13 papers
Training Diffusion Language Models for Black-Box Optimization
Zipeng Sun, Can Chen, Ye Yuan +4
We study offline black-box optimization (BBO), aiming to discover improved designs from an offline dataset of designs and labels, a problem common in robotics and DNA with limited…
SURGE: Surrogate Gradient Adaptation in Binary Neural Networks
Haoyu Huang, Boyu Liu, Linlin Yang +6
The training of Binary Neural Networks (BNNs) is fundamentally based on gradient approximation for non-differentiable binarization operations (e.g., sign function). However, prevai…
Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization
Yonghan Yang, Ye Yuan, Zipeng Sun +5
Offline black-box optimization aims to discover novel designs with high property scores using only a static dataset, a task fundamentally challenged by the out-of-distribution (OOD…
Retrieval-Augmented Generation for Natural Language Processing: A Survey
Shangyu Wu, Ying Xiong, Yufei Cui +8
Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still…
CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning
Sining Ang, Yuguang Yang, Canyu Chen +1
End-to-end autonomous driving planners are commonly trained by imitating a single logged trajectory, yet evaluated by rule-based planning metrics that measure safety, feasibility,…
Diffusion Large Language Models for Black-Box Optimization
Ye Yuan, Can, Chen +4
Offline black-box optimization (BBO) aims to find optimal designs based solely on an offline dataset of designs and their labels. Such scenarios frequently arise in domains like DN…