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DS535 (2024F)

• [KDD-23] Adaptive Graph Contrastive Learning for Recommendation
• [NeurIPS - 23] Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
• [WWW-24] AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
• [WWW-24] Can Small Language Models be Good Reasoners for Sequential Recommendation?
• [ICML-24] Class-Imbalanced Graph Learning without Class Rebalancing
• [NeurIPS-2022] Contrastive Graph Structure Learning via Information Bottleneck for Recommendation
• [SIGIR-23] Curse of Low Dimensionality in Recommender Systems
• [CVPR-2023] Deep Graph Reprogramming
• [KDD-2023] Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network
• [SIGIR-24] Diffusion Models for Generative Outfit Recommendation
• [NIPS-23] Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis
• [AAAI 2024] Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
• [AAAI-2024] Fine-Tuning Large Language Model Based Explainable Recommendation with Explainable Quality Reward
• [KDD-24] Generative Flow Network for Listwise Recommendation
• [AAAI-24] GINSD Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion
• [NeurIPS-22] Interaction Modeling with Multiplex Attention
• [AAAI-2024] Label Attentive Distillation for GNN-Based Graph Classification
• [NIPS-21] Language Models as Recommender Systems: Evaluations and Limitations
• [CIKM-2024] Learnable Item Tokenization for Generative Recommendation
• [CIKM-21] Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation
• [NeurIPS 2022] Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning
• [WSDM-24] LLMRec: Large Language Models with Graph Augmentation for Recommendation
• [AAAI-24] No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation
• [ICLR-22] On Evaluation Metrics For Graph Generative Models
• [WWW-2024] On the Feasibility of Simple Transformer for Dynamic Graph Modeling
• [SIGIR 2024] Pacer and Runner Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation
• [KDD-24] Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias
• [KDD 2024] RecExplainer: Aligning Large Language Models for Explaining Recommendation Models
• [ICML 2024] Recurrent Distance Filtering for Graph Representation Learning
• [SIGIR-24] SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation
• [NeurIPS-24] SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
• [ACL-24] Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs

DS503 (2024S)

• [ICLR 2024] Large Language Models Are Not Robust Multiple Choice Selectors
• [ICLR 2024] Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
• [CIKM 2023] Timestamps as Prompts for Geography-Aware Location Recommendation
• [CVPR 2023] DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices
• [NeurIPS 2023] Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
• [ICLR 2023] Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
• [ICLR 2023] CALIBRATING TRANSFORMERS VIA SPARSE GAUSSIAN PROCESSES
• [CVPR 2022] Text to Image Generation with Semantic-Spatial Aware GAN
• [ICML 2022] DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting
• [ICLR 2023] Learning Multimodal Data Augmentation in Feature Space
• [IJCAI 2021] Contrastive Losses and Solution Caching for Predict-and-Optimize
• [NeurIPS 2022] Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses
• [ICLR 2023] Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching
• [ESWA 2023] ProMIL: A weakly supervised multiple instance learning for whole slide image classification based on class proxy
• [ICDM 2023] Reserve Price optimization in First-Price Auctions via Multi-Task Learning
• [ICLR 2024] AnomalyCLIP:Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
• [CVPR 2022] End-to-end Generative Pretraining for Multimodal Video Captioning
• [CVPR 2023] TryOnDiffusion: A Tale of Two UNets
• [NeurIPS 2023] Quantification of Uncertainty with Adversarial Models
• [AAAI 2022] Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
• [WACV 2024] MIST: Medical Image Segmentation Transformer With Convolutional Attention Mixing (CAM) Decoder
• [ICML 2023] A Watermark for Large Language Models
• [ECCV 2022] Registration based Few-Shot Anomaly Detection
• [ICLR 2024] Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
• [NeurIPS 2022] Simulation-guided Beam search for Neural Combinatorial Optimization
• [ICLR 2024] Training Diffusion Models With Reinforcement Learning
• [NeurIPS 2023] EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought
• [ICLR 2024] Diffusion-TS: Interpretable Diffusion for General Time Series Generation
• [ICMLR 2022] A Psychological Theory of Explainability
• [ICCV 2023] 3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation
• [ICLR 2021] Dataset Condensation with Gradient Matching
• [ICLR 2023] Unsupervised Model Selection for Time-series Anomaly Detection
• [ICLR 2023] Relative Representations Enable Zero-Shot Latent Space Communication
• [ICLR 2021] Distilling Knowledge From Reader To Retriever For Question Answering
• [AAAI 2024] Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning
• [AAAI 2023] Contrastive Learning Reduces Hallucination in Conversations
• [CIKM 2023] Region Profile Enhanced Urban Spatio-Temporal Prediction via Adaptive Meta-Learning
• [ICCV 2023] Removing Anomalies as Noises for Industrial Defect Localization
• [AAAI 2024] Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems
• [ICLR 2024] Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
• [ACL 2022] M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
• [CVPR 2022] HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction
• [ICLR 2023] CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition
• [SIGIR 2022] CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space

DS535 (2023F)

• [RecSys 2023] Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
• [CIKM 2020] Multi-modal Knowledge Graphs for Recommender Systems
• [CVPR 2023] Learning to Generte Language-supervised and Open-vocabulary Scene Graph using Pre-trained Visual-Semantic Space
• [ICLR 2023] GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
• [ICLR 2021] Directed Acyclic Graph Neural Networks
• [ICLR 2023] GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
• [ICML 2022] Rethinking Graph Neural Networks for Anomaly Detection
• [ICML 2023] Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
• [KDD 2022] Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis
• [KDD 2022] ROLAND: Graph Learning Framework for Dynamic Graphs
• [KDD 2023] DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection
• [NIPS 2021] A/B Testing for Recommender Systems in a Two-sided Marketplace
• [NIPS 2021] Robustness of Graph Neural Networks at Scale
• [NIPS 2021] A 3D Generative Model for Structure-based Drug Design
• [RecSys 2021] Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders
• [RecSys 2022] Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning
• [RecSys 2023] STRec: Sparse Transformer for Sequential Recommendations
• [RecSys 2020] Fit to Run: Personalised Recommendations for Marathon Training
• [RecSys 2022] Don’t recommend the obvious: estimate probability ratios
• [RecSys 2023] Trending Now: Modeling Trend Recommendations
• [SIGIR 2020] Neural Interactive Collaborative Filtering
• [SIGIR 2021] ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation
• [SIGIR 2022] User-controllable Recommendation Against Filter Bubbles
• [SIGIR 2023] Diffusion Recommender Model
• [SIGIR 2020] Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
• [SIGIR 2020] LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• [SIGIR 2023] Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation
• [TSRML 2022] Private Data Leakage via Exploiting Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
• [WSDM 2020] RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
• [WWW 2023] HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer
• [AAAI 2023] Simple and Efficient Heterogeneous Graph Neural Network

DS503 (2023S)

• [NIPS 2021] Learning Graph Models for Retrosynthesis Prediction
• [NIPS 2021] Subgraph Federated Learning with Missing Neighbor Generation
• [NIPS 2021] Matrix Encoding Networks for Neural Combinatorial Optimization
• [NIPS 2021] ABC: Auxiliary Balanced Classifier for Class-Imbalanced Semi-Supervised Learning
• [NIPS 2022] SHINE: SubHypergraph Inductive Neural nEtwork
• [NIPS 2022] Pure Transformers are Powerful Graph Learner
• [ICLR 2021] Generative Scene Graph Networks
• [ICLR 2022] How to Train Your MAML to Excel in Few-Shot Classification
• [ICLR 2022] Neural Link Prediction with Walk Pooling
• [ICLR 2023] Temporal 2D-Variation Modeling for General Time Series Analysis
• [ICLR 2023] Temporal 2D-Variation Modeling for General Time Series Analysis2
• [ICLR 2023] LEARNING MLPS ON GRAPHS: A UNIFIED VIEW OF EFFECTIVENESS, ROBUSTNESS, AND EFFICIENCY
• [ICLR 2023] PLOT: PROMPT LEARNING WITH OPTIMAL TRANSPORT FOR VISION-LANGUAGE MODELS
• [ICLR 2023] NEURAL DAG SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING
• [ICML 2022] Structure-Aware Transformer for Graph Representation Learning
• [ICML 2020] Diffusion Models for Adversarial Purification
• [ICML 2022] Learning from Counterfactual Links for Link Prediction
• [KDD 2021] MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems
• [KDD 2021] A Multi-Graph Attributed Reinforcement Learning based Optimization Algorithm for Large-scale Hybrid Flow Shop Scheduling Problem
• [KDD 2021] Learning Process-consistent Knowledge Tracing
• [SIGIR 2021] HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing
• [CVPR 2020] Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
• [CVPR 2021] CutPaste : Self-Supervised learning for Anomaly Detection and Localization
• [CVPR 2022] A Stitch in Time Saves Nine
• [ICCV 2021] Feature Importance-aware Transferable Adversarial Attacks
• [ICDM 2022] DAGAD: Data Augmentation for Graph Anomaly Detection
• [WWW 2021] SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation
• [AAAI 2021] TabNet: Attentive Interpretable Tabular Learning
• [CIKM 2022] Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction
• [Nat. Mach. Intell. 2023] Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data
• [ASME 2022] Deep Generative Tread Pattern Design Framework for Efficient Conceptual Design
• [ACL 2021] EASE:Extractive-Abstractive Summarization End-to-End using the Information Bottleneck Principle
• [Entropy 2023] Mining Mobile Network Fraudsters with Augmented Graph
• [T-ITS 2021] Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks
• [Nature Machine Intelligence 2022] Super-resolution generative adversarial networks of randomly-seeded fields

KSE801 (2022F)

• [Recsys 2021] Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?
• [Recsys 2021] Denoising User-aware Memory Network for Recommendation
• [Recsys 2022] A User-Centered Investigation of Personal Music Tours
• [CVPR 2021] Energy-Based Learning for Scene Graph Generation
• [ICDE 2022] Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks
• [KDD 2020] ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps
• [KDD 2021] Relational Message Passing for Knowledge Graph Completion
• [KDD 2022] Streaming Graph Neural Networks via Generative Replay
• [KDD 2022] How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications
• [AAAI 2019] Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP
• [AAAI 2022] Graph Neural Controlled Differential Equations for Traffic Forecasting
• [ICML 2020] Bayesian Graph Neural Networks with Adaptive Connection Sampling
• [ICML 2020] Generalization and Representation Limits of Graph Nueral Networks
• [ICML 2022] How Powerful are Spectral Graph Neural Networks
• [ICML 2022] Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection
• [SIGIR 2022] Few-shot Node Classification on Attributed Networks with Graph Meta-learning​
• [ICLR 2020] Graph Information Bottleneck for Subgraph Recognition
• [ICLR 2022] Online Coreset Selection for Rehearsal-based Continual Learning
• [NeurIPS 2021] Do Transformers Really Perform Bad for Graph Representation