SIGKDD(KDD) 2016 论文列表
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016.
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Business Applications of Predictive Modeling at Scale.
Healthcare Data Mining with Matrix Models.
CNTK: Microsoft's Open-Source Deep-Learning Toolkit.
Leveraging Propagation for Data Mining: Models, Algorithms and Applications.
Scalable Learning of Graphical Models.
Extracting Optimal Performance from Dynamic Time Warping.
Collective Sensemaking via Social Sensors: Extracting, Profiling, Analyzing, and Predicting Real-world Events.
Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining.
Streaming Analytics.
Mining Reliable Information from Passively and Actively Crowdsourced Data.
IoT Big Data Stream Mining.
Lifelong Machine Learning and Computer Reading the Web.
Scalable Data Analytics Using R: Single Machines to Hadoop Spark Clusters.
Topic Modeling of Short Texts: A Pseudo-Document View.
Efficient Shift-Invariant Dictionary Learning.
Hierarchical Incomplete Multi-source Feature Learning for Spatiotemporal Event Forecasting.
Portfolio Selections in P2P Lending: A Multi-Objective Perspective.
FLASH: Fast Bayesian Optimization for Data Analytic Pipelines.
Online Asymmetric Active Learning with Imbalanced Data.
Collaborative Multi-View Denoising.
Accelerated Stochastic Block Coordinate Descent with Optimal Sampling.
Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit.
Beyond Sigmoids: The NetTide Model for Social Network Growth, and Its Applications.
Convex Optimization for Linear Query Processing under Approximate Differential Privacy.
A Text Clustering Algorithm Using an Online Clustering Scheme for Initialization.
FUSE: Full Spectral Clustering.
Distributing the Stochastic Gradient Sampler for Large-Scale LDA.
Diversified Temporal Subgraph Pattern Mining.
Absolute Fused Lasso and Its Application to Genome-Wide Association Studies.
Online Feature Selection: A Limited-Memory Substitution Algorithm and Its Asynchronous Parallel Variation.
Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach.
A Truth Discovery Approach with Theoretical Guarantee.
Probabilistic Robust Route Recovery with Spatio-Temporal Dynamics.
Transfer Knowledge between Cities.
The Million Domain Challenge: Broadcast Email Prioritization by Cross-domain Recommendation.
From Truth Discovery to Trustworthy Opinion Discovery: An Uncertainty-Aware Quantitative Modeling Approach.
Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices.
Data-driven Automatic Treatment Regimen Development and Recommendation.
Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay.
Improving Survey Aggregation with Sparsely Represented Signals.
Reconstructing an Epidemic Over Time.
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding.
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition.
Lossless Separation of Web Pages into Layout Code and Data.
Predict Risk of Relapse for Patients with Multiple Stages of Treatment of Depression.
Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining.
User Identity Linkage by Latent User Space Modelling.
Scalable Betweenness Centrality Maximization via Sampling.
Scalable Pattern Matching over Compressed Graphs via Dedensification.
Infinite Ensemble for Image Clustering.
Multi-Task Feature Interaction Learning.
A Real Linear and Parallel Multiple Longest Common Subsequences (MLCS) Algorithm.
A Multi-Task Learning Formulation for Survival Analysis.
Parallel Lasso Screening for Big Data Optimization.
Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts.
Lightweight Monitoring of Distributed Streams.
Interpretable Decision Sets: A Joint Framework for Description and Prediction.
Optimally Discriminative Choice Sets in Discrete Choice Models: Application to Data-Driven Test Design.
Causal Clustering for 1-Factor Measurement Models.
How to Compete Online for News Audience: Modeling Words that Attract Clicks.
Smart Broadcasting: Do You Want to be Seen?
Online Optimization Methods for the Quantification Problem.
Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations.
Robust and Effective Metric Learning Using Capped Trace Norm: Metric Learning via Capped Trace Norm.
Meta Structure: Computing Relevance in Large Heterogeneous Information Networks.
Fast Component Pursuit for Large-Scale Inverse Covariance Estimation.
Squish: Near-Optimal Compression for Archival of Relational Datasets.
Learning Cumulatively to Become More Knowledgeable.
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector.
Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test.
Compressing Graphs and Indexes with Recursive Graph Bisection.
Latent Space Model for Road Networks to Predict Time-Varying Traffic.
Efficient Processing of Network Proximity Queries via Chebyshev Acceleration.
Finding Gangs in War from Signed Networks.
Multi-layer Representation Learning for Medical Concepts.
Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in Multi-core Environments.
Compressing Convolutional Neural Networks in the Frequency Domain.
City-Scale Map Creation and Updating using GPS Collections.
Predicting Socio-Economic Indicators using News Events.
Deep Visual-Semantic Hashing for Cross-Modal Retrieval.
Towards Robust and Versatile Causal Discovery for Business Applications.
From Prediction to Action: A Closed-Loop Approach for Data-Guided Network Resource Allocation.
MANTRA: A Scalable Approach to Mining Temporally Anomalous Sub-trajectories.
Burstiness Scale: A Parsimonious Model for Characterizing Random Series of Events.
Optimal Reserve Prices in Upstream Auctions: Empirical Application on Online Video Advertising.
Accelerating Online CP Decompositions for Higher Order Tensors.
NetCycle: Collective Evolution Inference in Heterogeneous Information Networks.
Come-and-Go Patterns of Group Evolution: A Dynamic Model.
FINAL: Fast Attributed Network Alignment.
Partial Label Learning via Feature-Aware Disambiguation.
Annealed Sparsity via Adaptive and Dynamic Shrinking.
Approximate Personalized PageRank on Dynamic Graphs.
GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media.
DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks.
Taxi Driving Behavior Analysis in Latent Vehicle-to-Vehicle Networks: A Social Influence Perspective.
Robust Extreme Multi-label Learning.
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability.
A Multiple Test Correction for Streams and Cascades of Statistical Hypothesis Tests.
Structured Doubly Stochastic Matrix for Graph Based Clustering: Structured Doubly Stochastic Matrix.
Targeted Topic Modeling for Focused Analysis.
Structural Deep Network Embedding.
Just One More: Modeling Binge Watching Behavior.
Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure.
Towards Optimal Cardinality Estimation of Unions and Intersections with Sketches.
Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data.
Graph Wavelets via Sparse Cuts.
Goal-Directed Inductive Matrix Completion.
Sampling of Attributed Networks from Hierarchical Generative Models.
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier.
Robust Large-Scale Machine Learning in the Cloud.
PTE: Enumerating Trillion Triangles On Distributed Systems.
Asymmetric Transitivity Preserving Graph Embedding.
Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning.
Structural Neighborhood Based Classification of Nodes in a Network.
Continuous Experience-aware Language Model.
Semi-Markov Switching Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems.
Skinny-dip: Clustering in a Sea of Noise.
Regime Shifts in Streams: Real-time Forecasting of Co-evolving Time Sequences.
Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs.
AnyDBC: An Efficient Anytime Density-based Clustering Algorithm for Very Large Complex Datasets.
Unified Point-of-Interest Recommendation with Temporal Interval Assessment.
Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization.
Dynamic Clustering of Streaming Short Documents.
QUINT: On Query-Specific Optimal Networks.
Point-of-Interest Recommendations: Learning Potential Check-ins from Friends.
Mining Subgroups with Exceptional Transition Behavior.
Smart Reply: Automated Response Suggestion for Email.
CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors.
Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications.
Privacy-preserving Class Ratio Estimation.
When Social Influence Meets Item Inference.
Temporal Order-based First-Take-All Hashing for Fast Attention-Deficit-Hyperactive-Disorder Detection.
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage.
Robust Influence Maximization.
Joint Community and Structural Hole Spanner Detection via Harmonic Modularity.
Generalized Hierarchical Sparse Model for Arbitrary-Order Interactive Antigenic Sites Identification in Flu Virus Data.
node2vec: Scalable Feature Learning for Networks.
Efficient Frequent Directions Algorithm for Sparse Matrices.
A Subsequence Interleaving Model for Sequential Pattern Mining.
TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size.
Towards Conversational Recommender Systems.
Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations.
Robust Influence Maximization.
XGBoost: A Scalable Tree Boosting System.
Predicting Matchups and Preferences in Context.
FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks.
Positive-Unlabeled Learning in Streaming Networks.
The Limits of Popularity-Based Recommendations, and the Role of Social Ties.
Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns.
Communication Efficient Distributed Kernel Principal Component Analysis.
Inferring Network Effects from Observational Data.
Assessing Human Error Against a Benchmark of Perfection.
Streaming-LDA: A Copula-based Approach to Modeling Topic Dependencies in Document Streams.
Compact and Scalable Graph Neighborhood Sketching.
Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising.
Talent Circle Detection in Job Transition Networks.
Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix.
Crime Rate Inference with Big Data.
Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques.
Scalable Time-Decaying Adaptive Prediction Algorithm.
Compute Job Memory Recommender System Using Machine Learning.
EMBERS AutoGSR: Automated Coding of Civil Unrest Events.
Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics.
Deploying Analytics with the Portable Format for Analytics (PFA).
When Recommendation Goes Wrong: Anomalous Link Discovery in Recommendation Networks.
Detecting Devastating Diseases in Search Logs.
Text Mining in Clinical Domain: Dealing with Noise.
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank.
Understanding Behaviors that Lead to Purchasing: A Case Study of Pinterest.
CompanyDepot: Employer Name Normalization in the Online Recruitment Industry.
Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis.
How to Get Them a Dream Job?: Entity-Aware Features for Personalized Job Search Ranking.
Computational Drug Repositioning Using Continuous Self-Controlled Case Series.
Convolutional Neural Networks for Steady Flow Approximation.
Joint Optimization of Multiple Performance Metrics in Online Video Advertising.
Kam1n0: MapReduce-based Assembly Clone Search for Reverse Engineering.
Domain Adaptation in the Absence of Source Domain Data.
CaSMoS: A Framework for Learning Candidate Selection Models over Structured Queries and Documents.
Gemello: Creating a Detailed Energy Breakdown from Just the Monthly Electricity Bill.
MAP: Frequency-Based Maximization of Airline Profits based on an Ensemble Forecasting Approach.
Computational Social Science: Exciting Progress and Future Challenges.
Large-Scale Machine Learning at Verizon: Theory and Applications.
Accelerating the Race to Autonomous Cars.
Bayesian Optimization and Embedded Learning Systems.
The Wisdom of Crowds: Best Practices for Data Prep & Machine Learning Derived from Millions of Data Science Workflows.
Profiling Users from Online Social Behaviors with Applications for Tencent Social Ads.
Learning Sparse Models at Scale.
How Machine Learning has Finally Solved Wanamaker's Dilemma.
Can You Teach the Elephant to Dance? AKA: Culture Eats Data Science for Breakfast.
Days on Market: Measuring Liquidity in Real Estate Markets.
Recruitment Market Trend Analysis with Sequential Latent Variable Models.
A Non-parametric Approach to Detect Epileptogenic Lesions using Restricted Boltzmann Machines.
GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction.
Collaborative Knowledge Base Embedding for Recommender Systems.
Batch Model for Batched Timestamps Data Analysis with Application to the SSA Disability Program.
Identifying Decision Makers from Professional Social Networks.
Ranking Relevance in Yahoo Search.
Evaluating Mobile Apps with A/B and Quasi A/B Tests.
Identifying Earmarks in Congressional Bills.
An Engagement-Based Customer Lifetime Value System for E-commerce.
An Empirical Study on Recommendation with Multiple Types of Feedback.
Contextual Intent Tracking for Personal Assistants.
Question Independent Grading using Machine Learning: The Case of Computer Program Grading.
Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features.
Dynamic and Robust Wildfire Risk Prediction System: An Unsupervised Approach.
Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments.
Engagement Capacity and Engaging Team Formation for Reach Maximization of Online Social Media Platforms.
Anomaly Detection Using Program Control Flow Graph Mining From Execution Logs.
EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting System.
DopeLearning: A Computational Approach to Rap Lyrics Generation.
Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta.
From Online Behaviors to Offline Retailing.
Audience Expansion for Online Social Network Advertising.
Repeat Buyer Prediction for E-Commerce.
Predictors without Borders: Behavioral Modeling of Product Adoption in Three Developing Countries.
Ranking Universities Based on Career Outcomes of Graduates.
Minimizing Legal Exposure of High-Tech Companies through Collaborative Filtering Methods.
Online Dual Decomposition for Performance and Delivery-Based Distributed Ad Allocation.
Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks.
Email Volume Optimization at LinkedIn.
Catch Me If You Can: Detecting Pickpocket Suspects from Large-Scale Transit Records.
Data-Driven Metric Development for Online Controlled Experiments: Seven Lessons Learned.
Identifying Police Officers at Risk of Adverse Events.
The Legislative Influence Detector: Finding Text Reuse in State Legislation.
Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights.
Predicting Disk Replacement towards Reliable Data Centers.
Matrix Computations and Optimization in Apache Spark.
Aircraft Trajectory Prediction Made Easy with Predictive Analytics.
Designing Policy Recommendations to Reduce Home Abandonment in Mexico.
Big Data Needs Big Dreamers: Lessons from Successful Big Data Investors.
A VC View of Investing in ML.
People, Computers, and The Hot Mess of Real Data.
The Evolving Meaning of Information Security.
Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality.
Graphons and Machine Learning: Modeling and Estimation of Sparse Massive Networks.
