SIGKDD(KDD) 2021 论文列表
KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021.
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3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2021.
The Third International Workshop on Smart Data for Blockchain and Distributed Ledger (SDBD2021): Joint Workshop with SIGKDD 2021 Trust Day.
DeepSpatial'21: 2nd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems.
Overview of the 1st Workshop on City Brain Research.
The 4th Artificial Intelligence of Things (AIoT) Workshop.
Workshop on Model Mining.
20th International Workshop on Data Mining in Bioinformatics (BIOKDD 2021).
2nd International Workshop on Industrial Recommendation Systems (IRS).
PLP 2021: Workshop on Programming Language Processing.
TMC 2021: 2021 International Workshop on Talent and Management Computing.
The Sixth International Workshop on Deep Learning on Graphs - Methods and Applications (DLG-KDD'21).
Machine Learning for Consumers and Markets.
The Fifth International Workshop on Automation in Machine Learning.
MLHat: Deployable Machine Learning for Security Defense.
KDD Health Day/DSHealth 2021: Joint KDD 2021 Health Day and 2021 KDD Workshop on Applied Data Science for Healthcare: State of XAI and Trustworthiness in Health.
The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA 2021).
Risk Identification & Quantification in Complex Human-Natural Systems via Convergent Data Intensive Research.
ACM KDD AI4Cyber: The 1st Workshop on Artificial Intelligence-enabled Cybersecurity Analytics.
MiLeTS'21: 7th KDD Workshop on Mining and Learning from Time Series.
VDS'21: Visualization in Data Science.
2nd International Workshop on Data Quality Assessment for Machine Learning.
Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA).
The Third International TrueFact Workshop: Making a Credible Web for Tomorrow.
The KDD 2021 Workshop on Causal Discovery (CD2021).
Machine Learning in Finance.
2021 KDD Workshop on Understanding Public Perceptions for Applied Data Science: How Important is it to Engage Society in Technology Development?
Workshop on Data-Efficient Machine Learning (DeMaL).
Multi-Armed Bandits and Reinforcement Learning: Advancing Decision Making in E-Commerce and Beyond.
WIT: Workshop on deriving Insights from user-generated Text.
The Second International MIS2 Workshop: Misinformation and Misbehavior Mining on the Web.
DI-2021: The Second Document Intelligence Workshop.
Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning.
Data Science with Human in the Loop.
International Workshop on Knowledge Graph: Heterogenous Graph Deep Learning and Applications.
The International Workshop on Pretraining: Algorithms, Architectures, and Applications ([email protected] 2021).
Measures and Best Practices for Responsible AI.
Workshop on Online and Adaptative Recommender Systems (OARS).
Bayesian Causal Inference for Real World Interactive Systems.
Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021).
BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysis and Processing.
ODD: Outlier Detection and Description.
AdKDD 2021.
The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021).
Fragile Earth: Accelerating Progress towards Equitable Sustainability.
Towards Fair Federated Learning.
Multi-Objective Recommendations.
Causal Inference from Network Data.
Simple and Automatic Distributed Machine Learning on Ray.
Data Efficient Learning on Graphs.
All You Need to Know to Build a Product Knowledge Graph.
Physics-Guided AI for Large-Scale Spatiotemporal Data.
Adversarial Robustness in Deep Learning: From Practices to Theories.
Deep Learning on Graphs for Natural Language Processing.
Automated Machine Learning on Graph.
Counterfactual Explanations in Explainable AI: A Tutorial.
Fairness in Networks: Social Capital, Information Access, and Interventions.
From Deep Learning to Deep Reasoning.
Artificial Intelligence for Drug Discovery.
Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber.
Mixed Method Development of Evaluation Metrics.
Language Scaling: Applications, Challenges and Approaches.
KDD 2021 Tutorial on Systemic Challenges and Solutions on Bias and Unfairness in Peer Review.
A Visual Tour of Bias Mitigation Techniques for Word Representations.
High-Dimensional Similarity Query Processing for Data Science.
From Tables to Knowledge: Recent Advances in Table Understanding.
Data Pricing and Data Asset Governance in the AI Era.
Toward Explainable Deep Anomaly Detection.
Fake News, Disinformation, Propaganda, Media Bias, and Flattening the Curve of the COVID-19 Infodemic.
On the Power of Pre-Trained Text Representations: Models and Applications in Text Mining.
Advances in Mining Heterogeneous Healthcare Data.
AutoML: A Perspective where Industry Meets Academy.
Machine Learning Robustness, Fairness, and their Convergence.
Graph Representation Learning: Foundations, Methods, Applications and Systems.
Real-time Event Detection for Emergency Response Tutorial.
Data Quality for Machine Learning Tasks.
New Frontiers of Multi-Network Mining: Recent Developments and Future Trend.
Fairness and Explanation in Clustering and Outlier Detection.
Machine Learning Explainability and Robustness: Connected at the Hip.
Explainability for Natural Language Processing.
Challenges in KDD and ML for Sustainable Development.
Creating Recommender Systems Datasets in Scientific Fields.
Online Advertising Incrementality Testing And Experimentation: Industry Practical Lessons.
Data Science on Blockchains.
Software as a Medical Device: Regulating AI in Healthcare via Responsible AI.
Pre-trained Language Model based Ranking in Baidu Search.
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising.
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions.
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems.
AutoSmart: An Efficient and Automatic Machine Learning Framework for Temporal Relational Data.
Incorporating Prior Financial Domain Knowledge into Neural Networks for Implied Volatility Surface Prediction.
AutoLoss: Automated Loss Function Search in Recommendations.
HALO: Hierarchy-aware Fault Localization for Cloud Systems.
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal.
MEOW: A Space-Efficient Nonparametric Bid Shading Algorithm.
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization.
Talent Demand Forecasting with Attentive Neural Sequential Model.
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba.
Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation.
Semi-supervised Bearing Fault Diagnosis with Adversarially-Trained Phase-Consistent Network.
Device-Cloud Collaborative Learning for Recommendation.
Improving the Information Disclosure in Mobility-on-Demand Systems.
FLOP: Federated Learning on Medical Datasets using Partial Networks.
Session-Aware Query Auto-completion using Extreme Multi-Label Ranking.
PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma.
Towards the D-Optimal Online Experiment Design for Recommender Selection.
Learning Reliable User Representations from Volatile and Sparse Data to Accurately Predict Customer Lifetime Value.
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data.
DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction.
EXACTA: Explainable Column Annotation.
Medical Entity Relation Verification with Large-scale Machine Reading Comprehension.
Tolerating Data Missing in Breast Cancer Diagnosis from Clinical Ultrasound Reports via Knowledge Graph Inference.
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising.
Representation Learning for Predicting Customer Orders.
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection.
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature.
Multimodal Emergent Fake News Detection via Meta Neural Process Networks.
Reinforcing Pretrained Models for Generating Attractive Text Advertisements.
Tac-Valuer: Knowledge-based Stroke Evaluation in Table Tennis.
Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning.
Live-Streaming Fraud Detection: A Heterogeneous Graph Neural Network Approach.
Record: Joint Real-Time Repositioning and Charging for Electric Carsharing with Dynamic Deadlines.
MeLL: Large-scale Extensible User Intent Classification for Dialogue Systems with Meta Lifelong Learning.
Bipartite Dynamic Representations for Abuse Detection.
Clockwork: A Delay-Based Global Scheduling Framework for More Consistent Landing Times in the Data Warehouse.
Recommending the Most Effective Intervention to Improve Employment for Job Seekers with Disability.
Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms.
A PLAN for Tackling the Locust Crisis in East Africa: Harnessing Spatiotemporal Deep Models for Locust Movement Forecasting.
MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph.
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions.
Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries.
Interpretable Drug Response Prediction using a Knowledge-based Neural Network.
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing.
Does Air Quality Really Impact COVID-19 Clinical Severity: Coupling NASA Satellite Datasets with Geometric Deep Learning.
Predicting COVID-19 Spread from Large-Scale Mobility Data.
Contextual Bandit Applications in a Customer Support Bot.
A Bayesian Approach to In-Game Win Probability in Soccer.
RAPT: Pre-training of Time-Aware Transformer for Learning Robust Healthcare Representation.
Lambda Learner: Fast Incremental Learning on Data Streams.
Bootstrapping Recommendations at Chrome Web Store.
User Consumption Intention Prediction in Meituan.
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization.
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units.
A Multi-Graph Attributed Reinforcement Learning based Optimization Algorithm for Large-scale Hybrid Flow Shop Scheduling Problem.
SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce.
Diversity driven Query Rewriting in Search Advertising.
Zero-shot Multi-lingual Interrogative Question Generation for "People Also Ask" at Bing.
VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search.
What Happened Next? Using Deep Learning to Value Defensive Actions in Football Event-Data.
AliCoCo2: Commonsense Knowledge Extraction, Representation and Application in E-commerce.
Que2Search: Fast and Accurate Query and Document Understanding for Search at Facebook.
Pre-trained Language Model for Web-scale Retrieval in Baidu Search.
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising.
Lane Change Scheduling for Autonomous Vehicle: A Prediction-and-Search Framework.
Trustworthy and Powerful Online Marketplace Experimentation with Budget-split Design.
KompaRe: A Knowledge Graph Comparative Reasoning System.
Categorization of Financial Transactions in QuickBooks.
JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baidu.
Intention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection.
Large-Scale Network Embedding in Apache Spark.
PAM: Understanding Product Images in Cross Product Category Attribute Extraction.
M6: Multi-Modality-to-Multi-Modality Multitask Mega-transformer for Unified Pretraining.
Unveiling Fake Accounts at the Time of Registration: An Unsupervised Approach.
Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition.
Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding.
OpenBox: A Generalized Black-box Optimization Service.
An Experimental Study of Quantitative Evaluations on Saliency Methods.
Debiasing Learning based Cross-domain Recommendation.
Embedding-based Product Retrieval in Taobao Search.
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction.
SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations.
Diet Planning with Machine Learning: Teacher-forced REINFORCE for Composition Compliance with Nutrition Enhancement.
Architecture and Operation Adaptive Network for Online Recommendations.
Micro-climate Prediction - Multi Scale Encoder-decoder based Deep Learning Framework.
Addressing Non-Representative Surveys using Multiple Instance Learning.
Network Experimentation at Scale.
FleetRec: Large-Scale Recommendation Inference on Hybrid GPU-FPGA Clusters.
Bootstrapping for Batch Active Sampling.
Knowledge-Guided Efficient Representation Learning for Biomedical Domain.
MPCSL - A Modular Pipeline for Causal Structure Learning.
Deep Inclusion Relation-aware Network for User Response Prediction at Fliggy.
Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters.
Sliding Spectrum Decomposition for Diversified Recommendation.
HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps.
Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach.
Analysis of Faces in a Decade of US Cable TV News.
Purify and Generate: Learning Faithful Item-to-Item Graph from Noisy User-Item Interaction Behaviors.
A Unified Solution to Constrained Bidding in Online Display Advertising.
Neural Instant Search for Music and Podcast.
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud.
Adversarial Feature Translation for Multi-domain Recommendation.
Hierarchical Reinforcement Learning for Scarce Medical Resource Allocation with Imperfect Information.
MEDTO: Medical Data to Ontology Matching Using Hybrid Graph Neural Networks.
Budget Allocation as a Multi-Agent System of Contextual & Continuous Bandits.
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism.
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising.
An Embedding Learning Framework for Numerical Features in CTR Prediction.
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search.
Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling.
A Deep Learning Method for Route and Time Prediction in Food Delivery Service.
Adversarial Attacks on Deep Models for Financial Transaction Records.
Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering.
MoCha: Large-Scale Driving Pattern Characterization for Usage-based Insurance.
SSML: Self-Supervised Meta-Learner for En Route Travel Time Estimation at Baidu Maps.
Heterogeneous Temporal Graph Transformer: An Intelligent System for Evolving Android Malware Detection.
Meta-Learned Spatial-Temporal POI Auto-Completion for the Search Engine at Baidu Maps.
Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies.
Clustering for Private Interest-based Advertising.
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning.
Improving Protein Function Annotation via Unsupervised Pre-training: Robustness, Efficiency, and Insights.
Deep Learning based Crop Row Detection with Online Domain Adaptation.
Causal and Interpretable Rules for Time Series Analysis.
Globally Optimized Matchmaking in Online Games.
On Post-selection Inference in A/B Testing.
Would Your Tweet Invoke Hate on the Fly? Forecasting Hate Intensity of Reply Threads on Twitter.
Theory meets Practice at the Median: A Worst Case Comparison of Relative Error Quantile Algorithms.
FASER: Seismic Phase Identifier for Automated Monitoring.
Robust Object Detection Fusion Against Deception.
Curriculum Meta-Learning for Next POI Recommendation.
PD-Net: Quantitative Motor Function Evaluation for Parkinson's Disease via Automated Hand Gesture Analysis.
Web-Scale Generic Object Detection at Microsoft Bing.
Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction.
When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control.
Extreme Multi-label Learning for Semantic Matching in Product Search.
Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling.
Interactive Audience Expansion On Large Scale Online Visitor Data.
Generating Mobility Trajectories with Retained Data Utility.
A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps.
GEM: Translation-Free Zero-Shot Global Entity Matcher for Global Catalogs.
VisRel: Media Search at Scale.
A Framework for Modeling Cyber Attack Techniques from Security Vulnerability Descriptions.
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations.
Unpaired Generative Molecule-to-Molecule Translation for Lead Optimization.
Auto-Split: A General Framework of Collaborative Edge-Cloud AI.
On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition.
Quantifying and Addressing Ranking Disparity in Human-Powered Data Acquisition.
Dynamic Language Models for Continuously Evolving Content.
All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting.
Counterfactual Graphs for Explainable Classification of Brain Networks.
Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization.
TDGIA: Effective Injection Attacks on Graph Neural Networks.
Controllable Generation from Pre-trained Language Models via Inverse Prompting.
Popularity Bias in Dynamic Recommendation.
S-LIME: Stabilized-LIME for Model Explanation.
Modeling Context-aware Features for Cognitive Diagnosis in Student Learning.
PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network.
Maximizing Influence of Leaders in Social Networks.
Table2Charts: Recommending Charts by Learning Shared Table Representations.
Triplet Attention: Rethinking the Similarity in Transformers.
Quantifying Assimilate-Contrast Effects in Online Rating Systems: Modeling, Analysis and Application.
Knowledge is Power: Hierarchical-Knowledge Embedded Meta-Learning for Visual Reasoning in Artistic Domains.
Accelerating Set Intersections over Graphs by Reducing-Merging.
Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation.
Multi-graph Multi-label Learning with Dual-granularity Labeling.
Cluster-Reduce: Compressing Sketches for Distributed Data Streams.
Temporal Biased Streaming Submodular Optimization.
Fairness-Aware Online Meta-learning.
DHS: Adaptive Memory Layout Organization of Sketch Slots for Fast and Accurate Data Stream Processing.
H2MN: Graph Similarity Learning with Hierarchical Hypergraph Matching Networks.
Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems.
Multi-Task Learning via Generalized Tensor Trace Norm.
Learning Based Proximity Matrix Factorization for Node Embedding.
ROD: Reception-aware Online Distillation for Sparse Graphs.
Where are we in embedding spaces?
Balancing Consistency and Disparity in Network Alignment.
Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups.
Attentive Heterogeneous Graph Embedding for Job Mobility Prediction.
Knowledge-Enhanced Domain Adaptation in Few-Shot Relation Classification.
ELITE: Robust Deep Anomaly Detection with Meta Gradient.
Data Poisoning Attacks Against Outcome Interpretations of Predictive Models.
Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data.
Domain-oriented Language Modeling with Adaptive Hybrid Masking and Optimal Transport Alignment.
Efficient Incremental Computation of Aggregations over Sliding Windows.
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits.
A Transformer-based Framework for Multivariate Time Series Representation Learning.
Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations.
Efficient Optimization Methods for Extreme Similarity Learning with Nonlinear Embeddings.
Socially-Aware Self-Supervised Tri-Training for Recommendation.
A Novel Multi-View Clustering Method for Unknown Mapping Relationships Between Cross-View Samples.
Fed2: Feature-Aligned Federated Learning.
Extremely Compact Non-local Representation Learning.
Performance-Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks.
Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts.
Defending Privacy Against More Knowledgeable Membership Inference Attackers.
Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks.
Context-aware Outstanding Fact Mining from Knowledge Graphs.
TopNet: Learning from Neural Topic Model to Generate Long Stories.
Numerical Formula Recognition from Tables.
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space.
Model-Based Counterfactual Synthesizer for Interpretation.
MTC: Multiresolution Tensor Completion from Partial and Coarse Observations.
Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification.
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search.
Removing Disparate Impact on Model Accuracy in Differentially Private Stochastic Gradient Descent.
Contrastive Multi-View Multiplex Network Embedding with Applications to Robust Network Alignment.
Partial Multi-Label Learning with Meta Disambiguation.
Learning How to Propagate Messages in Graph Neural Networks.
Forecasting Interaction Order on Temporal Graphs.
Geometric Graph Representation Learning on Protein Structure Prediction.
MapEmbed: Perfect Hashing with High Load Factor and Fast Update.
Indirect Invisible Poisoning Attacks on Domain Adaptation.
Quantifying Uncertainty in Deep Spatiotemporal Forecasting.
Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems.
Enhancing SVMs with Problem Context Aware Pipeline.
Towards Robust Prediction on Tail Labels.
Probabilistic Label Tree for Streaming Multi-Label Learning.
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System.
TUTA: Tree-based Transformers for Generally Structured Table Pre-training.
Zero-shot Node Classification with Decomposed Graph Prototype Network.
Error-Bounded Online Trajectory Simplification with Multi-Agent Reinforcement Learning.
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning.
Meta Self-training for Few-shot Neural Sequence Labeling.
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning.
Deconfounded Recommendation for Alleviating Bias Amplification.
Deep Learning Embeddings for Data Series Similarity Search.
Relational Message Passing for Knowledge Graph Completion.
Approximate Graph Propagation.
JOHAN: A Joint Online Hurricane Trajectory and Intensity Forecasting Framework.
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective.
Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation.
Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control.
Alphacore: Data Depth based Core Decomposition.
Environment Agnostic Invariant Risk Minimization for Classification of Sequential Datasets.
The Generalized Mean Densest Subgraph Problem.
Statistical Models Coupling Allows for Complex Local Multivariate Time Series Analysis.
Learning Interpretable Feature Context Effects in Discrete Choice.
Choice Set Confounding in Discrete Choice.
Analysis and Applications of Class-wise Robustness in Adversarial Training.
Norm Adjusted Proximity Graph for Fast Inner Product Retrieval.
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns.
A Stagewise Hyperparameter Scheduler to Improve Generalization.
Redescription Model Mining.
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression.
Triangle-aware Spectral Sparsifiers and Community Detection.
Robust Learning by Self-Transition for Handling Noisy Labels.
Deep Clustering based Fair Outlier Detection.
Fruit-fly Inspired Neighborhood Encoding for Classification.
Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes.
Learning Process-consistent Knowledge Tracing.
Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviours.
Spectral Clustering of Attributed Multi-relational Graphs.
ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification.
MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi-task Learning.
Individual Treatment Prescription Effect Estimation in a Low Compliance Setting.
ImGAGN: Imbalanced Network Embedding via Generative Adversarial Graph Networks.
Retrieval & Interaction Machine for Tabular Data Prediction.
Network-Wide Traffic States Imputation Using Self-interested Coalitional Learning.
Learning to Recommend Visualizations from Data.
MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling.
Simple Yet Efficient Algorithms for Maximum Inner Product Search via Extreme Order Statistics.
Local Algorithms for Estimating Effective Resistance.
Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering.
Fast and Accurate Partial Fourier Transform for Time Series Data.
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data.
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition.
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes.
Filtration Curves for Graph Representation.
An Efficient Framework for Balancing Submodularity and Cost.
Scalable Hierarchical Agglomerative Clustering.
Semi-Supervised Deep Learning for Multiplex Networks.
DeGNN: Improving Graph Neural Networks with Graph Decomposition.
MULTIVERSE: Mining Collective Data Science Knowledge from Code on the Web to Suggest Alternative Analysis Approaches.
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling.
Temporal Graph Signal Decomposition.
Needle in a Haystack: Label-Efficient Evaluation under Extreme Class Imbalance.
BLOCKSET (Block-Aligned Serialized Trees): Reducing Inference Latency for Tree ensemble Deployment.
Graph Adversarial Attack via Rewiring.
Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks.
Leveraging Latent Features for Local Explanations.
HGK-GNN: Heterogeneous Graph Kernel based Graph Neural Networks.
Dialogue Based Disease Screening Through Domain Customized Reinforcement Learning.
Tail-GNN: Tail-Node Graph Neural Networks.
Online Additive Quantization.
Neural-Answering Logical Queries on Knowledge Graphs.
NewsEmbed: Modeling News through Pre-trained Document Representations.
Signed Graph Neural Network with Latent Groups.
Reinforced Anchor Knowledge Graph Generation for News Recommendation Reasoning.
ControlBurn: Feature Selection by Sparse Forests.
Multi-view Correlation based Black-box Adversarial Attack for 3D Object Detection.
What Do You See?: Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors.
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport.
Efficient Collaborative Filtering via Data Augmentation and Step-size Optimization.
FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data.
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions.
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity.
An Efficient and Scalable Algorithm for Estimating Kemeny's Constant of a Markov Chain on Large Graphs.
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs.
A Difficulty-Aware Framework for Churn Prediction and Intervention in Games.
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning.
Physical Equation Discovery Using Physics-Consistent Neural Network (PCNN) Under Incomplete Observability.
Large-Scale Data-Driven Airline Market Influence Maximization.
Dip-based Deep Embedded Clustering with k-Estimation.
Fast Rotation Kernel Density Estimation over Data Streams.
A Color-blind 3-Approximation for Chromatic Correlation Clustering and Improved Heuristics.
Q-Learning Lagrange Policies for Multi-Action Restless Bandits.
Auditing for Diversity Using Representative Examples.
Joint Graph Embedding and Alignment with Spectral Pivot.
Learning to Embed Categorical Features without Embedding Tables for Recommendation.
Topology Distillation for Recommender System.
Preference Amplification in Recommender Systems.
A Hyper-surface Arrangement Model of Ranking Distributions.
Learning to Walk across Time for Interpretable Temporal Knowledge Graph Completion.
Towards a Better Understanding of Linear Models for Recommendation.
Weakly Supervised Spatial Deep Learning based on Imperfect Vector Labels with Registration Errors.
Pre-training on Large-Scale Heterogeneous Graph.
Cross-Network Learning with Partially Aligned Graph Convolutional Networks.
ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations.
Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries.
TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction.
Coupled Graph ODE for Learning Interacting System Dynamics.
DisenQNet: Disentangled Representation Learning for Educational Questions.
A Broader Picture of Random-walk Based Graph Embedding.
Scaling Up Graph Neural Networks Via Graph Coarsening.
MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems.
Metric Learning via Penalized Optimization.
Representation Learning on Knowledge Graphs for Node Importance Estimation.
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem.
Uncertainty-Aware Reliable Text Classification.
Federated Adversarial Debiasing for Fair and Transferable Representations.
PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design.
DARING: Differentiable Causal Discovery with Residual Independence.
Pruning-Aware Merging for Efficient Multitask Inference.
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models.
Adaptive Transfer Learning on Graph Neural Networks.
A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks.
Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting.
Graph Summarization with Controlled Utility Loss.
Generalized Zero-Shot Extreme Multi-label Learning.
Subset Node Representation Learning over Large Dynamic Graphs.
Deep Generative Models for Spatial Networks.
Dual Graph enhanced Embedding Neural Network for CTR Prediction.
Learning from Imbalanced and Incomplete Supervision with Its Application to Ride-Sharing Liability Judgment.
UCPhrase: Unsupervised Context-aware Quality Phrase Tagging.
Towards Computing a Near-Maximum Weighted Independent Set on Massive Graphs.
Meaning Error Rate: ASR domain-specific metric framework.
Boosted Second Price Auctions: Revenue Optimization for Heterogeneous Bidders.
Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints.
Unsupervised Graph Alignment with Wasserstein Distance Discriminator.
Efficient Data-specific Model Search for Collaborative Filtering.
Probabilistic and Dynamic Molecule-Disease Interaction Modeling for Drug Discovery.
ProgRPGAN: Progressive GAN for Route Planning.
Differentiable Pattern Set Mining.
Multiple-Instance Learning from Similar and Dissimilar Bags.
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting.
Gaussian Process with Graph Convolutional Kernel for Relational Learning.
Large-Scale Subspace Clustering via k-Factorization.
When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods.
TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data.
Sylvester Tensor Equation for Multi-Way Association.
Individual Fairness for Graph Neural Networks: A Ranking based Approach.
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs.
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks.
ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting.
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution.
MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification.
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics.
NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs.
Labeled Data Generation with Inexact Supervision.
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility.
Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages.
Graph Similarity Description: How Are These Graphs Similar?
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data.
Improve Learning from Crowds via Generative Augmentation.
Interpreting Internal Activation Patterns in Deep Temporal Neural Networks by Finding Prototypes.
Causal Understanding of Fake News Dissemination on Social Media.
Learning Elastic Embeddings for Customizing On-Device Recommenders.
PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks.
On Breaking Truss-Based Communities.
Graph Deep Factors for Forecasting with Applications to Cloud Resource Allocation.
How Interpretable and Trustworthy are GAMs?
Aggregating Complex Annotations via Merging and Matching.
Causal Models for Real Time Bidding with Repeated User Interactions.
Fast One-class Classification using Class Boundary-preserving Random Projections.
Uplift Modeling with Generalization Guarantees.
Partial Label Dimensionality Reduction via Confidence-Based Dependence Maximization.
Multi-facet Contextual Bandits: A Neural Network Perspective.
Why Attentions May Not Be Interpretable?
Fine-Grained System Identification of Nonlinear Neural Circuits.
LawyerPAN: A Proficiency Assessment Network for Trial Lawyers.
On the Nature of Data Science.
Safe Learning in Robotics.
Data Science for Assembly Engineering.
Automated Mechanism Design for Strategic Classification: Abstract for KDD'21 Keynote Talk.
