4 papers
Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation
Cedar Site Bai, Duanshun Li, Zhenyu Liao +6
Recent advances in large language models (LLMs) have enabled their use as conversational recommender systems (CRS), demonstrating strong recommendation accuracy and natural dialogu…
Spectral Saliency for Machine Unlearning
Cedar Site Bai, Amber Yijia Zheng, Raymond A. Yeh +1
Machine unlearning (MU) aims to remove the influence of specific training data while preserving model utility. As the name suggests, MU can be viewed as the inverse of learning, us…
Can Entry-Wise Clipping Give Spectral Control of Stochastic Gradients?
Zitao Song, Cedar Site Bai, Zhe Zhang +2
Training instabilities such as loss spikes are frequently the result of stochastic gradient noise. Because of rare expressions in language training data, and multiple layer composi…
Decoupling Variance and Scale-Invariant Updates in Adaptive Gradient Descent for Unified Vector and Matrix Optimization
Zitao Song, Cedar Site Bai, Zhe Zhang +2
Adaptive methods like Adam have become the standard for large-scale vector and Euclidean optimization due to their coordinate-wise adaptation with a second-orde…