5 papers
Learning to Perturb Hidden Representations for Generalizable Deep Learning
Hua Li
Deep neural networks process data through a cascade of representations: input features, hidden activations, logits, and loss. While perturbations at the input, logit, and label lev…
Gradient Perturbation: Learning to Perturb Gradients for Adaptive Training
Hua Li
Deep neural network training involves both forward propagation (from features through logits to loss) and backward propagation (from loss through gradients to parameter updates). W…
Fine-Tuning Diffusion-Based Recommender Systems via Reinforcement Learning with Reward Function Optimization
Yu Hou, Hua Li, Ha Young Kim +1
Diffusion models recently emerged as a powerful paradigm for recommender systems, offering state-of-the-art performance by modeling the generative process of user-item interactions…
Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning
Amit Sharma, Hua Li, Xue Li +1
Given an input query, a recommendation model is trained using user feedback data (e.g., click data) to output a ranked list of items. In real-world systems, besides accuracy, an im…
Task Facet Learning: A Structured Approach to Prompt Optimization
Gurusha Juneja, Gautam Jajoo, Nagarajan Natarajan +3
Given a task in the form of a basic description and its training examples, prompt optimization is the problem of synthesizing the given information into a text prompt for a large l…