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Found 7 papers, 0 papers with code
Integrating Mamba and Transformer for Long-Short Range Time Series Forecasting
In this paper, we propose to leverage a hybrid framework Mambaformer that internally combines Mamba for long-range dependency, and Transformer for short range dependency, for long-short range forecasting.
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction
Recognizing the critical role of confidence in aligning training objectives with evaluation metrics, we propose CPFT, a versatile framework that enhances recommendation confidence by integrating Conformal Prediction (CP)-based losses with CE loss during fine-tuning.
Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models
This work investigates the potential of undermining both fairness and detection performance in abusive language detection.
Collaborative Semantic Alignment in Recommendation Systems
However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic representation spaces and fine-tuning semantic representations for better alignment with warm-start conditions.
When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes
Thus, it is essential to ensure fairness in machine learning models.
Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach
Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes.
Pre-training Graph Neural Network for Cross Domain Recommendation
Then, we transfer the pre-trained graph encoder to initialize the node embeddings on the target domain, which benefits the fine-tuning of the single domain recommender system on the target domain.
