icdm 2021 论文列表
IEEE International Conference on Data Mining, ICDM 2021, Auckland, New Zealand, December 7-10, 2021.
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SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series.
Operation-level Progressive Differentiable Architecture Search.
A new multiple instance algorithm using structural information.
Self-supervised Universal Domain Adaptation with Adaptive Memory Separation.
Multi-Objective Distributional Reinforcement Learning for Large-Scale Order Dispatching.
Joint Scence Network and Attention-Guided for Image Captioning.
Topic-Attentive Encoder-Decoder with Pre-Trained Language Model for Keyphrase Generation.
AdaBoosting Clusters on Graph Neural Networks.
Graph Neighborhood Routing and Random Walk for Session-based Recommendation.
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values.
C3-GAN: Complex-Condition-Controlled Urban Traffic Estimation through Generative Adversarial Networks.
Unified Fairness from Data to Learning Algorithm.
A Multi-view Confidence-calibrated Framework for Fair and Stable Graph Representation Learning.
Generating Structural Node Representations via Higher-order Features and Adversarial Learning.
MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series.
Adaptive Spatio-Temporal Convolutional Network for Traffic Prediction.
Jointly Multi-Similarity Loss for Deep Metric Learning.
Online Testing of Subgroup Treatment Effects Based on Value Difference.
Limited-memory Common-directions Method With Subsampled Newton Directions for Large-scale Linear Classification.
Zero-shot Key Information Extraction from Mixed-Style Tables: Pre-training on Wikipedia.
Towards Stochastic Neural Network via Feature Distribution Calibration.
Overfitting Avoidance in Tensor Train Factorization and Completion: Prior Analysis and Inference.
Boosting Deep Ensemble Performance with Hierarchical Pruning.
Composition-Enhanced Graph Collaborative Filtering for Multi-behavior Recommendation.
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications.
BioHanBERT: A Hanzi-aware Pre-trained Language Model for Chinese Biomedical Text Mining.
Summarizing User-Item Matrix By Group Utility Maximization.
Constrained Non-Affine Alignment of Embeddings.
Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches.
Aspect-based Sentiment Classification via Reinforcement Learning.
Dynamic Attributed Graph Prediction with Conditional Normalizing Flows.
A Lookahead Algorithm for Robust Subspace Recovery.
Detecting adversaries in Crowdsourcing.
DhakaNet: Unstructured Vehicle Detection using Limited Computational Resources.
Streaming Dynamic Graph Neural Networks for Continuous-Time Temporal Graph Modeling.
PSANet - subspace attention for personalized compatibility.
Pest-YOLO: Deep Image Mining and Multi-Feature Fusion for Real-Time Agriculture Pest Detection.
Learning Dynamic User Interactions for Online Forum Commenting Prediction.
Multimodal N-best List Rescoring with Weakly Supervised Pre-training in Hybrid Speech Recognition.
Compressibility of Distributed Document Representations.
T3: Domain-Agnostic Neural Time-series Narration.
Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services.
Alternative Ruleset Discovery to Support Black-box Model Predictions.
Scalable Pareto Front Approximation for Deep Multi-Objective Learning.
Causal Discovery with Flow-based Conditional Density Estimation.
Incomplete Multi-view Multi-label Active Learning.
GQNAS: Graph Q Network for Neural Architecture Search.
Density-Based Clustering for Adaptive Density Variation.
A general framework for mining concept-drifting data streams with evolvable features.
Multi-Classification Prediction of Alzheimer's Disease based on Fusing Multi-modal Features.
STING: Self-attention based Time-series Imputation Networks using GAN.
MetaEDL: Meta Evidential Learning For Uncertainty-Aware Cold-Start Recommendations.
Exploring Reflective Limitation of Behavior Cloning in Autonomous Vehicles.
PhyFlow: Physics-Guided Deep Learning for Generating Interpretable 3D Flow Fields.
Matrix Profile XXIII: Contrast Profile: A Novel Time Series Primitive that Allows Real World Classification.
Semi-Supervised Graph Attention Networks for Event Representation Learning.
Accurately Quantifying under Score Variability.
An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data.
Communication Efficient Tensor Factorization for Decentralized Healthcare Networks.
HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation.
Adversarial Regularized Reconstruction for Anomaly Detection and Generation.
StarGAT: Star-Shaped Hierarchical Graph Attentional Network for Heterogeneous Network Representation Learning.
Robust BiPoly-Matching for Multi-Granular Entities.
Out-of-Category Document Identification Using Target-Category Names as Weak Supervision.
Learnable Structural Semantic Readout for Graph Classification.
Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning.
Learning Personal Human Biases and Representations for Subjective Tasks in Natural Language Processing.
ENGINE: Enhancing Neuroimaging and Genetic Information by Neural Embedding.
Addressing Exposure Bias in Uplift Modeling for Large-scale Online Advertising.
Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks.
An Effective and Robust Framework by Modeling Correlations of Multiplex Network Embedding.
Heterogeneous Graph Neural Network with Distance Encoding.
SCALP - Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata.
Bi-Level Attention Graph Neural Networks.
Spikelet: An Adaptive Symbolic Approximation for Finding Higher-Level Structure in Time Series.
Trajectory WaveNet: A Trajectory-Based Model for Traffic Forecasting.
MC-RGCN: A Multi-Channel Recurrent Graph Convolutional Network to Learn High-Order Social Relations for Diffusion Prediction.
Source Inference Attacks in Federated Learning.
Thin Semantics Enhancement via High-Frequency Priori Rule for Thin Structures Segmentation.
Federated Principal Component Analysis for Genome-Wide Association Studies.
PaGAN: Generative Adversarial Network for Patent understanding.
Recurrent Neural Networks Meet Context-Free Grammar: Two Birds with One Stone.
Attacking Similarity-Based Sign Prediction.
Heterogeneous Graph Neural Architecture Search.
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs.
Fair Graph Auto-Encoder for Unbiased Graph Representations with Wasserstein Distance.
Gain-Some-Lose-Some: Reliable Quantification Under General Dataset Shift.
Temporal Multi-view Graph Convolutional Networks for Citywide Traffic Volume Inference.
Promoting Fairness through Hyperparameter Optimization.
Improving Deep Forest by Exploiting High-order Interactions.
Heterogeneous Stream-reservoir Graph Networks with Data Assimilation.
Self-learn to Explain Siamese Networks Robustly.
Generating Explanations for Recommendation Systems via Injective VAE.
K-means for Evolving Data Streams.
LOGIC: Probabilistic Machine Learning for Time Series Classification.
Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates.
DIVINIA: Rare Object Localization and Search in Overhead Imagery.
TEST-GCN: Topologically Enhanced Spatial-Temporal Graph Convolutional Networks for Traffic Forecasting.
PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series.
Deep Incremental RNN for Learning Sequential Data: A Lyapunov Stable Dynamical System.
Temporal Clustering with External Memory Network for Disease Progression Modeling.
Triplet Deep Subspace Clustering via Self-Supervised Data Augmentation.
Discriminative Additive Scale Loss for Deep Imbalanced Classification and Embedding.
Few-Shot Partial Multi-Label Learning.
DAC-ML: Domain Adaptable Continuous Meta-Learning for Urban Dynamics Prediction.
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations.
Fair Decision-making Under Uncertainty.
Robustifying DARTS by Eliminating Information Bypass Leakage via Explicit Sparse Regularization.
FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance.
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records.
Physics Interpretable Shallow-Deep Neural Networks for Physical System Identification with Unobservability.
AS-GCN: Adaptive Semantic Architecture of Graph Convolutional Networks for Text-Rich Networks.
Accurate Graph-Based PU Learning without Class Prior.
Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting.
Structure-Aware Stabilization of Adversarial Robustness with Massive Contrastive Adversaries.
Graph-based Adversarial Online Kernel Learning with Adaptive Embedding.
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation.
Predictive Modeling of Clinical Events with Mutual Enhancement Between Longitudinal Patient Records and Medical Knowledge Graph.
A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity.
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification.
Impression Allocation and Policy Search in Display Advertising.
A Regularized Wasserstein Framework for Graph Kernels.
PRGAN: Personalized Recommendation with Conditional Generative Adversarial Networks.
Learning to Reweight Samples with Offline Loss Sequence.
Nonlinear Causal Structure Learning for Mixed Data.
Global Convolutional Neural Processes.
Combining Ranking and Point-wise Losses for Training Deep Survival Analysis Models.
Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning.
Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval.
USTEP: Unfixed Search Tree for Efficient Log Parsing.
Precise Bayes Classifier: Summary of Results.
CASPITA: Mining Statistically Significant Paths in Time Series Data from an Unknown Network.
DCF: An Efficient and Robust Density-Based Clustering Method.
Isolation Kernel Density Estimation.
Fast computation of distance-generalized cores using sampling.
A Robust Algorithm to Unifying Offline Causal Inference and Online Multi-armed Bandit Learning.
Ultra fast warping window optimization for Dynamic Time Warping.
Attention-based Feature Interaction for Efficient Online Knowledge Distillation.
PARWiS: Winner determination from Active Pairwise Comparisons under a Shoestring Budget.
Fast Attributed Graph Embedding via Density of States.
Better Prevent than React: Deep Stratified Learning to Predict Hate Intensity of Twitter Reply Chains.
Truth Discovery in Sequence Labels from Crowds.
Robust Low-rank Deep Feature Recovery in CNNs: Toward Low Information Loss and Fast Convergence.
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation.
Powered Hawkes-Dirichlet Process: Challenging Textual Clustering using a Flexible Temporal Prior.
Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning.
Sequential Diagnosis Prediction with Transformer and Ontological Representation.
GraphANGEL: Adaptive aNd Structure-Aware Sampling on Graph NEuraL Networks.
Towards Generating Real-World Time Series Data.
Cutting to the Chase with Warm-Start Contextual Bandits.
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records.
Outlier-Robust Multi-View Subspace Clustering with Prior Constraints.
Multi-way Time Series Join on Multi-length Patterns.
FGC-Stream: A novel joint miner for frequent generators and closed itemsets in data streams.
Multi-objective Explanations of GNN Predictions.
Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy.
Technological Knowledge Flow Forecasting through A Hierarchical Interactive Graph Neural Network.
Deep Generation of Heterogeneous Networks.
SSDNet: State Space Decomposition Neural Network for Time Series Forecasting.
Disentangled Deep Multivariate Hawkes Process for Learning Event Sequences.
Preference-aware Group Task Assignment in Spatial Crowdsourcing: A Mutual Information-based Approach.
Towards Interpretability and Personalization: A Predictive Framework for Clinical Time-series Analysis.
Mcore: Multi-Agent Collaborative Learning for Knowledge-Graph-Enhanced Recommendation.
BaT: Beat-aligned Transformer for Electrocardiogram Classification.
THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting.
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings.
MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems.
Crowdsourcing with Self-paced Workers.
Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems.
Hypergraph Convolutional Network for Group Recommendation.
Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting.
Risk-aware Temporal Cascade Reconstruction to Detect Asymptomatic Cases : For the CDC MInD Healthcare Network.
Climate Modeling with Neural Diffusion Equations.
STAN: Adversarial Network for Cross-domain Question Difficulty Prediction.
Group-Level Cognitive Diagnosis: A Multi-Task Learning Perspective.
Flexible, Robust, Scalable Semi-supervised Learning via Reliability Propagation.
Conversion Prediction with Delayed Feedback: A Multi-task Learning Approach.
Online Learning in Variable Feature Spaces with Mixed Data.
GANBLR: A Tabular Data Generation Model.
LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time.
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions.
Finding Age Path of Self-Paced Learning.
Graph Transfer Learning.
GNES: Learning to Explain Graph Neural Networks.
Dictionary Pair-based Data-Free Fast Deep Neural Network Compression.
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network.
MetaGB: A Gradient Boosting Framework for Efficient Task Adaptive Meta Learning.
Hypergraph Ego-networks and Their Temporal Evolution.
TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection.
Topic-Noise Models: Modeling Topic and Noise Distributions in Social Media Post Collections.
Highly Scalable and Provably Accurate Classification in Poincaré Balls.
Learning Transferable User Representations with Sequential Behaviors via Contrastive Pre-training.
Differentially Private String Sanitization for Frequency-Based Mining Tasks.
Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence.
A Linear Primal-Dual Multi-Instance SVM for Big Data Classifications.
Partial Differential Equation Driven Dynamic Graph Networks for Predicting Stream Water Temperature.
Gated Information Bottleneck for Generalization in Sequential Environments.
