BibSLEIGH — Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining

Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, Rajeev Rastogi
Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining
KDD, 2016.

KER

@proceedings{KDD-2016,
	doi           = "10.1145/2939672",
	editor        = "Balaji Krishnapuram and Mohak Shah and Alexander J. Smola and Charu C. Aggarwal and Dou Shen and Rajeev Rastogi",
	isbn          = "978-1-4503-4232-2",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining}",
	year          = 2016,
}

Contents (236 items)

KDD-2016-Chayes #estimation #machine learning #modelling #network: Graphons and Machine Learning: Modeling and Estimation of Sparse Massive Networks (JTC), p. 1.
KDD-2016-Freitas #composition #learning #network: Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality (NdF), p. 3.
KDD-2016-Diffie #evolution #security: The Evolving Meaning of Information Security (WD), p. 5.
KDD-2016-Hellerstein #people: People, Computers, and The Hot Mess of Real Data (JMH), p. 7.
KDD-2016-Papadopoulos #ml: A VC View of Investing in ML (GP), p. 9.
KDD-2016-SimoudisGGOS #big data #lessons learnt: Big Data Needs Big Dreamers: Lessons from Successful Big Data Investors (ES, MG, TG, MO, GS), pp. 11–12.
KDD-2016-AckermannRHKBKG #design #policy #recommendation: Designing Policy Recommendations to Reduce Home Abandonment in Mexico (KA, EBR, SH, TAK, PvdB, RK, RG, JCG), pp. 13–20.
KDD-2016-AyhanS #predict: Aircraft Trajectory Prediction Made Easy with Predictive Analytics (SA, HS), pp. 21–30.
KDD-2016-ZadehMUYPVSSZ #matrix #optimisation: Matrix Computations and Optimization in Apache Spark (RBZ, XM, AU, BY, LP, SV, ERS, AS, MZ), pp. 31–38.
KDD-2016-BotezatuGBW #predict #reliability #towards: Predicting Disk Replacement towards Reliable Data Centers (MMB, IG, JB, DW), pp. 39–48.
KDD-2016-BrooksKG #data-driven #predict #ranking #using: Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights (JB, MK, JVG), pp. 49–55.
KDD-2016-BurgessGKWWHG #detection #reuse: The Legislative Influence Detector: Finding Text Reuse in State Legislation (MB, EG, JKS, JW, DW, LH, RG), pp. 57–66.
KDD-2016-CartonHJMPWCPHG #identification: Identifying Police Officers at Risk of Adverse Events (SC, JH, KJ, AM, YP, JW, CC, CPTEP, LH, RG), pp. 67–76.
KDD-2016-DengS #data-driven #development #lessons learnt #metric #online: Data-Driven Metric Development for Online Controlled Experiments: Seven Lessons Learned (AD, XS), pp. 77–86.
KDD-2016-DuLZHX #detection #scalability: Catch Me If You Can: Detecting Pickpocket Suspects from Large-Scale Transit Records (BD, CL, WZ, ZH, HX), pp. 87–96.
KDD-2016-GuptaLTVCR #email #optimisation: Email Volume Optimization at LinkedIn (RG, GL, HPT, RKHV, XC, RR), pp. 97–106.
KDD-2016-HaPK #categorisation #e-commerce #multi #network #scalability #using: Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks (JH, HP, JK), pp. 107–115.
KDD-2016-HuangJA #composition #distributed #online #performance: Online Dual Decomposition for Performance and Delivery-Based Distributed Ad Allocation (JCH, RJ, CA), pp. 117–126.
KDD-2016-JinCYQGYYZ #collaboration: Minimizing Legal Exposure of High-Tech Companies through Collaborative Filtering Methods (BJ0, CC, KY, YQ, LG0, CY, RY, QZ0), pp. 127–136.
KDD-2016-KapurLCAP #ranking: Ranking Universities Based on Career Outcomes of Graduates (NK, NIL, BCC, DA, IP), pp. 137–144.
KDD-2016-KhanB #behaviour #modelling #predict: Predictors without Borders: Behavioral Modeling of Product Adoption in Three Developing Countries (MRK, JEB), pp. 145–154.
KDD-2016-LiuNZZYCWZC #e-commerce #predict: Repeat Buyer Prediction for E-Commerce (GL, TTN, GZ, WZ, JY, JC, MW0, PZ, WC), pp. 155–164.
KDD-2016-LiuPLTCL #network #online #social: Audience Expansion for Online Social Network Advertising (HL, DP, KL, MT, FC, CL), pp. 165–174.
KDD-2016-LuoYLSYH #behaviour #online: From Online Behaviors to Offline Retailing (PL0, SY, ZL, ZS, SY, QH), pp. 175–184.
KDD-2016-MadaioCHZCHCD #named #predict: Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta (MAM, STC, OLH, WZ, XC0, MHA, DHC, BD), pp. 185–194.
KDD-2016-MalmiTTRG #approach #generative #named: DopeLearning: A Computational Approach to Rap Lyrics Generation (EM, PT, HT, TR, AG), pp. 195–204.
KDD-2016-MuthiahBKSSRZCL #case study #experience #open source: EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting System (SM, PB, RPK, PS, NS, AR, LZ0, JC, CTL, AV, AM, KMS, GK, AD, JA, DKG, DM, NR), pp. 205–214.
KDD-2016-NandiMADB #control flow #detection #execution #graph #mining #using: Anomaly Detection Using Program Control Flow Graph Mining From Execution Logs (AN, AM, SA, GBD, SB), pp. 215–224.
KDD-2016-NikolaevGG #capacity #online #platform #social #social media: Engagement Capacity and Engaging Team Formation for Reach Maximization of Online Social Media Platforms (AGN, SG, VG), pp. 225–234.
KDD-2016-PoyarkovDKGS #online #reduction: Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments (AP, AD, AK, GG, PS), pp. 235–244.
KDD-2016-SalehiRLP #approach #predict #risk management #robust: Dynamic and Robust Wildfire Risk Prediction System: An Unsupervised Approach (MS, LIR, TML, AP), pp. 245–254.
KDD-2016-ShanHJWYM #combinator #modelling: Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features (YS, TRH, JJ, HW, DY, JCM), pp. 255–262.
KDD-2016-SinghSA #independence #machine learning #using: Question Independent Grading using Machine Learning: The Case of Computer Program Grading (GS, SS, VA), pp. 263–272.
KDD-2016-SunYWXMZ: Contextual Intent Tracking for Personal Assistants (YS0, NJY, YW, XX0, KM, RZ0), pp. 273–282.
KDD-2016-TangLCA #empirical #feedback #multi #recommendation: An Empirical Study on Recommendation with Multiple Types of Feedback (LT, BL, BCC, DA), pp. 283–292.
KDD-2016-VanderveldPHP #e-commerce: An Engagement-Based Customer Lifetime Value System for E-commerce (AV, AP, AH, RP), pp. 293–302.
KDD-2016-WulczynKVHWBG #identification: Identifying Earmarks in Congressional Bills (EW, MK, VV, MH, JW, CB, RG), pp. 303–311.
KDD-2016-XuC #mobile #testing: Evaluating Mobile Apps with A/B and Quasi A/B Tests (YX, NC), pp. 313–322.
KDD-2016-YinHTDZOCKDNLC #ranking: Ranking Relevance in Yahoo Search (DY, YH, JT, TDJ, MZ, HO, JC, CK, HD, CN, JML, YC), pp. 323–332.
KDD-2016-YuCG #identification #network #social: Identifying Decision Makers from Professional Social Networks (SY, EC, AG), pp. 333–342.
KDD-2016-YueYCHZ #data analysis: Batch Model for Batched Timestamps Data Analysis with Application to the SSA Disability Program (QY, AY, XC, MH, CZ), pp. 343–352.
KDD-2016-ZhangYLXM #collaboration #knowledge base #recommendation: Collaborative Knowledge Base Embedding for Recommender Systems (FZ, NJY, DL, XX0, WYM), pp. 353–362.
KDD-2016-ZhangZMCZA #linear #modelling #named #predict #scalability: GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction (XZ, YZ, YM, BCC, LZ, DA), pp. 363–372.
KDD-2016-ZhaoATBDBKD #approach #detection #parametricity #strict #using: A Non-parametric Approach to Detect Epileptogenic Lesions using Restricted Boltzmann Machines (YZ, BA, TT, KEB, JGD, CEB, RK, OD), pp. 373–382.
KDD-2016-ZhuZXDX #analysis #modelling: Recruitment Market Trend Analysis with Sequential Latent Variable Models (CZ, HZ, HX, PD, FX), pp. 383–392.
KDD-2016-ZhuXTLGCF: Days on Market: Measuring Liquidity in Real Estate Markets (HZ, HX, FT, QL0, YG, EC, YF), pp. 393–402.
KDD-2016-Becher #education: Can You Teach the Elephant to Dance? AKA: Culture Eats Data Science for Breakfast (JDB), p. 403.
KDD-2016-Downs #how #machine learning: How Machine Learning has Finally Solved Wanamaker's Dilemma (OD), p. 405.
KDD-2016-Herbrich #learning #modelling #scalability: Learning Sparse Models at Scale (RH), p. 407.
KDD-2016-Law #behaviour #online #profiling #social: Profiling Users from Online Social Behaviors with Applications for Tencent Social Ads (CL), p. 409.
KDD-2016-Mierswa #machine learning #workflow: The Wisdom of Crowds: Best Practices for Data Prep & Machine Learning Derived from Millions of Data Science Workflows (IM), p. 411.
KDD-2016-Schneider #embedded #learning #optimisation: Bayesian Optimization and Embedded Learning Systems (JS), p. 413.
KDD-2016-Shapiro: Accelerating the Race to Autonomous Cars (DS), p. 415.
KDD-2016-Srivastava #machine learning #scalability #theory and practice: Large-Scale Machine Learning at Verizon: Theory and Applications (AS), p. 417.
KDD-2016-Watts #challenge #social: Computational Social Science: Exciting Progress and Future Challenges (DW), p. 419.
KDD-2016-AnCPS #approach #named: MAP: Frequency-Based Maximization of Airline Profits based on an Ensemble Forecasting Approach (BA, HC0, NP, VSS), pp. 421–430.
KDD-2016-BatraSW #energy #named: Gemello: Creating a Detailed Energy Breakdown from Just the Monthly Electricity Bill (NB0, AS0, KW), pp. 431–440.
KDD-2016-BorisyukKSZ #documentation #framework #learning #modelling #named #query: CaSMoS: A Framework for Learning Candidate Selection Models over Structured Queries and Documents (FB, KK, DS, BZ), pp. 441–450.
KDD-2016-ChidlovskiiCC #adaptation: Domain Adaptation in the Absence of Source Domain Data (BC, SC, GC), pp. 451–460.
KDD-2016-DingFC #assembly #named #reverse engineering: Kam1n0: MapReduce-based Assembly Clone Search for Reverse Engineering (SHHD, BCMF, PC), pp. 461–470.
KDD-2016-GeyikFSOK #metric #multi #online #optimisation #performance #video: Joint Optimization of Multiple Performance Metrics in Online Video Advertising (SCG, SF, JS, SO, SK), pp. 471–480.
KDD-2016-GuoLI #approximate #network: Convolutional Neural Networks for Steady Flow Approximation (XG, WL, FI), pp. 481–490.
KDD-2016-KuangTCPSP #self #using: Computational Drug Repositioning Using Continuous Self-Controlled Case Series (ZK, JAT, MC, PLP, RMS, DP), pp. 491–500.
KDD-2016-LiAHS #how #personalisation #ranking: How to Get Them a Dream Job?: Entity-Aware Features for Personalized Job Search Ranking (JL, DA, VHT, SS), pp. 501–510.
KDD-2016-LiMLFDYLQ #big data #data analysis #learning #performance #scalability #taxonomy: Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis (XL0, MM, BL, MSF, ID, JY, TL, SQ), pp. 511–519.
KDD-2016-LiuJM #industrial #named #normalisation #online: CompanyDepot: Employer Name Normalization in the Online Recruitment Industry (QL, FJ, MM), pp. 521–530.
KDD-2016-LoFL #behaviour #case study #comprehension: Understanding Behaviors that Lead to Purchasing: A Case Study of Pinterest (CL, DF, JL), pp. 531–540.
KDD-2016-LynchAA #image #learning #multimodal #rank #scalability #semantics #visual notation: Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank (CL, KA, JA), pp. 541–548.
KDD-2016-NguyenP #mining: Text Mining in Clinical Domain: Dealing with Noise (HN, JP), pp. 549–558.
KDD-2016-PaparrizosWH #detection: Detecting Devastating Diseases in Search Logs (JP, RWW, EH), pp. 559–568.
KDD-2016-PerozziSST #network #recommendation: When Recommendation Goes Wrong: Anomalous Link Discovery in Recommendation Networks (BP, MS, JS, MT), pp. 569–578.
KDD-2016-PivarskiBG: Deploying Analytics with the Portable Format for Analytics (PFA) (JP, CB, RLG), pp. 579–588.
KDD-2016-PoonawalaKBWS #data analysis #mobile: Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics (HP, VK, SB, LW, SS), pp. 589–598.
KDD-2016-SarafR #automation: EMBERS AutoGSR: Automated Coding of Civil Unrest Events (PS, NR), pp. 599–608.
KDD-2016-TaghaviLK #machine learning #memory management #recommendation #using: Compute Job Memory Recommender System Using Machine Learning (TT, ML, YK), pp. 609–616.
KDD-2016-TanFLWLLPXH #adaptation #algorithm #predict #scalability: Scalable Time-Decaying Adaptive Prediction Algorithm (YT, ZF, GL, FW, ZL, SL, QP, EPX, QH), pp. 617–626.
KDD-2016-HaarenSDF #machine learning #using: Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques (JVH, HBS, JD, PF), pp. 627–634.
KDD-2016-WangKGL #big data: Crime Rate Inference with Big Data (HW0, DK, CG, ZL), pp. 635–644.
KDD-2016-XieA #case study #online: Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix (HX, JA), pp. 645–654.
KDD-2016-XuYYXZ #detection #network: Talent Circle Detection in Job Transition Networks (HX, ZY0, JY, HX, HZ), pp. 655–664.
KDD-2016-ZhangZWX #learning: Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising (WZ0, TZ, JW0, JX), pp. 665–674.
KDD-2016-AkibaY #graph #scalability #sketching: Compact and Scalable Graph Neighborhood Sketching (TA, YY), pp. 685–694.
KDD-2016-AmoualianCGA #approach #dependence #documentation #modelling #named #topic: Streaming-LDA: A Copula-based Approach to Modeling Topic Dependencies in Document Streams (HA, MC, ÉG, MRA), pp. 695–704.
KDD-2016-AndersonKM #benchmark #fault #metric: Assessing Human Error Against a Benchmark of Perfection (AA, JMK, SM), pp. 705–714.
KDD-2016-ArbourGJ #network: Inferring Network Effects from Observational Data (DTA, DG, DDJ), pp. 715–724.
KDD-2016-BalcanLSW0 #analysis #communication #component #distributed #kernel #performance: Communication Efficient Distributed Kernel Principal Component Analysis (MFB, YL, LS, DPW, BX0), pp. 725–734.
KDD-2016-BertensVS #multi #sequence: Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns (RB, JV, AS), pp. 735–744.
KDD-2016-Bressan0PRT #recommendation #social: The Limits of Popularity-Based Recommendations, and the Role of Social Ties (MB0, SL0, AP, PR, ET), pp. 745–754.
KDD-2016-ChangZTYCHH #learning #network #streaming: Positive-Unlabeled Learning in Streaming Networks (SC, YZ0, JT, DY, YC, MAHJ, TSH), pp. 755–764.
KDD-2016-ChenTXYH #dependence #multi #named #network #performance: FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks (CC0, HT, LX, LY, QH), pp. 765–774.
KDD-2016-ChenJ #predict: Predicting Matchups and Preferences in Context (SC0, TJ), pp. 775–784.
KDD-2016-ChenG #named #scalability: XGBoost: A Scalable Tree Boosting System (TC, CG), pp. 785–794.
KDD-2016-ChenLTZZ #robust: Robust Influence Maximization (WC, TL, ZT, MZ, XZ), pp. 795–804.
KDD-2016-ChengZCJCW #analysis #correlation #ranking: Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations (WC, KZ0, HC, GJ, ZC, WW0), pp. 805–814.
KDD-2016-Christakopoulou #recommendation #towards: Towards Conversational Recommender Systems (KC, FR, KH), pp. 815–824.
KDD-2016-StefaniERU #memory management #named: TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size (LDS, AE, MR, EU), pp. 825–834.
KDD-2016-FowkesS #mining #sequence: A Subsequence Interleaving Model for Sequential Pattern Mining (JMF, CAS), pp. 835–844.
KDD-2016-GhashamiLP #algorithm #matrix #performance: Efficient Frequent Directions Algorithm for Sparse Matrices (MG, EL, JMP), pp. 845–854.
KDD-2016-GroverL #learning #named #network #scalability: node2vec: Scalable Feature Learning for Networks (AG, JL), pp. 855–864.
KDD-2016-HanZWZ #identification #interactive: Generalized Hierarchical Sparse Model for Arbitrary-Order Interactive Antigenic Sites Identification in Flu Virus Data (LH0, YZ0, XFW, TZ0), pp. 865–874.
KDD-2016-HeLMCSY #community #composition #detection: Joint Community and Structural Hole Spanner Detection via Harmonic Modularity (LH0, CTL, JM, JC, LS, PSY), pp. 875–884.
KDD-2016-HeK #robust: Robust Influence Maximization (XH, DK0), pp. 885–894.
KDD-2016-HooiSBSSF #bound #graph #named: FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (BH, HAS, AB, NS, KS, CF), pp. 895–904.
KDD-2016-HuVQ #detection #performance: Temporal Order-based First-Take-All Hashing for Fast Attention-Deficit-Hyperactive-Disorder Detection (HH, JVG, GJQ), pp. 905–914.
KDD-2016-HungSYHLPC #social: When Social Influence Meets Item Inference (HJH, HHS, DNY, LHH, WCL, JP, MSC), pp. 915–924.
KDD-2016-IyerNS #estimation #privacy: Privacy-preserving Class Ratio Estimation (ASI, JSN, SS), pp. 925–934.
KDD-2016-JainPV #multi #ranking #recommendation: Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications (HJ, YP, MV), pp. 935–944.
KDD-2016-JiangFH #behaviour #multi #named #representation: CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors (MJ0, CF, JH0), pp. 945–954.
KDD-2016-KannanKRKTMCLGY #automation #email: Smart Reply: Automated Response Suggestion for Email (AK, KK, SR, TK, AT, BM, GC, LL, MG, PY, VR), pp. 955–964.
KDD-2016-Lemmerich0SHHS #behaviour #mining: Mining Subgroups with Exceptional Transition Behavior (FL, MB0, PS, DH, AH, MS), pp. 965–974.
KDD-2016-LiGHZ #learning #recommendation: Point-of-Interest Recommendations: Learning Potential Check-ins from Friends (HL, YG, RH, HZ), pp. 975–984.
KDD-2016-Li0TFT #named #network: QUINT: On Query-Specific Optimal Networks (LL, YY0, JT0, WF0, HT), pp. 985–994.
KDD-2016-LiangYK #clustering #documentation #streaming: Dynamic Clustering of Streaming Short Documents (SL, EY, EK), pp. 995–1004.
KDD-2016-LiuSCX #multi #optimisation: Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization (JL, LS, WC0, HX), pp. 1005–1014.
KDD-2016-LiuLLQX #assessment #recommendation: Unified Point-of-Interest Recommendation with Temporal Interval Assessment (YL, CL, BL0, MQ, HX), pp. 1015–1024.
KDD-2016-MaiAS #algorithm #clustering #dataset #named #performance #scalability: AnyDBC: An Efficient Anytime Density-based Clustering Algorithm for Very Large Complex Datasets (STM, IA, MS), pp. 1025–1034.
KDD-2016-ManzoorMA #detection #graph #performance #streaming: Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs (EAM, SMM, LA), pp. 1035–1044.
KDD-2016-MatsubaraS #co-evolution #realtime #sequence: Regime Shifts in Streams: Real-time Forecasting of Co-evolving Time Sequences (YM, YS), pp. 1045–1054.
KDD-2016-MaurusP #clustering #named: Skinny-dip: Clustering in a Sea of Noise (SM, CP), pp. 1055–1064.
KDD-2016-MelnykBMO #detection #markov #modelling: Semi-Markov Switching Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems (IM, AB, BLM, NCO), pp. 1065–1074.
KDD-2016-MukherjeeGW: Continuous Experience-aware Language Model (SM, SG, GW), pp. 1075–1084.
KDD-2016-NandanwarM #classification #network: Structural Neighborhood Based Classification of Nodes in a Network (SN, MNM), pp. 1085–1094.
KDD-2016-NingMRR #learning #modelling #multi: Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning (YN, SM, HR, NR), pp. 1095–1104.
KDD-2016-OuCPZ0 #graph #symmetry #transitive: Asymmetric Transitivity Preserving Graph Embedding (MO, PC0, JP, ZZ, WZ0), pp. 1105–1114.
KDD-2016-ParkMK #distributed #named: PTE: Enumerating Trillion Triangles On Distributed Systems (HMP, SHM, UK), pp. 1115–1124.
KDD-2016-RendleFSS #in the cloud #machine learning #robust #scalability: Robust Large-Scale Machine Learning in the Cloud (SR, DF, EJS, BYS), pp. 1125–1134.
KDD-2016-Ribeiro0G #classification #predict #quote #trust #why: “Why Should I Trust You?”: Explaining the Predictions of Any Classifier (MTR, SS0, CG), pp. 1135–1144.
KDD-2016-RiondatoU #approximate #graph #named: ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages (MR, EU), pp. 1145–1154.
KDD-2016-Robles-GrandaMN #generative #modelling #network: Sampling of Attributed Networks from Hierarchical Generative Models (PRG, SM, JN), pp. 1155–1164.
KDD-2016-SiCHRD #induction #matrix: Goal-Directed Inductive Matrix Completion (SS, KYC, CJH, NR, ISD), pp. 1165–1174.
KDD-2016-SilvaDBSS #graph: Graph Wavelets via Sparse Cuts (AS, XHD, PB, AKS, AS), pp. 1175–1184.
KDD-2016-SiyariDD #framework #named #optimisation: Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data (PS, BD, CD), pp. 1185–1194.
KDD-2016-Ting #estimation #sketching #towards: Towards Optimal Cardinality Estimation of Unions and Intersections with Sketches (DT), pp. 1195–1204.
KDD-2016-TingZCZZ #difference #using: Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure (KMT, YZ, MJC, YZ, ZHZ), pp. 1205–1214.
KDD-2016-TrouleauADE #behaviour #modelling: Just One More: Modeling Binge Watching Behavior (WT, AA, WD, BE), pp. 1215–1224.
KDD-2016-WangC0 #network: Structural Deep Network Embedding (DW, PC0, WZ0), pp. 1225–1234.
KDD-2016-WangCF0E #analysis #modelling #topic: Targeted Topic Modeling for Focused Analysis (SW, ZC0, GF, BL0, SE), pp. 1235–1244.
KDD-2016-WangNH #clustering #graph #matrix #probability: Structured Doubly Stochastic Matrix for Graph Based Clustering: Structured Doubly Stochastic Matrix (XW, FN, HH), pp. 1245–1254.
KDD-2016-WebbP #multi #statistics #testing: A Multiple Test Correction for Streams and Cascades of Statistical Hypothesis Tests (GIW, FP), pp. 1255–1264.
KDD-2016-WuYCY #convergence #performance #random: Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability (LW, IEHY, JC, RY), pp. 1265–1274.
KDD-2016-XuT0 #learning #multi #robust: Robust Extreme Multi-label Learning (CX0, DT, CX0), pp. 1275–1284.
KDD-2016-XuZZLZCX #analysis #behaviour #network #perspective #social: Taxi Driving Behavior Analysis in Latent Vehicle-to-Vehicle Networks: A Social Influence Perspective (TX, HZ, XZ, QL0, HZ, EC, HX), pp. 1285–1294.
KDD-2016-ZhaiCZZ #learning #named #network #online: DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks (SZ, KhC, RZ, Z(Z), pp. 1295–1304.
KDD-2016-ZhangZYZHH #modelling #named #social #social media #using: GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media (CZ0, KZ, QY0, LZ, TH, JH0), pp. 1305–1314.
KDD-2016-ZhangLG #approximate #graph #personalisation #rank: Approximate Personalized PageRank on Dynamic Graphs (HZ, PL, AG), pp. 1315–1324.
KDD-2016-ZhangZCWCCJQY #adaptation: Annealed Sparsity via Adaptive and Dynamic Shrinking (KZ0, SZ, CC, ZW, ZC, HC, GJ, YQ, JY), pp. 1325–1334.
KDD-2016-ZhangZL #ambiguity #learning: Partial Label Learning via Feature-Aware Disambiguation (MLZ, BBZ, XYL), pp. 1335–1344.
KDD-2016-ZhangT #named #network #performance: FINAL: Fast Attributed Network Alignment (SZ, HT), pp. 1345–1354.
KDD-2016-ZhangCFLY0Y #evolution: Come-and-Go Patterns of Group Evolution: A Dynamic Model (TZ, PC0, CF, YL, HY, WZ0, SY), pp. 1355–1364.
KDD-2016-ZhangXKZ #evolution #named #network: NetCycle: Collective Evolution Inference in Heterogeneous Information Networks (YZ, YX, XK, YZ), pp. 1365–1374.
KDD-2016-ZhouVBJD #higher-order #online: Accelerating Online CP Decompositions for Higher Order Tensors (SZ0, XVN, JB0, YJ, ID), pp. 1375–1384.
KDD-2016-LisbonaCL #empirical #online #video: Optimal Reserve Prices in Upstream Auctions: Empirical Application on Online Video Advertising (MAAL, SC, KcL), pp. 1395–1404.
KDD-2016-AlvesAM #random: Burstiness Scale: A Parsimonious Model for Characterizing Random Series of Events (RAdSA, RMA, POSVdM), pp. 1405–1414.
KDD-2016-BanerjeeYR #approach #mining #named #scalability: MANTRA: A Scalable Approach to Mining Temporally Anomalous Sub-trajectories (PB, PY, SR), pp. 1415–1424.
KDD-2016-BaoWL #approach #data-driven #network #predict #resource management: From Prediction to Action: A Closed-Loop Approach for Data-Guided Network Resource Allocation (YB, HW, XL0), pp. 1425–1434.
KDD-2016-BorboudakisT #robust #towards: Towards Robust and Versatile Causal Discovery for Business Applications (GB, IT), pp. 1435–1444.
KDD-2016-CaoLWYY #retrieval #semantics: Deep Visual-Semantic Hashing for Cross-Modal Retrieval (YC0, ML, JW0, QY0, PSY), pp. 1445–1454.
KDD-2016-ChakrabortyVJS #predict #using: Predicting Socio-Economic Indicators using News Events (SC, AV, SJ, LS), pp. 1455–1464.
KDD-2016-ChenLHYGG #using: City-Scale Map Creation and Updating using GPS Collections (CC0, CL, QH, QY0, DG, LJG), pp. 1465–1474.
KDD-2016-ChenWTWC #network: Compressing Convolutional Neural Networks in the Frequency Domain (WC, JTW, ST, KQW, YC), pp. 1475–1484.
KDD-2016-ChiangLL #classification #coordination #linear #manycore #parallel #scalability: Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in Multi-core Environments (WLC, MCL, CJL), pp. 1485–1494.
KDD-2016-ChoiBSCTBTS #concept #learning #multi #representation: Multi-layer Representation Learning for Medical Concepts (EC, MTB, ES, CC, MT, JB, JTS, JS), pp. 1495–1504.
KDD-2016-ChuWPWZC #network: Finding Gangs in War from Signed Networks (LC, ZW, JP, JW, ZZ, EC), pp. 1505–1514.
KDD-2016-CoskunGK #network #performance #proximity #query: Efficient Processing of Network Proximity Queries via Chebyshev Acceleration (MC, AG, MK), pp. 1515–1524.
KDD-2016-DengSDZYL #network #predict: Latent Space Model for Road Networks to Predict Time-Varying Traffic (DD, CS, UD, LZ, RY, YL0), pp. 1525–1534.
KDD-2016-DhulipalaKKOPS #graph #recursion: Compressing Graphs and Indexes with Recursive Graph Bisection (LD, IK, BK, GO, SP, AS), pp. 1535–1544.
KDD-2016-ReisFMB #detection #incremental #online #performance #using: Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test (DMdR, PAF, SM, GEAPAB), pp. 1545–1554.
KDD-2016-DuDTUGS #process: Recurrent Marked Temporal Point Processes: Embedding Event History to Vector (ND, HD, RT, UU, MGR, LS), pp. 1555–1564.
KDD-2016-FeiW0 #cumulative #information management #learning: Learning Cumulatively to Become More Knowledgeable (GF, SW, BL0), pp. 1565–1574.
KDD-2016-GaoP #dataset #named #relational: Squish: Near-Optimal Compression for Archival of Relational Datasets (YG, AGP), pp. 1575–1584.
KDD-2016-HanZZ #component #estimation #performance #scalability: Fast Component Pursuit for Large-Scale Inverse Covariance Estimation (LH0, YZ0, TZ0), pp. 1585–1594.
KDD-2016-HuangZCSML #network #scalability: Meta Structure: Computing Relevance in Large Heterogeneous Information Networks (ZH0, YZ, RC, YS, NM, XL), pp. 1595–1604.
KDD-2016-HuoNH #effectiveness #learning #metric #robust #using: Robust and Effective Metric Learning Using Capped Trace Norm: Metric Learning via Capped Trace Norm (ZH, FN, HH), pp. 1605–1614.
KDD-2016-KangLSB #analysis #component: Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations (BK, JL, RSR, TDB), pp. 1615–1624.
KDD-2016-KarLNC0 #online #optimisation #problem #quantifier: Online Optimization Methods for the Quantification Problem (PK, SL, HN, SC, FS0), pp. 1625–1634.
KDD-2016-KarimiTFSG #question: Smart Broadcasting: Do You Want to be Seen? (MRK, ET, MF, LS, MGR), pp. 1635–1644.
KDD-2016-KimMJO #how #modelling #online #word: How to Compete Online for News Audience: Modeling Words that Attract Clicks (JHK, AM, AJ, AHO), pp. 1645–1654.
KDD-2016-KummerfeldR #clustering #metric #modelling: Causal Clustering for 1-Factor Measurement Models (EK, JR), pp. 1655–1664.
KDD-2016-LabutovSLLS #data-driven #design #modelling #set: Optimally Discriminative Choice Sets in Discrete Choice Models: Application to Data-Driven Test Design (IL, FS, KL, HL, CS), pp. 1665–1674.
KDD-2016-LakkarajuBL #framework #predict #set: Interpretable Decision Sets: A Joint Framework for Description and Prediction (HL, SHB, JL), pp. 1675–1684.
KDD-2016-LazersonKS #distributed #lightweight #monitoring: Lightweight Monitoring of Distributed Streams (AL, DK, AS), pp. 1685–1694.
KDD-2016-LethamLR #behaviour #transaction: Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts (BL, LML, CR), pp. 1695–1704.
KDD-2016-LiQJTYW #big data #optimisation #parallel: Parallel Lasso Screening for Big Data Optimization (QL, SQ, SJ, PMT, JY, JW0), pp. 1705–1714.
KDD-2016-LiWYR #analysis #learning #multi: A Multi-Task Learning Formulation for Survival Analysis (YL, JW0, JY, CKR), pp. 1715–1724.
KDD-2016-LiLDWWC #algorithm #linear #parallel #sequence: A Real Linear and Parallel Multiple Longest Common Subsequences (MLCS) Algorithm (YL, HL, TD, SW, ZW, YC), pp. 1725–1734.
KDD-2016-LinXBJZ #feature model #interactive #learning #multi: Multi-Task Feature Interaction Learning (KL, JX, IMB, SJ, JZ), pp. 1735–1744.
KDD-2016-LiuSLF #clustering #image #infinity: Infinite Ensemble for Image Clustering (HL, MS, SL0, YF0), pp. 1745–1754.
KDD-2016-MaccioniA #graph #pattern matching #scalability: Scalable Pattern Matching over Compressed Graphs via Dedensification (AM, DJA), pp. 1755–1764.
KDD-2016-MahmoodyTU #scalability: Scalable Betweenness Centrality Maximization via Sampling (AM, CET, EU), pp. 1765–1773.
KDD-2016-MuZLXWZ #modelling: User Identity Linkage by Latent User Space Modelling (XM, FZ0, EPL, JX, JW, ZHZ), pp. 1775–1784.
KDD-2016-NakagawaSKTT #approach #mining #performance #predict: Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining (KN, SS, MK, KT, IT), pp. 1785–1794.
KDD-2016-NieGY #multi #predict: Predict Risk of Relapse for Patients with Multiple Stages of Treatment of Depression (ZN, PG, JY), pp. 1795–1804.
KDD-2016-OmariKYS #layout #web: Lossless Separation of Web Pages into Layout Code and Data (AO, BK, EY, SS), pp. 1805–1814.
KDD-2016-ReddyLBJ #bound #learning #scheduling: Unbounded Human Learning: Optimal Scheduling for Spaced Repetition (SR, IL, SB, TJ), pp. 1815–1824.
KDD-2016-RenHQVJH #reduction #type system: Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding (XR, WH, MQ, CRV, HJ, JH0), pp. 1825–1834.
KDD-2016-RozenshteinGPV: Reconstructing an Epidemic Over Time (PR, AG, BAP, JV), pp. 1835–1844.
KDD-2016-ShiASWM #overview: Improving Survey Aggregation with Sparsely Represented Signals (TS, FA, MS, DPW, TM), pp. 1845–1854.
KDD-2016-Shi0CTGR #multi #network #scalability #social: Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay (YS, MK0, SC, MT, SG, RR), pp. 1855–1864.
KDD-2016-SunLGXX #automation #data-driven #development #recommendation: Data-driven Automatic Treatment Regimen Development and Recommendation (LS, CL, CG, HX, YX), pp. 1865–1874.
KDD-2016-TabeiSYP #matrix #scalability: Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices (YT, HS, YY, SJP), pp. 1875–1884.
KDD-2016-WanCKHGZ #approach #modelling: From Truth Discovery to Trustworthy Opinion Discovery: An Uncertainty-Aware Quantitative Modeling Approach (MW, XC, LMK, JH0, JG0, BZ0), pp. 1885–1894.
KDD-2016-WangELBZGC #challenge #email #recommendation: The Million Domain Challenge: Broadcast Email Prioritization by Cross-domain Recommendation (BW, ME, YL, JB, YZ, ZG, DC), pp. 1895–1904.
KDD-2016-WeiZ0: Transfer Knowledge between Cities (YW, YZ0, QY0), pp. 1905–1914.
KDD-2016-WuMSZZCW #probability #robust: Probabilistic Robust Route Recovery with Spatio-Temporal Dynamics (HW0, JM, WS, BZ, HZ, ZC, WW0), pp. 1915–1924.
KDD-2016-XiaoGWWSL #approach: A Truth Discovery Approach with Theoretical Guarantee (HX, JG0, ZW, SW, LS, HL0), pp. 1925–1934.
KDD-2016-XiaoGLMSFZ #approach #towards: Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach (HX, JG0, QL0, FM, LS, YF, AZ), pp. 1935–1944.
KDD-2016-YangFK0 #algorithm #feature model #online #parallel: Online Feature Selection: A Limited-Memory Substitution Algorithm and Its Asynchronous Parallel Variation (HY, RF, YK, JL0), pp. 1945–1954.
KDD-2016-YangLGZSY: Absolute Fused Lasso and Its Application to Genome-Wide Association Studies (TY0, JL0, PG, RZ, XS, JY), pp. 1955–1964.
KDD-2016-YangYWCZL #mining: Diversified Temporal Subgraph Pattern Mining (YY, DY0, HW, JC, SZ, JCSL), pp. 1965–1974.
KDD-2016-Yang0Z #probability #scalability: Distributing the Stochastic Gradient Sampler for Large-Scale LDA (YY, JC0, JZ0), pp. 1975–1984.
KDD-2016-YeGPB #clustering #named: FUSE: Full Spectral Clustering (WY0, SG, CP, CB), pp. 1985–1994.
KDD-2016-YinW #algorithm #clustering #online #using: A Text Clustering Algorithm Using an Online Clustering Scheme for Initialization (JY, JW), pp. 1995–2004.
KDD-2016-YuanYZH #approximate #difference #linear #optimisation #privacy #query: Convex Optimization for Linear Query Processing under Approximate Differential Privacy (GY, YY, ZZ, ZH), pp. 2005–2014.
KDD-2016-ZangCF #network #social: Beyond Sigmoids: The NetTide Model for Social Network Growth, and Its Applications (CZ, PC0, CF), pp. 2015–2024.
KDD-2016-ZengWML #multi #online #recommendation: Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit (CZ, QW, SM, TL0), pp. 2025–2034.
KDD-2016-ZhangG #coordination #probability: Accelerated Stochastic Block Coordinate Descent with Optimal Sampling (AZ, QG), pp. 2035–2044.
KDD-2016-ZhangWZWLSJ #collaboration #multi: Collaborative Multi-View Denoising (LZ, SW, XZ0, YW, BL, DS, SJ), pp. 2045–2054.
KDD-2016-ZhangYS #learning #online #symmetry: Online Asymmetric Active Learning with Imbalanced Data (XZ, TY, PS), pp. 2055–2064.
KDD-2016-ZhangBSS #named #optimisation #performance #pipes and filters: FLASH: Fast Bayesian Optimization for Data Analytic Pipelines (YZ, MTB, HS, JS), pp. 2065–2074.
KDD-2016-ZhaoLWGC #multi #perspective: Portfolio Selections in P2P Lending: A Multi-Objective Perspective (HZ, QL0, GW, YG, EC), pp. 2075–2084.
KDD-2016-ZhaoYCLR #learning #multi: Hierarchical Incomplete Multi-source Feature Learning for Spatiotemporal Event Forecasting (LZ0, JY, FC0, CTL, NR), pp. 2085–2094.
KDD-2016-ZhengYC #invariant #learning #performance #taxonomy: Efficient Shift-Invariant Dictionary Learning (GZ, YY, JGC), pp. 2095–2104.
KDD-2016-ZuoWZLWXX #modelling #perspective #pseudo #topic: Topic Modeling of Short Texts: A Pseudo-Document View (YZ, JW, HZ0, HL, FW, KX0, HX), pp. 2105–2114.
KDD-2016-AgostaGHIKZ #clustering #data analysis #scalability #using: Scalable Data Analytics Using R: Single Machines to Hadoop Spark Clusters (JMA, DG, RH, MI, SK, MZ), p. 2115.
KDD-2016-ChenH0 #machine learning #web: Lifelong Machine Learning and Computer Reading the Web (ZC0, ERHJ, BL0), pp. 2117–2118.
KDD-2016-MoralesBKGF #big data #data type #mining: IoT Big Data Stream Mining (GDFM, AB, LK, JG, WF0), pp. 2119–2120.
KDD-2016-GaoLZFH #crowdsourcing #mining #reliability: Mining Reliable Information from Passively and Actively Crowdsourced Data (JG0, QL0, BZ0, WF0, JH0), pp. 2121–2122.
KDD-2016-GuptaA #streaming: Streaming Analytics (AG, NA), p. 2123.
KDD-2016-HajianBC #algorithm #bias #data mining #mining: Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining (SH, FB, CC0), pp. 2125–2126.
KDD-2016-HuLL #predict #profiling #social: Collective Sensemaking via Social Sensors: Extracting, Profiling, Analyzing, and Predicting Real-world Events (YH, YRL, JL), pp. 2127–2128.
KDD-2016-MueenK #performance: Extracting Optimal Performance from Dynamic Time Warping (AM, EJK), pp. 2129–2130.
KDD-2016-PetitjeanW #learning #modelling #scalability #visual notation: Scalable Learning of Graphical Models (FP, GIW), pp. 2131–2132.
KDD-2016-PrakashR #algorithm #data mining #mining #modelling: Leveraging Propagation for Data Mining: Models, Algorithms and Applications (BAP, NR), pp. 2133–2134.
KDD-2016-SeideA #named #open source #tool support: CNTK: Microsoft's Open-Source Deep-Learning Toolkit (FS, AA), p. 2135.
KDD-2016-WangZD #data mining #matrix #mining #modelling: Healthcare Data Mining with Matrix Models (FW0, PZ0, JD), pp. 2137–2138.
KDD-2016-0002GOL #modelling #predict #scalability: Business Applications of Predictive Modeling at Scale (QZ0, SG, PO, YL), pp. 2139–2140.

Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, Rajeev RastogiProceedings of the 22nd International Conference on Knowledge Discovery and Data MiningKDD, 2016.

Contents (236 items)

Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, Rajeev Rastogi
Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining
KDD, 2016.