75 citations · 248 across the 14 of their papers we have counts for
5 papers · 1 filter
On the Embedding Collapse when Scaling up Recommendation Models
Xingzhuo Guo, Junwei Pan, Ximei Wang +3
Recent advances in foundation models have led to a promising trend of developing large recommendation models to leverage vast amounts of available data. Still, mainstream models re…
Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning
Ximei Wang, Junwei Pan, Xingzhuo Guo +2
Multi-domain learning (MDL) aims to train a model with minimal average risk across multiple overlapping but non-identical domains. To tackle the challenges of dataset bias and doma…
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Liangcai Su, Junwei Pan, Ximei Wang +4
Multi-task learning (MTL) has gained significant popularity in recommender systems as it enables simultaneous optimization of multiple objectives. A key challenge in MTL is negativ…
CLIPood: Generalizing CLIP to Out-of-Distributions
Yang Shu, Xingzhuo Guo, Jialong Wu +3
Out-of-distribution (OOD) generalization, where the model needs to handle distribution shifts from training, is a major challenge of machine learning. Contrastive language-image pr…
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Junguang Jiang, Baixu Chen, Junwei Pan +4
Auxiliary-Task Learning (ATL) aims to improve the performance of the target task by leveraging the knowledge obtained from related tasks. Occasionally, learning multiple tasks simu…