890–1899.
KDD-2018-Luo0Z0ZP #online #sketching: Sketched Follow-The-Regularized-Leader for Online Factorization Machine (LL, WZ0, ZZ, WZ0, TZ, JP), pp. 1900–1909.
KDD-2018-MaGSYZZ #health #predict #risk management: Risk Prediction on Electronic Health Records with Prior Medical Knowledge (FM, JG0, QS, QY, JZ, AZ), pp. 1910–1919.
KDD-2018-MaCW0 #network #taxonomy: Hierarchical Taxonomy Aware Network Embedding (JM, PC0, XW0, WZ0), pp. 1920–1929.
KDD-2018-MaZYCHC #learning #modelling #multi: Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (JM, ZZ, XY, JC, LH, EHC), pp. 1930–1939.
KDD-2018-ManapragadaWS #performance: Extremely Fast Decision Tree (CM, GIW, MS), pp. 1953–1962.
KDD-2018-ManzoorLA #performance: Extremely Fast Decision Tree (EAM, HL, LA), pp. 1963–1972.
KDD-2018-MautzYPB #clustering: Discovering Non-Redundant K-means Clusterings in Optimal Subspaces (DM, WY0, CP, CB), pp. 1973–1982.
KDD-2018-ReisMSB: Classifying and Counting with Recurrent Contexts (DMdR, AGM, DFS, GEAPAB), pp. 1983–1992.
KDD-2018-NaKY #data type #detection #effectiveness #memory management #named #performance: DILOF: Effective and Memory Efficient Local Outlier Detection in Data Streams (GSN, DHK, HY), pp. 1993–2002.
KDD-2018-NguyenLNPW #big data #kernel #robust: Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data (KN, TL, TDN, DQP, GIW), pp. 2003–2011.
KDD-2018-NieHL #learning #multi: Calibrated Multi-Task Learning (FN, ZH, XL), pp. 2012–2021.
KDD-2018-NieTL #adaptation #clustering #multi: Multiview Clustering via Adaptively Weighted Procrustes (FN, LT, XL), pp. 2022–2030.
KDD-2018-NiuZWTGC #correlation #privacy #statistics: Unlocking the Value of Privacy: Trading Aggregate Statistics over Private Correlated Data (CN, ZZ, FW0, ST, XG, GC), pp. 2031–2040.
KDD-2018-PangCCL #detection #learning #random: Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection (GP, LC, LC, HL), pp. 2041–2050.
KDD-2018-Park0 #effectiveness #graph #named #performance #scalability: EvoGraph: An Effective and Efficient Graph Upscaling Method for Preserving Graph Properties (HP, MSK0), pp. 2051–2059.
KDD-2018-PeakeW #mining #modelling #recommendation: Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems (GP, JW), pp. 2060–2069.
KDD-2018-PellegrinaV #mining #mutation testing #performance #permutation #testing: Efficient Mining of the Most Significant Patterns with Permutation Testing (LP, FV), pp. 2070–2079.
KDD-2018-PerrosPPVYdSS #named #scalability: SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping (IP, EEP, HP, RWV, XY, Cd, WFS, JS), pp. 2080–2089.
KDD-2018-Pouget-AbadieMP #clustering #optimisation #random: Optimizing Cluster-based Randomized Experiments under Monotonicity (JPA, VSM, DCP, EMA), pp. 2090–2099.
KDD-2018-QahtanEFO0 #detection #named #robust: FAHES: A Robust Disguised Missing Values Detector (AAQ, AKE, RCF, MO, NT0), pp. 2100–2109.
KDD-2018-QiuTMDW0 #learning #named #predict #social: DeepInf: Social Influence Prediction with Deep Learning (JQ, JT, HM, YD, KW, JT0), pp. 2110–2119.
KDD-2018-RabbanyBD: Active Search of Connections for Case Building and Combating Human Trafficking (RR, DB, AD), pp. 2120–2129.
KDD-2018-RiondatoV #mining #named #pseudo: MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension (MR, FV), pp. 2130–2139.
KDD-2018-SachanX #framework #parsing #source code: Parsing to Programs: A Framework for Situated QA (MS, EPX), pp. 2140–2149.
KDD-2018-Sanei-MehriST #network: Butterfly Counting in Bipartite Networks (SVSM, AES, ST), pp. 2150–2159.
KDD-2018-SatohTY #equivalence #incremental: Accelerated Equivalence Structure Extraction via Pairwise Incremental Search (SS, YT, HY), pp. 2160–2169.
KDD-2018-ShanJZM #retrieval #semantics: Recurrent Binary Embedding for GPU-Enabled Exhaustive Retrieval from Billion-Scale Semantic Vectors (YS, JJ, JZ0, JCM), pp. 2170–2179.
KDD-2018-ShenWLZRVS0 #named #taxonomy: HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion (JS, ZW, DL, CZ0, XR, MTV, BMS, JH0), pp. 2180–2189.
KDD-2018-ShiZGZ0 #learning #network: Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks (YS, QZ, FG, CZ0, JH0), pp. 2190–2199.
KDD-2018-SiddiquiFDWTA #online #optimisation: Feedback-Guided Anomaly Discovery via Online Optimization (MAS, AF, TGD, RW, AT, DWA), pp. 2200–2209.
KDD-2018-SifferFTL #question: Are your data gathered? (AS, PAF, AT, CL), pp. 2210–2218.
KDD-2018-SinghJ #ranking: Fairness of Exposure in Rankings (AS, TJ), pp. 2219–2228.
KDD-2018-SongXCCT #multi #rank #retrieval: Deep r -th Root of Rank Supervised Joint Binary Embedding for Multivariate Time Series Retrieval (DS, NX, WC, HC, DT), pp. 2229–2238.
KDD-2018-SpeicherHGGSWZ #algorithm #approach #difference: A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices (TS, HH, NGH, KPG, AS, AW, MBZ), pp. 2239–2248.
KDD-2018-SunHYC #multi: Multi-Round Influence Maximization (LS, WH0, PSY, WC), pp. 2249–2258.
KDD-2018-SunBZWZ #modelling #multi #network: Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases (MS, IMB, LZ, ZW, JZ), pp. 2259–2268.
KDD-2018-SunZZGH #analysis #online #query: Exploring the Urban Region-of-Interest through the Analysis of Online Map Search Queries (YS, HZ, FZ, JG, QH), pp. 2269–2278.
KDD-2018-SuttonHGC #execution #summary: Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling (CAS, TH, JG, RC), pp. 2279–2288.
KDD-2018-TangW #learning #modelling #performance #ranking #recommendation: Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System (JT, KW), pp. 2289–2298.
KDD-2018-TayTH #multi #network: Multi-Cast Attention Networks (YT, LAT, SCH), pp. 2299–2308.
KDD-2018-TayLH #multi #network #recommendation: Multi-Pointer Co-Attention Networks for Recommendation (YT, ATL, SCH), pp. 2309–2318.
KDD-2018-Ting #bound #empirical #estimation #fault #named #using: Count-Min: Optimal Estimation and Tight Error Bounds using Empirical Error Distributions (DT), pp. 2319–2328.
KDD-2018-TingZZ #kernel: Isolation Kernel and Its Effect on SVM (KMT, YZ, ZHZ), pp. 2329–2337.
KDD-2018-TomasiTSV #network: Latent Variable Time-varying Network Inference (FT, VT, SS, AV), pp. 2338–2346.
KDD-2018-TsitsulinMKBM #graph #named: NetLSD: Hearing the Shape of a Graph (AT, DM, PK, AMB, EM), pp. 2347–2356.
KDD-2018-TuCWY0 #equivalence #network #recursion: Deep Recursive Network Embedding with Regular Equivalence (KT, PC0, XW0, PSY, WZ0), pp. 2357–2366.
KDD-2018-RijnH #dataset: Hyperparameter Importance Across Datasets (JNvR, FH), pp. 2367–2376.
KDD-2018-VandalKDGNG #learning #nondeterminism: Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning (TV, EK, JGD, SG, RRN, ARG), pp. 2377–2386.
KDD-2018-0001C #performance #probability #recommendation: Efficient Attribute Recommendation with Probabilistic Guarantee (CW0, KC), pp. 2387–2396.
KDD-2018-WangJZEC #behaviour #learning #multi: Multi-Type Itemset Embedding for Learning Behavior Success (DW, MJ0, QZ, ZE, NVC), pp. 2397–2406.
KDD-2018-WangZBZCY #learning #mobile #performance #privacy: Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud (JW0, JZ, WB, XZ, BC, PSY), pp. 2407–2416.
KDD-2018-WangOWW #learning #modelling: Learning Credible Models (JW, JO, HW, JW), pp. 2417–2426.
KDD-2018-WangZ #learning #problem #towards: Towards Mitigating the Class-Imbalance Problem for Partial Label Learning (JW, MLZ), pp. 2427–2436.
KDD-2018-WangWLW #analysis #composition #multi #network: Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis (JW, ZW, JL, JW), pp. 2437–2446.
KDD-2018-WangZHZ #learning #network #recommendation: Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (LW, WZ0, XH, HZ), pp. 2447–2456.
KDD-2018-WangFZWZA #analysis #behaviour #how #learning #representation: You Are How You Drive: Peer and Temporal-Aware Representation Learning for Driving Behavior Analysis (PW, YF, JZ, PW, YZ, CCA), pp. 2457–2466.
KDD-2018-WangYHLWH #memory management #network #recommendation #streaming: Neural Memory Streaming Recommender Networks with Adversarial Training (QW, HY, ZH, DL, HW, ZH), pp. 2467–2475.
KDD-2018-WangXQ0T #towards: Towards Evolutionary Compression (YW, CX0, JQ, CX0, DT), pp. 2476–2485.
KDD-2018-WangJ #predict: Smoothed Dilated Convolutions for Improved Dense Prediction (ZW, SJ), pp. 2486–2495.
KDD-2018-WeiZYL #approach #learning #named: IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control (HW, GZ, HY, ZL), pp. 2496–2505.
KDD-2018-WuCYXXA #clustering #random #scalability #using: Scalable Spectral Clustering Using Random Binning Features (LW, PYC, IEHY, FX, YX, CCA), pp. 2506–2515.
KDD-2018-WuYYZ #learning #process: Decoupled Learning for Factorial Marked Temporal Point Processes (WW, JY, XY, HZ), pp. 2516–2525.
KDD-2018-WuYC #learning #realtime: Deep Censored Learning of the Winning Price in the Real Time Bidding (WCHW, MYY, MSC), pp. 2526–2535.
KDD-2018-WuZW #graph #on the #using: On Discrimination Discovery and Removal in Ranked Data using Causal Graph (YW, LZ0, XW), pp. 2536–2544.
KDD-2018-Xie0S #markov: Geographical Hidden Markov Tree for Flood Extent Mapping (MX, ZJ0, AMS), pp. 2545–2554.
KDD-2018-XuLDH #learning #metric #robust #using: New Robust Metric Learning Model Using Maximum Correntropy Criterion (JX0, LL, CD, HH), pp. 2555–2564.
KDD-2018-XuBDMS #monitoring #multimodal #named: RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data (YX, SB, SRD, KOM, JS), pp. 2565–2573.
KDD-2018-YanZ #modelling #towards: Coupled Context Modeling for Deep Chit-Chat: Towards Conversations between Human and Computer (RY0, DZ0), pp. 2574–2583.
KDD-2018-0003GZZSL #data type #named: HeavyGuardian: Separate and Guard Hot Items in Data Streams (TY0, JG, HZ, LZ0, LS, XL), pp. 2584–2593.
KDD-2018-YangWZL0 #classification #multi #network: Complex Object Classification: A Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport (YY, YFW, DCZ, ZBL, YJ0), pp. 2594–2603.
KDD-2018-YardimKMG #predict #question: Can Who-Edits-What Predict Edit Survival? (ABY, VK, LM, MG), pp. 2604–2613.
KDD-2018-YasarC #network: An Iterative Global Structure-Assisted Labeled Network Aligner (AY, ÜVÇ), pp. 2614–2623.
KDD-2018-YeZXZGD #mobile #paradigm #parallel #performance #recommendation: Multi-User Mobile Sequential Recommendation: An Efficient Parallel Computing Paradigm (ZY, LZ, KX, WZ, YG, YD), pp. 2624–2633.
KDD-2018-YinCLZYW #clustering #modelling: Model-based Clustering of Short Text Streams (JY, DC, ZL, WZ0, XY0, JW), pp. 2634–2642.
KDD-2018-YinHCLZXH #image: Transcribing Content from Structural Images with Spotlight Mechanism (YY, ZH, EC, QL0, FZ, XX, GH), pp. 2643–2652.
KDD-2018-YoshidaTK #distance #learning #metric: Safe Triplet Screening for Distance Metric Learning (TY, IT, MK), pp. 2653–2662.
KDD-2018-YuZCASZCW #learning #network: Learning Deep Network Representations with Adversarially Regularized Autoencoders (WY, CZ, WC, CCA, DS, BZ, HC, WW0), pp. 2663–2671.
KDD-2018-YuCAZCW #approach #detection #flexibility #named #network: NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks (WY, WC, CCA, KZ0, HC, WW0), pp. 2672–2681.
KDD-2018-ZangC0 #empirical #learning: Learning and Interpreting Complex Distributions in Empirical Data (CZ, PC0, WZ0), pp. 2682–2691.
KDD-2018-ZhangZGWC #estimation #infinity #process: Simultaneous Urban Region Function Discovery and Popularity Estimation via an Infinite Urbanization Process Model (BZ, LZ, TG, YW0, FC0), pp. 2692–2700.
KDD-2018-ZhangTCSJSV0 #adaptation #clustering #named #taxonomy #topic: TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering (CZ0, FT, XC, JS, MJ0, BMS, MV, JH0), pp. 2701–2709.
KDD-2018-ZhangWCDYW #modelling #named #reliability: StockAssIstant: A Stock AI Assistant for Reliability Modeling of Stock Comments (CZ, YW, CC, CD, HY, HW), pp. 2710–2719.
KDD-2018-ZhangLDFY #generative #on the: On the Generative Discovery of Structured Medical Knowledge (CZ, YL, ND, WF0, PSY), pp. 2720–2728.
KDD-2018-ZhangLMGS #approach #multi #named: TextTruth: An Unsupervised Approach to Discover Trustworthy Information from Multi-Sourced Text Data (HZ, YL, FM, JG0, LS), pp. 2729–2737.
KDD-2018-ZhangW #crowdsourcing #multi: Multi-Label Inference for Crowdsourcing (JZ, XW0), pp. 2738–2747.
KDD-2018-ZhangBLLZP: Trajectory-driven Influential Billboard Placement (PZ, ZB, YL, GL0, YZ, ZP), pp. 2748–2757.
KDD-2018-ZhangWLTYY #learning #matrix #self: Discrete Ranking-based Matrix Factorization with Self-Paced Learning (YZ0, HW, DL, IWT, HY, GY), pp. 2758–2767.
KDD-2018-ZhangZCMHWT #adaptation #learning #online #symmetry: Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data (YZ0, PZ, JC, WM, JH, QW, MT), pp. 2768–2777.
KDD-2018-ZhangCWPY0 #network #proximity: Arbitrary-Order Proximity Preserved Network Embedding (ZZ, PC0, XW0, JP, XY, WZ0), pp. 2778–2786.
KDD-2018-ZhaoAS0 #classification #dependence #performance #predict #using: Prediction-time Efficient Classification Using Feature Computational Dependencies (LZ0, AAF, MS, KZ0), pp. 2787–2796.
KDD-2018-ZhaoSWZN0 #framework #named #rest: REST: A Reference-based Framework for Spatio-temporal Trajectory Compression (YZ0, SS, YW, BZ, QVHN, KZ0), pp. 2797–2806.
KDD-2018-ZhouHYF #named #network #representation #self: SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization (DZ, JH, HY, WF), pp. 2807–2816.
KDD-2018-ZhouNH #adaptation #education #memory management #what: Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners (YZ, ARN, JH), pp. 2817–2826.
KDD-2018-ZhuCW0 #network: Deep Variational Network Embedding in Wasserstein Space (DZ, PC0, DW, WZ0), pp. 2827–2836.
KDD-2018-ZhuLYQLZZWXC #framework #generative #music: XiaoIce Band: A Melody and Arrangement Generation Framework for Pop Music (HZ, QL0, NJY, CQ, JL, KZ, GZ, FW, YX, EC), pp. 2837–2846.
KDD-2018-ZugnerAG #graph #network: Adversarial Attacks on Neural Networks for Graph Data (DZ, AA, SG), pp. 2847–2856.
KDD-2018-ZuoLLGHW #network: Embedding Temporal Network via Neighborhood Formation (YZ, GL, HL, JG, XH, JW), pp. 2857–2866.
KDD-2018-Abowd #difference #privacy: The U.S. Census Bureau Adopts Differential Privacy (JMA), p. 2867.
KDD-2018-Datar #e-commerce: Data Science at Flipkart - An Indian E-Commerce company (MD), p. 2868.
KDD-2018-Dong #challenge #graph: Challenges and Innovations in Building a Product Knowledge Graph (XLD), p. 2869.
KDD-2018-Fan #approach #machine learning: The Pinterest Approach to Machine Learning (LF), p. 2870.
KDD-2018-Hodson #big data #future of: Humans, Jobs, and the Economy: The Future of Finance in the Age of Big Data (JH), p. 2871.
KDD-2018-ProvostHWYN: Societal Impact of Data Science and Artificial Intelligence (FJP, JH, JMW, QY, JN), pp. 2872–2873.
KDD-2018-Raghavan #community #realtime #recommendation: Building Near Realtime Contextual Recommendations for Active Communities on LinkedIn (HR), p. 2874.
KDD-2018-Rajan #scalability: Computational Advertising at Scale (SR), p. 2875.
KDD-2018-Re: Software 2.0 and Snorkel: Beyond Hand-Labeled Data (CR), p. 2876.
KDD-2018-Sirosh #classification: Planet-Scale Land Cover Classification with FPGAs (JS), p. 2877.
KDD-2018-Smola #algorithm #hardware #tool support: Algorithms, Data, Hardware and Tools: A Perfect Storm (AS), p. 2878.
KDD-2018-Walraven: Data Science and Entertainment Production (JW), p. 2879.
KDD-2018-Xing #algorithm #co-evolution #design #machine learning #named: SysML: On System and Algorithm Co-design for Practical Machine Learning (EPX), p. 2880.

Yike Guo, Faisal FarooqProceedings of the 24th International Conference on Knowledge Discovery and Data MiningKDD, 2018.

Contents (310 items)

Yike Guo, Faisal Farooq
Proceedings of the 24th International Conference on Knowledge Discovery and Data Mining
KDD, 2018.