SIGKDD(KDD) 2019 论文列表
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019.
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Tutorial: Are You My Neighbor?: Bringing Order to Neighbor Computing Problems.
Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks.
Spatio-temporal Event Forecasting and Precursor Identification.
Social User Interest Mining: Methods and Applications.
Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning.
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching.
Modern MDL meets Data Mining Insights, Theory, and Practice.
Modeling and Applications for Temporal Point Processes.
Mining Temporal Networks.
Mining and Model Understanding on Medical Data.
Learning From Networks: Algorithms, Theory, and Applications.
Interpretable Knowledge Discovery Reinforced by Visual Methods.
Incompleteness in Networks: Biases, Skewed Results, and Some Solutions.
Hypothesis Testing and Statistically-sound Pattern Mining.
Gold Panning from the Mess: Rare Category Exploration, Exposition, Representation, and Interpretation.
Foundations of Large-Scale Sequential Experimentation.
Forecasting Big Time Series: Theory and Practice.
Fake News Research: Theories, Detection Strategies, and Open Problems.
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned.
Explainable AI in Industry.
Deep Reinforcement Learning with Applications in Transportation.
Deep Natural Language Processing for Search and Recommender Systems.
Deep Bayesian Mining, Learning and Understanding.
Tutorial: Data Mining Methods for Drug Discovery and Development.
Data Integration and Machine Learning: A Natural Synergy.
Constructing and Mining Heterogeneous Information Networks from Massive Text.
Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments.
Advances in Cost-sensitive Multiclass and Multilabel Classification.
Adaptive Influence Maximization.
Welfare Maximization in Online Two-sided Marketplaces.
Transportation: A Data Driven Approach.
Towards ML Engineering with TensorFlow Extended (TFX).
Spinning the AI Pinwheel.
Seven Years of Data Science at Airbnb.
Roll of Unified Graph Analysis Platforms.
Product Ecosystem Optimization at LinkedIn.
Preventing Rhino Poaching through Machine Learning.
Integrating Domain-Knowledge into Deep Learning.
From Code to Data: AI at Scale for Developer Productivity.
Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning.
Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery.
Exploiting High Dimensionality in Big Data.
Earth Observations from a New Generation of Geostationary Satellites.
Data Science Challenges @ LinkedIn.
Building a Better Self-Driving Car: Hardware, Software, and Knowledge.
Applications of AI/ML in Established and New Industries.
Analytics Journey Map: An Approach Enable to ML at Scale.
AliGraph: A Comprehensive Graph Neural Network Platform.
AI for Small Businesses and Consumers: Applications and Innovations.
Addressing Challenges in Data Science: Scale, Skill Sets and Complexity.
4 Perspectives in Human-Centered Machine Learning.
Whole Page Optimization with Global Constraints.
Using Twitter to Predict When Vulnerabilities will be Exploited.
UrbanFM: Inferring Fine-Grained Urban Flows.
Unsupervised Clinical Language Translation.
Understanding the Role of Style in E-commerce Shopping.
Understanding Consumer Journey using Attention based Recurrent Neural Networks.
Uncovering the Co-driven Mechanism of Social and Content Links in User Churn Phenomena.
Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform.
TV Advertisement Scheduling by Learning Expert Intentions.
TrajGuard: A Comprehensive Trajectory Copyright Protection Scheme.
Towards Sustainable Dairy Management - A Machine Learning Enhanced Method for Estrus Detection.
Towards Knowledge-Based Personalized Product Description Generation in E-commerce.
Towards Identifying Impacted Users in Cellular Services.
Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation.
Time-Series Anomaly Detection Service at Microsoft.
The Secret Lives of Names?: Name Embeddings from Social Media.
The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis.
The Error is the Feature: How to Forecast Lightning using a Model Prediction Error.
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank.
Temporal Probabilistic Profiles for Sepsis Prediction in the ICU.
Structured Noise Detection: Application on Well Test Pressure Derivative Data.
Social Skill Validation at LinkedIn.
SMOILE: A Shopper Marketing Optimization and Inverse Learning Engine.
Smart Roles: Inferring Professional Roles in Email Networks.
Shrinkage Estimators in Online Experiments.
Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data.
Sequential Scenario-Specific Meta Learner for Online Recommendation.
Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data.
Semantic Product Search.
Seeker: Real-Time Interactive Search.
Seasonal-adjustment Based Feature Selection Method for Predicting Epidemic with Large-scale Search Engine Logs.
Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points.
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement.
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network.
Reserve Price Failure Rate Prediction with Header Bidding in Display Advertising.
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems.
Recurrent Neural Networks for Stochastic Control in Real-Time Bidding.
Real-World Product Deployment of Adaptive Push Notification Scheduling on Smartphones.
Real-time On-Device Troubleshooting Recommendation for Smartphones.
Real-time Event Detection on Social Data Streams.
Real-time Attention Based Look-alike Model for Recommender System.
Ranking in Genealogy: Search Results Fusion at Ancestry.
Randomized Experimental Design via Geographic Clustering.
Raise to Speak: An Accurate, Low-power Detector for Activating Voice Assistants on Smartwatches.
Pythia: AI-assisted Code Completion System.
Probabilistic Latent Variable Modeling for Assessing Behavioral Influences on Well-Being.
Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior.
Predicting Economic Development using Geolocated Wikipedia Articles.
Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising.
Precipitation Nowcasting with Satellite Imagery.
Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction.
POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion.
PinText: A Multitask Text Embedding System in Pinterest.
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching.
Personalized Attraction Enhanced Sponsored Search with Multi-task Learning.
Optuna: A Next-generation Hyperparameter Optimization Framework.
Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics.
Online Amnestic DTW to allow Real-Time Golden Batch Monitoring.
OCC: A Smart Reply System for Efficient In-App Communications.
OAG: Toward Linking Large-scale Heterogeneous Entity Graphs.
NPA: Neural News Recommendation with Personalized Attention.
Nostalgin: Extracting 3D City Models from Historical Image Data.
Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data - An Application to Climate Prediction.
Naranjo Question Answering using End-to-End Multi-task Learning Model.
MVAN: Multi-view Attention Networks for Real Money Trading Detection in Online Games.
Multi-Horizon Time Series Forecasting with Temporal Attention Learning.
MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data.
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search.
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records.
Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation.
MediaRank: Computational Ranking of Online News Sources.
Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning.
Machine Learning at Microsoft with ML.NET.
LightNet: A Dual Spatiotemporal Encoder Network Model for Lightning Prediction.
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data.
Learning Sleep Quality from Daily Logs.
Learning a Unified Embedding for Visual Search at Pinterest.
Large-scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework.
Large-Scale Training Framework for Video Annotation.
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery.
Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction.
Investigate Transitions into Drug Addiction through Text Mining of Reddit Data.
Internal Promotion Optimization.
IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation.
Infer Implicit Contexts in Real-time Online-to-Offline Recommendation.
Improving Subseasonal Forecasting in the Western U.S. with Machine Learning.
Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System.
How to Invest my Time: Lessons from Human-in-the-Loop Entity Extraction.
Hard to Park?: Estimating Parking Difficulty at Scale.
Gmail Smart Compose: Real-Time Assisted Writing.
Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression.
Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning.
FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging.
Finding Users Who Act Alike: Transfer Learning for Expanding Advertiser Audiences.
Feedback Shaping: A Modeling Approach to Nurture Content Creation.
FDML: A Collaborative Machine Learning Framework for Distributed Features.
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search.
Fairness in Recommendation Ranking through Pairwise Comparisons.
Estimating Cellular Goals from High-Dimensional Biological Data.
Enabling Onboard Detection of Events of Scientific Interest for the Europa Clipper Spacecraft.
E.T.-RNN: Applying Deep Learning to Credit Loan Applications.
Dynamic Pricing for Airline Ancillaries with Customer Context.
DuerQuiz: A Personalized Question Recommender System for Intelligent Job Interview.
Diagnosing Sample Ratio Mismatch in Online Controlled Experiments: A Taxonomy and Rules of Thumb for Practitioners.
Developing Measures of Cognitive Impairment in the Real World from Consumer-Grade Multimodal Sensor Streams.
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition.
Detecting Anomalies in Space using Multivariate Convolutional LSTM with Mixtures of Probabilistic PCA.
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events.
DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery.
DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data.
Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting.
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction.
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives.
Context by Proxy: Identifying Contextual Anomalies Using an Output Proxy.
Constructing High Precision Knowledge Bases with Subjective and Factual Attributes.
Community Detection on Large Complex Attribute Network.
Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search.
Characterizing and Forecasting User Engagement with In-App Action Graph: A Case Study of Snapchat.
Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study.
Chainer: A Deep Learning Framework for Accelerating the Research Cycle.
Carousel Ads Optimization in Yahoo Gemini Native.
Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior.
Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration.
Bid Optimization by Multivariable Control in Display Advertising.
Automatic Dialogue Summary Generation for Customer Service.
Auto-Keras: An Efficient Neural Architecture Search System.
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications.
Applying Deep Learning to Airbnb Search.
Anomaly Detection for an E-commerce Pricing System.
Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning.
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks.
AKUPM: Attention-Enhanced Knowledge-Aware User Preference Model for Recommendation.
AiAds: Automated and Intelligent Advertising System for Sponsored Search.
Adversarial Matching of Dark Net Market Vendor Accounts.
Active Deep Learning for Activity Recognition with Context Aware Annotator Selection.
Actions Speak Louder than Goals: Valuing Player Actions in Soccer.
AccuAir: Winning Solution to Air Quality Prediction for KDD Cup 2018.
A User-Centered Concept Mining System for Query and Document Understanding at Tencent.
A Unified Framework for Marketing Budget Allocation.
A Severity Score for Retinopathy of Prematurity.
A Robust Framework for Accelerated Outcome-driven Risk Factor Identification from EHR.
A Generalized Framework for Population Based Training.
A Deep Value-network Based Approach for Multi-Driver Order Dispatching.
A Deep Generative Approach to Search Extrapolation and Recommendation.
A Data-Driven Approach for Multi-level Packing Problems in Manufacturing Industry.
A Collaborative Learning Framework to Tag Refinement for Points of Interest.
150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com.
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning.
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts.
Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning.
Uncovering Pattern Formation of Information Flow.
TUBE: Embedding Behavior Outcomes for Predicting Success.
Training and Meta-Training Binary Neural Networks with Quantum Computing.
Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training?
Time Critic Policy Gradient Methods for Traffic Signal Control in Complex and Congested Scenarios.
Three-Dimensional Stable Matching Problem for Spatial Crowdsourcing Platforms.
The Role of: A Novel Scientific Knowledge Graph Representation and Construction Model.
The Impact of Person-Organization Fit on Talent Management: A Structure-Aware Convolutional Neural Network Approach.
Testing Dynamic Incentive Compatibility in Display Ad Auctions.
Tensorized Determinantal Point Processes for Recommendation.
Task-Adversarial Co-Generative Nets.
Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning.
SurfCon: Synonym Discovery on Privacy-Aware Clinical Data.
Streaming Session-based Recommendation.
Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units.
State-Sharing Sparse Hidden Markov Models for Personalized Sequences.
Stability and Generalization of Graph Convolutional Neural Networks.
SPuManTE: Significant Pattern Mining with Unconditional Testing.
Social Recommendation with Optimal Limited Attention.
Significance of Patterns in Data Visualisations.
Sherlock: A Deep Learning Approach to Semantic Data Type Detection.
Sets2Sets: Learning from Sequential Sets with Neural Networks.
Sequential Anomaly Detection using Inverse Reinforcement Learning.
Separated Trust Regions Policy Optimization Method.
Scaling Multinomial Logistic Regression via Hybrid Parallelism.
Scaling Multi-Armed Bandit Algorithms.
Scalable Hierarchical Clustering with Tree Grafting.
Scalable Graph Embeddings via Sparse Transpose Proximities.
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding.
Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning.
Robust Graph Convolutional Networks Against Adversarial Attacks.
Riker: Mining Rich Keyword Representations for Interpretable Product Question Answering.
Revisiting kd-tree for Nearest Neighbor Search.
Retaining Privileged Information for Multi-Task Learning.
Representation Learning for Attributed Multiplex Heterogeneous Network.
Relation Extraction via Domain-aware Transfer Learning.
Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions.
QuesNet: A Unified Representation for Heterogeneous Test Questions.
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs.
ProGAN: Network Embedding via Proximity Generative Adversarial Network.
PrivPy: General and Scalable Privacy-Preserving Data Mining.
PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network.
Predicting Path Failure In Time-Evolving Graphs.
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks.
PerDREP: Personalized Drug Effectiveness Prediction from Longitudinal Observational Data.
Paper Matching with Local Fairness Constraints.
Pairwise Comparisons with Flexible Time-Dynamics.
Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling.
Optimizing Peer Learning in Online Groups with Affinities.
Optimizing Impression Counts for Outdoor Advertising.
On Dynamic Network Models and Application to Causal Impact.
Off-policy Learning for Multiple Loggers.
OBOE: Collaborative Filtering for AutoML Model Selection.
NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching.
Network Density of States.
Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction: Multi-task RNN and Higer-order MRFs for Stock Price Classification.
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings.
Multiple Relational Attention Network for Multi-task Learning.
Modeling Extreme Events in Time Series Prediction.
Modeling Dwell Time Engagement on Visual Multimedia.
MinJoin: Efficient Edit Similarity Joins via Local Hash Minima.
Mining Algorithm Roadmap in Scientific Publications.
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation.
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network.
Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit.
Link Prediction with Signed Latent Factors in Signed Social Networks.
Learning Network-to-Network Model for Content-rich Network Embedding.
Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining.
Learning from Incomplete and Inaccurate Supervision.
Learning Dynamic Context Graphs for Predicting Social Events.
Learning Class-Conditional GANs with Active Sampling.
Latent Network Summarization: Bridging Network Embedding and Summarization.
λOpt: Learn to Regularize Recommender Models in Finer Levels.
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems.
K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters.
KGAT: Knowledge Graph Attention Network for Recommendation.
Isolation Set-Kernel and Its Application to Multi-Instance Learning.
Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding.
Investigating Cognitive Effects in Session-level Search User Satisfaction.
Interview Choice Reveals Your Preference on the Market: To Improve Job-Resume Matching through Profiling Memories.
Interpretable and Steerable Sequence Learning via Prototypes.
Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding.
Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis.
Improving the Quality of Explanations with Local Embedding Perturbations.
Identifiability of Cause and Effect using Regularized Regression.
Hypothesis Generation From Text Based On Co-Evolution Of Biomedical Concepts.
Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction.
Hierarchical Gating Networks for Sequential Recommendation.
Hidden POI Ranking with Spatial Crowdsourcing.
Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications.
Heterogeneous Graph Neural Network.
HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings.
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data.
Graph-based Semi-Supervised & Active Learning for Edge Flows.
Graph Transformation Policy Network for Chemical Reaction Prediction.
Graph Representation Learning via Hard and Channel-Wise Attention Networks.
Graph Recurrent Networks With Attributed Random Walks.
Graph Convolutional Networks with EigenPooling.
Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space.
GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization.
Focused Context Balancing for Robust Offline Policy Evaluation.
Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging.
Fighting Opinion Control in Social Networks via Link Recommendation.
Fates of Microscopic Social Ecosystems: Keep Alive or Dead?
Fast Approximation of Empirical Entropy via Subsampling.
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach.
Factorization Bandits for Online Influence Maximization.
Exploiting Cognitive Structure for Adaptive Learning.
Exact-K Recommendation via Maximal Clique Optimization.
ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data.
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks.
Estimating Graphlet Statistics via Lifting.
EpiDeep: Exploiting Embeddings for Epidemic Forecasting.
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation.
Enhancing Domain Word Embedding via Latent Semantic Imputation.
Enhancing Collaborative Filtering with Generative Augmentation.
Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation.
Efficient Maximum Clique Computation over Large Sparse Graphs.
Efficient Global String Kernel with Random Features: Beyond Counting Substructures.
Efficient and Effective Express via Contextual Cooperative Reinforcement Learning.
Effective and Efficient Sports Play Retrieval with Deep Representation Learning.
Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation.
EdMot: An Edge Enhancement Approach for Motif-aware Community Detection.
Dynamical Origins of Distribution Functions.
Dynamic Modeling and Forecasting of Time-evolving Data Streams.
Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination.
Dual Averaging Method for Online Graph-structured Sparsity.
Discovering Unexpected Local Nonlinear Interactions in Scientific Black-box Models.
Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction.
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification.
dEFEND: Explainable Fake News Detection.
DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks.
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information.
Deep Landscape Forecasting for Real-time Bidding Advertising.
Deep Anomaly Detection with Deviation Networks.
DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation.
Coupled Variational Recurrent Collaborative Filtering.
CoSTCo: A Neural Tensor Completion Model for Sparse Tensors.
Coresets for Minimum Enclosing Balls over Sliding Windows.
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network.
Contrastive Antichains in Hierarchies.
Contextual Fact Ranking and Its Applications in Table Synthesis and Compression.
Conditional Random Field Enhanced Graph Convolutional Neural Networks.
Clustering without Over-Representation.
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks.
Certifiable Robustness and Robust Training for Graph Convolutional Networks.
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction.
Axiomatic Interpretability for Multiclass Additive Models.
AutoNE: Hyperparameter Optimization for Massive Network Embedding.
Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning.
Auditing Data Provenance in Text-Generation Models.
Attribute-Driven Backbone Discovery.
AtSNE: Efficient and Robust Visualization on GPU through Hierarchical Optimization.
Assessing The Factual Accuracy of Generated Text.
A Visual Dialog Augmented Interactive Recommender System.
Adversarially Robust Submodular Maximization under Knapsack Constraints.
Adversarial Variational Embedding for Robust Semi-supervised Learning.
Adversarial Substructured Representation Learning for Mobile User Profiling.
Adversarial Learning on Heterogeneous Information Networks.
ADMM for Efficient Deep Learning with Global Convergence.
Adaptive-Halting Policy Network for Early Classification.
Adaptive Unsupervised Feature Selection on Attributed Networks.
Adaptive Graph Guided Disambiguation for Partial Label Learning.
Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability.
A Representation Learning Framework for Property Graphs.
A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health.
A Multiscale Scan Statistic for Adaptive Submatrix Localization.
A Minimax Game for Instance based Selective Transfer Learning.
A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets.
A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction.
A Free Energy Based Approach for Distance Metric Learning.
The Unreasonable Effectiveness, and Difficulty, of Data in Healthcare.
Do Simpler Models Exist and How Can We Find Them?
