3 papers
cs.IR2026
Exploring Bottom-Up Clustering for Creating Semantic IDs
Leah Woldemariam, Sudhanshu Garg, Taha Belkhouja +2
The success of generative retrieval has largely been attributed to the use of Semantic IDs, which improve over arbitrary item-level identifiers such as hashes by capturing the sema…
cs.CV2026
Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition
Minxue Tang, Yangyang Yu, Aolin Ding +3
Recognizing implicit visual and textual patterns is essential in many real-world applications of modern AI. However, tackling long-tail pattern recognition tasks remains challengin…
cs.LG2024
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration
Yuanjie Shi, Subhankar Ghosh, Taha Belkhouja +2
Conformal prediction (CP) is an emerging uncertainty quantification framework that allows us to construct a prediction set to cover the true label with a pre-specified marginal or…