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Jaewon Chu

7 accepted papers

2025

Latent Bayesian Optimization via Autoregressive Normalizing Flows

ICLR 2025oral

Bayesian Optimization (BO) has been recognized for its effectiveness in optimizing expensive and complex objective functions. Recent advancements in Latent Bayesian Optimization (LBO) have shown promise by integrating generative models such as variational autoencoders (VAEs) to manage the complexity…

Cited by 1SourcePDFScholar
2025

PRESTO: Preimage-Informed Instruction Optimization for Prompting Black-Box LLMs

NeurIPS 2025poster

Large language models (LLMs) have achieved remarkable success across diverse domains, due to their strong instruction-following capabilities. This raised interest in optimizing instructions for black-box LLMs, whose internal parameters are inaccessible but popular for their strong performance and ea…

Cited by 0SourceScholar
2024

vid-TLDR: Training Free Token Merging for Light-weight Video Transformer

CVPR 2024poster

Video Transformers have become the prevalent solution for various video downstream tasks with superior expressive power and flexibility. However these video transformers suffer from heavy computational costs induced by the massive number of tokens across the entire video frames which has been the ma…

2023

Advancing Bayesian Optimization via Learning Correlated Latent Space

NeurIPS 2023poster

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 deep generative models such as variational autoencoders leads to effective and efficient Bayesian optimization for stru…

2023

NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA

NeurIPS 2023poster

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 approaches formulate this task as a KG path searching problem, where messages are sequentially propagated from the seed nod…

2023

Open-vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Models

ICCV 2023poster

Video Question Answering (VideoQA) is a challenging task that entails complex multi-modal reasoning. In contrast to multiple-choice VideoQA which aims to predict the answer given several options, the goal of open-ended VideoQA is to answer questions without restricting candidate answers. However, th…

Cited by 7PDFcodeScholar