SIGKDD(KDD) 2020 论文列表
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020.
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Data Paucity and Low Resource Scenarios: Challenges and Opportunities.
Innovating with Language AI.
Unleashing the Power of Subjective Data: Managing Experiences as First-Class Citizens.
Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning.
Multimodal Machine Learning for Video and Image Analysis.
Toward Responsible AI by Planning to Fail.
How AI Can Help Build Resiliency for Small Businesses in a Global Economic Crisis.
Build the State-of-the-Art Machine Learning Technology for the Crypto Economy.
Using Machine Learning to Detect Cancer Early.
Artificial Intelligence for Healthcare.
Straddling the Boundary between Contribution and Solution Driven Science.
Preserving Integrity in Online Social Media.
The Data Science Mentoring Fire Next Time: Innovative Strategies for Mentoring in Data Science.
Bringing Inclusive Diversity to Data Science: Opportunities and Challenges.
Mutually Beneficial Collaborations to Broaden Participation of Hispanics in Data Science.
No Computation without Representation: Avoiding Data and Algorithm Biases through Diversity.
Models of Data Governance and Advancing Indigenous Genomic Data Sovereignty.
The Illusion of Inclusion: Large Scale Genomic Data Sovereignty and Indigenous Populations.
Perspectives on Broadening Participation in STEM Careers across Academia, Government, and Industry.
Accessible Online Meetings and Presentations.
The Dark Side of Machine Learning Algorithms: How and Why They Can Leverage Bias, and What Can Be Done to Pursue Algorithmic Fairness.
Broadening Participation in Technology Policy.
Support for Diverse Students.
CoRE Lab - An Effort to Engage College Hispanic Students in STEM.
Diversity and Inclusion, a Perspective from a Four Years MSI Faculty Member.
How Can Computer Science Education Address Inequities.
Data-Driven Never-Ending Learning Question Answering Systems.
Image and Video Understanding for Recommendation and Spam Detection Systems.
Learning by Exploration: New Challenges in Real-World Environments.
Embedding-Driven Multi-Dimensional Topic Mining and Text Analysis.
Deep Learning for Industrial AI: Challenges, New Methods and Best Practices.
Deep Learning for Anomaly Detection.
Data Sketching for Real Time Analytics: Theory and Practice.
Edge AI: Systems Design and ML for IoT Data Analytics.
Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data.
Overview and Importance of Data Quality for Machine Learning Tasks.
Data Science for the Real Estate Industry.
Multi-modal Network Representation Learning.
Deep Graph Learning: Foundations, Advances and Applications.
Data Pricing - From Economics to Data Science.
Tutorial on Online User Engagement: Metrics and Optimization.
Recent Advances in Multimodal Educational Data Mining in K-12 Education.
Tutorial on Human-Centered Explainability for Healthcare.
Recent Advances on Graph Analytics and Its Applications in Healthcare.
Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web.
Adversarial Attacks and Defenses: Frontiers, Advances and Practice.
Learning with Small Data.
Scientific Text Mining and Knowledge Graphs.
Physics Inspired Models in Artificial Intelligence.
Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys.
Learning from All Types of Experiences: A Unifying Machine Learning Perspective.
Fairness in Machine Learning for Healthcare.
Causal Inference Meets Machine Learning.
Building Recommender Systems with PyTorch.
Introduction to Computer Vision and Real Time Deep Learning-based Object Detection.
Scalable Graph Neural Networks with Deep Graph Library.
In Search for a Cure: Recommendation With Knowledge Graph on CORD-19.
Computer Vision: Deep Dive into Object Segmentation Approaches.
Deep Learning for Search and Recommender Systems in Practice.
Dealing with Bias and Fairness in Data Science Systems: A Practical Hands-on Tutorial.
Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges.
Faster, Simpler, More Accurate: Practical Automated Machine Learning with Tabular, Text, and Image Data.
Robust Deep Learning Methods for Anomaly Detection.
DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters.
Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS.
Neural Structured Learning: Training Neural Networks with Structured Signals.
How to Calibrate your Neural Network Classifier: Getting True Probabilities from a Classification Model.
Building Forecasting Solutions Using Open-Source and Azure Machine Learning.
Put Deep Learning to Work: Accelerate Deep Learning through Amazon SageMaker and ML Services.
From Zero to AI Hero with Automated Machine Learning.
Fighting a Pandemic: Convergence of Expertise, Data Science and Policy.
Understanding the Urban Pandemic Spreading of COVID-19 with Real World Mobility Data.
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data.
Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea.
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs.
Simulating the Impact of Hospital Capacity and Social Isolation to Minimize the Propagation of Infectious Diseases.
Understanding the Impact of the COVID-19 Pandemic on Transportation-related Behaviors with Human Mobility Data.
Data-driven Simulation and Optimization for Covid-19 Exit Strategies.
Learning to Simulate Human Mobility.
Learning with Limited Labels via Momentum Damped & Differentially Weighted Optimization.
A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce.
USAD: UnSupervised Anomaly Detection on Multivariate Time Series.
Ads Allocation in Feed via Constrained Optimization.
Towards Building an Intelligent Chatbot for Customer Service: Learning to Respond at the Appropriate Time.
Hypergraph Convolutional Recurrent Neural Network.
Gemini: A Novel and Universal Heterogeneous Graph Information Fusing Framework for Online Recommendations.
Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning.
CompactETA: A Fast Inference System for Travel Time Prediction.
Fitbit for Chickens?: Time Series Data Mining Can Increase the Productivity of Poultry Farms.
Jointly Learning to Recommend and Advertise.
Explainable Classification of Brain Networks via Contrast Subgraphs.
Bootstrapping Complete The Look at Pinterest.
An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map Images.
DeepTriage: Automated Transfer Assistance for Incidents in Cloud Services.
An Empirical Analysis of Backward Compatibility in Machine Learning Systems.
CrowdQuake: A Networked System of Low-Cost Sensors for Earthquake Detection via Deep Learning.
Characterizing and Learning Representation on Customer Contact Journeys in Cellular Services.
BusTr: Predicting Bus Travel Times from Real-Time Traffic.
Multimodal Deep Learning Based Crop Classification Using Multispectral and Multitemporal Satellite Imagery.
Bandit based Optimization of Multiple Objectives on a Music Streaming Platform.
Dynamic Heterogeneous Graph Neural Network for Real-time Event Prediction.
Shop The Look: Building a Large Scale Visual Shopping System at Pinterest.
Time-Aware User Embeddings as a Service.
SimClusters: Community-Based Representations for Heterogeneous Recommendations at Twitter.
Prediction of Hourly Earnings and Completion Time on a Crowdsourcing Platform.
Taming Pretrained Transformers for Extreme Multi-label Text Classification.
Cracking the Black Box: Distilling Deep Sports Analytics.
Climate Downscaling Using YNet: A Deep Convolutional Network with Skip Connections and Fusion.
Price Investment using Prescriptive Analytics and Optimization in Retail.
TIES: Temporal Interaction Embeddings for Enhancing Social Media Integrity at Facebook.
Pest Management In Cotton Farms: An AI-System Case Study from the Global South.
Attention based Multi-Modal New Product Sales Time-series Forecasting.
Interleaved Sequence RNNs for Fraud Detection.
Identifying Homeless Youth At-Risk of Substance Use Disorder: Data-Driven Insights for Policymakers.
Multitask Mixture of Sequential Experts for User Activity Streams.
Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data.
Meta-Learning for Query Conceptualization at Web Scale.
Efficiently Solving the Practical Vehicle Routing Problem: A Novel Joint Learning Approach.
Balanced Order Batching with Task-Oriented Graph Clustering.
Fraud Transactions Detection via Behavior Tree with Local Intention Calibration.
Delivery Scope: A New Way of Restaurant Retrieval for On-demand Food Delivery Service.
Multi-objective Optimization for Guaranteed Delivery in Video Service Platform.
A Sleeping, Recovering Bandit Algorithm for Optimizing Recurring Notifications.
Learning to Generate Personalized Query Auto-Completions via a Multi-View Multi-Task Attentive Approach.
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning.
A Request-level Guaranteed Delivery Advertising Planning: Forecasting and Allocation.
Learning to Score Economic Development from Satellite Imagery.
Molecular Inverse-Design Platform for Material Industries.
Managing Diversity in Airbnb Search.
Controllable Multi-Interest Framework for Recommendation.
Mining Implicit Relevance Feedback from User Behavior for Web Question Answering.
Large-Scale Training System for 100-Million Classification at Alibaba.
Improving Recommendation Quality in Google Drive.
User Sentiment as a Success Metric: Persistent Biases Under Full Randomization.
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection.
Salience and Market-aware Skill Extraction for Job Targeting.
Forecasting the Evolution of Hydropower Generation.
Debiasing Grid-based Product Search in E-commerce.
Unsupervised Translation via Hierarchical Anchoring: Functional Mapping of Places across Cities.
General-Purpose User Embeddings based on Mobile App Usage.
Improving Deep Learning for Airbnb Search.
Doing in One Go: Delivery Time Inference Based on Couriers' Trajectories.
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition.
Domain Specific Knowledge Graphs as a Service to the Public: Powering Social-Impact Funding in the US.
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective.
A Self-Evolving Mutually-Operative Recurrent Network-based Model for Online Tool Condition Monitoring in Delay Scenario.
Geodemographic Influence Maximization.
Acoustic Measures for Real-Time Voice Coaching.
Comprehensive Information Integration Modeling Framework for Video Titling.
Personalized Image Retrieval with Sparse Graph Representation Learning.
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types.
Contagious Chain Risk Rating for Networked-guarantee Loans.
Faster Secure Data Mining via Distributed Homomorphic Encryption.
ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps.
Category-Specific CNN for Visual-aware CTR Prediction at JD.com.
Personalized Prefix Embedding for POI Auto-Completion in the Search Engine of Baidu Maps.
Cascade-LSTM: A Tree-Structured Neural Classifier for Detecting Misinformation Cascades.
Game Action Modeling for Fine Grained Analyses of Player Behavior in Multi-player Card Games (Rummy as Case Study).
City Metro Network Expansion with Reinforcement Learning.
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction.
Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments.
Learning Instrument Invariant Characteristics for Generating High-resolution Global Coral Reef Maps.
GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce.
Cracking Tabular Presentation Diversity for Automatic Cross-Checking over Numerical Facts.
Privileged Features Distillation at Taobao Recommendations.
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors.
Order Fulfillment Cycle Time Estimation for On-Demand Food Delivery.
Lumos: A Library for Diagnosing Metric Regressions in Web-Scale Applications.
Embedding-based Retrieval in Facebook Search.
CLARA: Confidence of Labels and Raters.
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data.
Grale: Designing Networks for Graph Learning.
Map Generation from Large Scale Incomplete and Inaccurate Data Labels.
Reconstruction and Decomposition of High-Dimensional Landscapes via Unsupervised Learning.
To Tune or Not to Tune?: In Search of Optimal Configurations for Data Analytics.
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data.
Combo-Attention Network for Baidu Video Advertising.
Scaling Graph Neural Networks with Approximate PageRank.
Hubble: An Industrial System for Audience Expansion in Mobile Marketing.
HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival.
MultiSage: Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks.
What is that Building?: An End-to-end System for Building Recognition from Streetside Images.
Learning to Cluster Documents into Workspaces Using Large Scale Activity Logs.
Building Continuous Integration Services for Machine Learning.
Easy Perturbation EEG Algorithm for Spectral Importance (easyPEASI): A Simple Method to Identify Important Spectral Features of EEG in Deep Learning Models.
Neural Input Search for Large Scale Recommendation Models.
Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks.
Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy.
Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies.
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems.
Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization.
Context-Aware Attentive Knowledge Tracing.
Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine.
PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest.
OptMatch: Optimized Matchmaking via Modeling the High-Order Interactions on the Arena.
Temporal-Contextual Recommendation in Real-Time.
Sub-Matrix Factorization for Real-Time Vote Prediction.
Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps.
TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding.
Octet: Online Catalog Taxonomy Enrichment with Self-Supervision.
Attentional Multi-graph Convolutional Network for Regional Economy Prediction with Open Migration Data.
CurvaNet: Geometric Deep Learning based on Directional Curvature for 3D Shape Analysis.
Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns.
Learning Stable Graphs from Multiple Environments with Selection Bias.
Stable Learning via Differentiated Variable Decorrelation.
REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs.
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining.
Voronoi Graph Traversal in High Dimensions with Applications to Topological Data Analysis and Piecewise Linear Interpolation.
Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC.
Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation.
Algorithmic Decision Making with Conditional Fairness.
On Sampling Top-K Recommendation Evaluation.
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering.
Non-Linear Mining of Social Activities in Tensor Streams.
Algorithmic Aspects of Temporal Betweenness.
Interactive Path Reasoning on Graph for Conversational Recommendation.
GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases.
Tight Sensitivity Bounds For Smaller Coresets.
Learning Based Distributed Tracking.
A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks.
Dual Channel Hypergraph Collaborative Filtering.
Geography-Aware Sequential Location Recommendation.
Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction.
Scaling Choice Models of Relational Social Data.
DeepSinger: Singing Voice Synthesis with Data Mined From the Web.
Hypergraph Clustering Based on PageRank.
Neural Subgraph Isomorphism Counting.
List-wise Fairness Criterion for Point Processes.
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes.
CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring.
Combinatorial Black-Box Optimization with Expert Advice.
Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding.
AdvMind: Inferring Adversary Intent of Black-Box Attacks.
A Non-Iterative Quantile Change Detection Method in Mixture Model with Heavy-Tailed Components.
Prioritized Restreaming Algorithms for Balanced Graph Partitioning.
Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs.
GPT-GNN: Generative Pre-Training of Graph Neural Networks.
TinyGNN: Learning Efficient Graph Neural Networks.
Estimating the Percolation Centrality of Large Networks through Pseudo-dimension Theory.
Multimodal Learning with Incomplete Modalities by Knowledge Distillation.
Graph Attention Networks over Edge Content-Based Channels.
CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams.
Unsupervised Paraphrasing via Deep Reinforcement Learning.
TAdaNet: Task-Adaptive Network for Graph-Enriched Meta-Learning.
Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions.
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data.
ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization.
On Sampled Metrics for Item Recommendation.
RayS: A Ray Searching Method for Hard-label Adversarial Attack.
Hyperbolic Distance Matrices.
RECIPTOR: An Effective Pretrained Model for Recipe Representation Learning.
Minimizing Localized Ratio Cut Objectives in Hypergraphs.
Interpretable Deep Graph Generation with Node-edge Co-disentanglement.
Heidegger: Interpretable Temporal Causal Discovery.
Aligning Superhuman AI with Human Behavior: Chess as a Model System.
Understanding Negative Sampling in Graph Representation Learning.
Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations.
Statistically Significant Pattern Mining with Ordinal Utility.
RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift.
LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values.
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units.
Context-to-Session Matching: Utilizing Whole Session for Response Selection in Information-Seeking Dialogue Systems.
A Geometric Approach to Predicting Bounds of Downstream Model Performance.
Dynamic Knowledge Graph based Multi-Event Forecasting.
WavingSketch: An Unbiased and Generic Sketch for Finding Top-k Items in Data Streams.
Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation.
Deep State-Space Generative Model For Correlated Time-to-Event Predictions.
Vamsa: Automated Provenance Tracking in Data Science Scripts.
Diverse Rule Sets.
Measuring Model Complexity of Neural Networks with Curve Activation Functions.
Evaluating Conversational Recommender Systems via User Simulation.
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction.
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection.
Evaluating Fairness Using Permutation Tests.
Towards Physics-informed Deep Learning for Turbulent Flow Prediction.
AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space.
Unsupervised Differentiable Multi-aspect Network Embedding.
The Spectral Zoo of Networks: Embedding and Visualizing Networks with Spectral Moments.
Attackability Characterization of Adversarial Evasion Attack on Discrete Data.
Discovering Functional Dependencies from Mixed-Type Data.
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks.
Recurrent Halting Chain for Early Multi-label Classification.
In and Out: Optimizing Overall Interaction in Probabilistic Graphs under Clustering Constraints.
Multi-Class Data Description for Out-of-distribution Detection.
Prediction and Profiling of Audience Competition for Online Television Series.
Catalysis Clustering with GAN by Incorporating Domain Knowledge.
xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis.
InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity.
A Novel Deep Learning Model by Stacking Conditional Restricted Boltzmann Machine and Deep Neural Network.
ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification.
HGMF: Heterogeneous Graph-based Fusion for Multimodal Data with Incompleteness.
The NodeHopper: Enabling Low Latency Ranking with Constraints via a Fast Dual Solver.
HOPS: Probabilistic Subtree Mining for Small and Large Graphs.
Competitive Analysis for Points of Interest.
Discovering Approximate Functional Dependencies using Smoothed Mutual Information.
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks.
FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems.
Joint Policy-Value Learning for Recommendation.
Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning.
Block Model Guided Unsupervised Feature Selection.
LayoutLM: Pre-training of Text and Layout for Document Image Understanding.
Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping.
Handling Information Loss of Graph Neural Networks for Session-based Recommendation.
HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training.
Semi-Supervised Multi-Label Learning from Crowds via Deep Sequential Generative Model.
Average Sensitivity of Spectral Clustering.
Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks.
Matrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness.
SEAL: Learning Heuristics for Community Detection with Generative Adversarial Networks.
FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents.
Predicting Temporal Sets with Deep Neural Networks.
Correlation Networks for Extreme Multi-label Text Classification.
Graph Structural-topic Neural Network.
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision.
Re-identification Attack to Privacy-Preserving Data Analysis with Noisy Sample-Mean.
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation.
STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths.
Sliding Sketches: A Framework using Time Zones for Data Stream Processing in Sliding Windows.
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion.
Redundancy-Free Computation for Graph Neural Networks.
NetTrans: Neural Cross-Network Transformation.
Adaptive Graph Encoder for Attributed Graph Embedding.
Off-policy Bandits with Deficient Support.
High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder.
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction.
Mining Persistent Activity in Continually Evolving Networks.
Robust Spammer Detection by Nash Reinforcement Learning.
Generic Outlier Detection in Multi-Armed Bandit.
Grammatically Recognizing Images with Tree Convolution.
Neural Dynamics on Complex Networks.
Targeted Data-driven Regularization for Out-of-Distribution Generalization.
A Causal Look at Statistical Definitions of Discrimination.
Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder.
Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams.
Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks.
AutoGrow: Automatic Layer Growing in Deep Convolutional Networks.
TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations.
Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity.
COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching.
AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction.
BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals.
Enterprise Cooperation and Competition Analysis with a Sign-Oriented Preference Network.
Learning Opinion Dynamics From Social Traces.
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks.
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction.
Estimating Properties of Social Networks via Random Walk considering Private Nodes.
Representing Temporal Attributes for Schema Matching.
Finding Effective Geo-social Group for Impromptu Activities with Diverse Demands.
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation.
Deep Learning of High-Order Interactions for Protein Interface Prediction.
Edge-consensus Learning: Deep Learning on P2P Networks with Nonhomogeneous Data.
Personalized PageRank to a Target Node, Revisited.
HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records.
Missing Value Imputation for Mixed Data via Gaussian Copula.
Parallel DNN Inference Framework Leveraging a Compact RISC-V ISA-based Multi-core System.
MoFlow: An Invertible Flow Model for Generating Molecular Graphs.
AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks.
Towards Fair Truth Discovery from Biased Crowdsourced Answers.
Residual Correlation in Graph Neural Network Regression.
Mining Large Quasi-cliques with Quality Guarantees from Vertex Neighborhoods.
MinSearch: An Efficient Algorithm for Similarity Search under Edit Distance.
Feature-Induced Manifold Disambiguation for Multi-View Partial Multi-label Learning.
WeightGrad: Geo-Distributed Data Analysis Using Quantization for Faster Convergence and Better Accuracy.
Imputing Various Incomplete Attributes via Distance Likelihood Maximization.
Z-Miner: An Efficient Method for Mining Frequent Arrangements of Event Intervals.
Geodesic Forests.
MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals.
DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation.
Disentangled Self-Supervision in Sequential Recommenders.
Malicious Attacks against Deep Reinforcement Learning Interpretations.
Policy-GNN: Aggregation Optimization for Graph Neural Networks.
INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare.
CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data.
XGNN: Towards Model-Level Explanations of Graph Neural Networks.
Vulnerability vs. Reliability: Disentangled Adversarial Examples for Cross-Modal Learning.
Recurrent Networks for Guided Multi-Attention Classification.
A Data-Driven Graph Generative Model for Temporal Interaction Networks.
Local Motif Clustering on Time-Evolving Graphs.
InFoRM: Individual Fairness on Graph Mining.
TranSlider: Transfer Ensemble Learning from Exploitation to Exploration.
Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling.
Laplacian Change Point Detection for Dynamic Graphs.
Towards Deeper Graph Neural Networks.
From Online to Non-i.i.d. Batch Learning.
Incremental Lossless Graph Summarization.
How to Count Triangles, without Seeing the Whole Graph.
Grounding Visual Concepts for Zero-Shot Event Detection and Event Captioning.
Adversarial Infidelity Learning for Model Interpretation.
A Block Decomposition Algorithm for Sparse Optimization.
Local Community Detection in Multiple Networks.
SCE: Scalable Network Embedding from Sparsest Cut.
Hierarchical Attention Propagation for Healthcare Representation Learning.
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction.
Kronecker Attention Networks.
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks.
NodeAug: Semi-Supervised Node Classification with Data Augmentation.
Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection.
Efficient Algorithm for the b-Matching Graph.
Structural Patterns and Generative Models of Real-world Hypergraphs.
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems.
Rethinking Pruning for Accelerating Deep Inference At the Edge.
SSumM: Sparse Summarization of Massive Graphs.
Semantic Search in Millions of Equations.
Attention and Memory-Augmented Networks for Dual-View Sequential Learning.
Spectrum-Guided Adversarial Disparity Learning.
Partial Multi-Label Learning via Probabilistic Graph Matching Mechanism.
Truth Discovery against Strategic Sybil Attack in Crowdsourcing.
Directional Multivariate Ranking.
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph.
Graph Structure Learning for Robust Graph Neural Networks.
Kernel Assisted Learning for Personalized Dose Finding.
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach.
Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction.
Higher-order Clustering in Complex Heterogeneous Networks.
Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense.
Learning Effective Road Network Representation with Hierarchical Graph Neural Networks.
Keynote Speaker: Alessandro Vespignani.
Keynote Speaker: Yolanda Gil.
Keynote Speaker: Emery N. Brown.
AI for Intelligent Financial Services: Examples and Discussion.
