150 citations · 234 across the 29 of their papers we have counts for
29 papers
Fine-tuning a vision-language model for fracture-surface morphology recognition
Quanliang Liu, Jungtaek Kim, Kangwook Lee +1
Vision-language models (VLMs) have shown strong potential for scientific image understanding, but general-purpose models often lack the domain-specific visual knowledge required fo…
Exploration and Exploitation Errors Are Measurable for Language Model Agents
Jaden Park, Jungtaek Kim, Jongwon Jeong +3
Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. A core requirement in these settings is the ability to…
GP-4DGS: Probabilistic 4D Gaussian Splatting from Monocular Video via Variational Gaussian Processes
Mijeong Kim, Jungtaek Kim, Bohyung Han
We present GP-4DGS, a novel framework that integrates Gaussian Processes (GPs) into 4D Gaussian Splatting (4DGS) for principled probabilistic modeling of dynamic scenes. While exis…
Transformers in the Dark: Navigating Unknown Search Spaces via Bandit Feedback
Jungtaek Kim, Thomas Zeng, Ziqian Lin +5
Effective problem solving with Large Language Models (LLMs) can be enhanced when they are paired with external search algorithms. By viewing the space of diverse ideas and their fo…
TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents
Jongwon Jeong, Jungtaek Kim, Kangwook Lee
Language Model (LM) agents have demonstrated remarkable capabilities in solving tasks that require multiple interactions with the environment. However, they remain vulnerable in en…
ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning
Yuchen Zeng, Shuibai Zhang, Wonjun Kang +9
Large Reasoning Models (LRMs) are Large Language Models (LLMs) explicitly trained to generate long-form Chain-of-Thoughts (CoTs), achieving impressive success on challenging tasks…
VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
Thomas Zeng, Shuibai Zhang, Shutong Wu +13
Process Reward Models (PRMs) have proven effective at enhancing mathematical reasoning for Large Language Models (LLMs) by leveraging increased inference-time computation. However,…
Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
Chaeyun Jang, Hyungi Lee, Jungtaek Kim +1
Fine-tuning pre-trained models for downstream tasks is a widely adopted technique known for its adaptability and reliability across various domains. Despite its conceptual simplici…
Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity
Hyunsoo Chung, Jungtaek Kim, Hyungeun Jo +1
A choice of optimization objective is immensely pivotal in the design of a recommender system as it affects the general modeling process of a user's intent from previous interactio…
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization
Kwang-Sung Jun, Jungtaek Kim
Adapting to a priori unknown noise level is a very important but challenging problem in sequential decision-making as efficient exploration typically requires knowledge of the nois…
Beyond Regrets: Geometric Metrics for Bayesian Optimization
Jungtaek Kim
Bayesian optimization is a principled optimization strategy for a black-box objective function. It shows its effectiveness in a wide variety of real-world applications such as scie…
Generative Neural Fields by Mixtures of Neural Implicit Functions
Tackgeun You, Mijeong Kim, Jungtaek Kim +1
We propose a novel approach to learning the generative neural fields represented by linear combinations of implicit basis networks. Our algorithm learns basis networks in the form…
Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations
Jungtaek Kim, Mingxuan Li, Oliver Hinder +1
Nanophotonic structures have versatile applications including solar cells, anti-reflective coatings, electromagnetic interference shielding, optical filters, and light emitting dio…
Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions
Jungtaek Kim, Jeongbeen Yoon, Minsu Cho
Sorting is a fundamental operation of all computer systems, having been a long-standing significant research topic. Beyond the problem formulation of traditional sorting algorithms…
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
Jungtaek Kim
Bayesian optimization has attracted huge attention from diverse research areas in science and engineering, since it is capable of efficiently finding a global optimum of an expensi…
Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction
Seokjun Ahn, Jungtaek Kim, Minsu Cho +1
We tackle the problem of sequential brick assembly with LEGO bricks to create combinatorial 3D structures. This problem is challenging since this brick assembly task encompasses th…
Learning to Assemble Geometric Shapes
Jinhwi Lee, Jungtaek Kim, Hyunsoo Chung +2
Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering. Pre…
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization
Jungtaek Kim, Seungjin Choi
Sequential model-based optimization sequentially selects a candidate point by constructing a surrogate model with the history of evaluations, to solve a black-box optimization prob…
On Evaluation Metrics for Graph Generative Models
Rylee Thompson, Boris Knyazev, Elahe Ghalebi +2
In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), mak…
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Hyunsoo Chung, Jungtaek Kim, Boris Knyazev +4
Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possib…
Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytopes
Jungtaek Kim, Seungjin Choi, Minsu Cho
Bayesian optimization is a popular method for solving the problem of global optimization of an expensive-to-evaluate black-box function. It relies on a probabilistic surrogate mode…
Bootstrapping Neural Processes
Juho Lee, Yoonho Lee, Jungtaek Kim +3
Unlike in the traditional statistical modeling for which a user typically hand-specify a prior, Neural Processes (NPs) implicitly define a broad class of stochastic processes with…
Combinatorial 3D Shape Generation via Sequential Assembly
Jungtaek Kim, Hyunsoo Chung, Jinhwi Lee +2
Sequential assembly with geometric primitives has drawn attention in robotics and 3D vision since it yields a practical blueprint to construct a target shape. However, due to its c…
Bayesian Optimization with Approximate Set Kernels
Jungtaek Kim, Michael McCourt, Tackgeun You +2
We propose a practical Bayesian optimization method over sets, to minimize a black-box function that takes a set as a single input. Because set inputs are permutation-invariant, tr…