1 citations · 1 across the 6 of their papers we have counts for
6 papers
Learning to Solve Generative ODEs Beyond the Linear Span
Sihyeon Kim, Seunghun Lee, Vikas Singh +1
Diffusion and flow generative models sample by integrating a learned ODE, but high quality still requires many sequential model evaluations. Solver learning reduces this cost by ad…
PRESTO: Preimage-Informed Instruction Optimization for Prompting Black-Box LLMs
Jaewon Chu, Seunghun Lee, Hyunwoo J. Kim
Large language models (LLMs) have achieved remarkable success across diverse domains, due to their strong instruction-following capabilities. This has led to increasing interest in…
Latent Bayesian Optimization via Autoregressive Normalizing Flows
Seunghun Lee, Jinyoung Park, Jaewon Chu +2
Bayesian Optimization (BO) has been recognized for its effectiveness in optimizing expensive and complex objective functions. Recent advancements in Latent Bayesian Optimization (L…
Inversion-based Latent Bayesian Optimization
Jaewon Chu, Jinyoung Park, Seunghun Lee +1
Latent Bayesian optimization (LBO) approaches have successfully adopted Bayesian optimization over a continuous latent space by employing an encoder-decoder architecture to address…
Advancing Bayesian Optimization via Learning Correlated Latent Space
Seunghun Lee, Jaewon Chu, Sihyeon Kim +2
Bayesian optimization is a powerful method for optimizing black-box functions with limited function evaluations. Recent works have shown that optimization in a latent space through…
NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
Hyeong Kyu Choi, Seunghun Lee, Jaewon Chu +1
Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions. Recent GNN-based appr…