Real-world data often exhibit imbalanced label distributions. Existing studies on data
imbalance focus on single-domain settings, ie, samples are from the same data distribution …

The ubiquity of implicit feedback makes them the default choice to build modern
recommender systems. Generally speaking, observed interactions are considered as …

Real-world data universally confronts a severe class-imbalance problem and exhibits a long-
tailed distribution, ie, most labels are associated with limited instances. The naïve models …

Vision-language models such as CLIP learn a generic text-image embedding from large-
scale training data. A vision-language model can be adapted to a new classification task …

In class incremental learning (CIL) a model must learn new classes in a sequential manner
without forgetting old ones. However, conventional CIL methods consider a balanced …

Multimodal large language models acquire their instruction-following capabilities through a
two-stage training process: pre-training on image-text pairs and fine-tuning on supervised …

Fine-grained image recognition is a longstanding computer vision challenge that focuses on
differentiating objects belonging to multiple subordinate categories within the same meta …

Offline model-based optimization aims to find a design that maximizes a property of interest
using only an offline dataset, with applications in robot, protein, and molecule design …

Although deep face recognition has achieved impressive progress in recent years,
controversy has arisen regarding discrimination based on skin tone, questioning their …

Deep learning has been the mainstream technique in natural language processing (NLP)
area. However, the techniques require many labeled data and are less generalizable across …