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SIGKDD Explorations
SIGKDD Explorations
- Home
- Explorations
Explorations Archive
- DBR: Divergence-Based Regularization for Debiasing Natural Language Understanding Models [1]
- AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models [10]
- Is Less Really More? Fake News Detection with Limited Information [20]
- The b2biers System: A Content-Based Perspective on Maximizing Influence and Subscription in Social Networks [32]
- Dual-Target Disjointed Cross-Domain Recommendation Mediated via Latent User Preferences [52]
- MoralBench: Moral Evaluation of LLMs [62]
- Frugal AI: Introduction, Concepts, Development and Open Questions [72]
- Advancing Table Understanding of Large Language Models via Feature Re-ordering [112]
- Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective [124]
- Span-Oriented Information Extraction: A Unified Framework. [137]
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- Blockchain for Large Language Model Security and Safety: A Holistic Survey [1]
- Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges [21]
- Exploring Large Language Models for Feature Selection: A Data-centric Perspective [44]
- Causal inference under limited outcome observability: A case study with Pinterest Conversion Lift [54]
- DiffusionShield: A Watermark for Data Copyright Protection against Generative Diffusion Models [60]
- FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models [76]
- Graph Fairness via Authentic Counterfactuals: Tackling Structural and Causal Challenges. [89]
- FG-SMOTE: Towards Fair Node Classification with Graph Neural Network. [99]
- Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities [109]
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- Higher-Order Networks Representation and Learning: A Survey [1]
- Synthetic data for learning-based knowledge discovery [19]
- The Case for Hybrid Multi-Objective Optimisation in High-Stakes Machine Learning Applications [24]
- Fairness in Large Language Models: A Taxonomic Survey [34]
- Analyzing and explaining privacy risks on time series data: ongoing work and challenges [49]
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- An interview with Dr. Jure Lesvek, Winner of ACM SIGKDD 2023 Innovation Award [1]
- Marginal Nodes Matter: Towards Structure Fairness in Graphs [4]
- Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text? [14]
- Storage Systems: Organization, Performance, Coding, Reliability, and Their Data Processing, 1st Edition, October 13, 2021 [22]
- Report on the 3rd International Workshop on Learning to Quantify (LQ 2023) [25]
- Anomaly Detection using Generative Adversarial Networks [29]
- Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs [42]
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- Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective [1]
- The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies. [19]
- Stop Using the Elbow Criterion for k-means, and How to Choose the Number of Clusters Instead. [36]
- Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking. [43]
- Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey. [54]
- Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising. [73]
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