7 papers · 1 filter
Out-Of-The-Loop Multi-Fidelity Bayesian Optimization
Gustavo Sutter, Hao Wang, Luis Ricardez-Sandoval +2
Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available. Multi-fid…
A Critical Look At Tokenwise Reward-Guided Text Generation
Ahmad Rashid, Ruotian Wu, Julia Grosse +2
Large language models (LLMs) can be improved by aligning with human preferences through fine-tuning -- the so-called reinforcement learning from human feedback (RLHF). However, the…
Uncertainty-Guided Likelihood Tree Search
Julia Grosse, Ruotian Wu, Ahmad Rashid +4
Tree search is a fundamental tool for planning, as many sequential decision-making problems can be framed as searching over tree-structured spaces. We propose an uncertainty-guided…
Towards Cost-Effective Reward Guided Text Generation
Ahmad Rashid, Ruotian Wu, Rongqi Fan +3
Reward-guided text generation (RGTG) has emerged as a viable alternative to offline reinforcement learning from human feedback (RLHF). RGTG methods can align baseline language mode…
A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models
Yanting Miao, William Loh, Pacal Poupart +1
Recent work uses reinforcement learning (RL) to fine-tune text-to-image diffusion models, improving text-image alignment and sample quality. However, existing approaches introduce…
Basis Transformers for Multi-Task Tabular Regression
Wei Min Loh, Jiaqi Shang, Pascal Poupart
Dealing with tabular data is challenging due to partial information, noise, and heterogeneous structure. Existing techniques often struggle to simultaneously address key aspects of…