Portrait de Sanghyeok Choi

Sanghyeok Choi

Collaborateur·rice de recherche - KAIST
Superviseur⋅e principal⋅e
Yoshua Bengio
Sujets de recherche
Apprentissage profond
Modèles probabilistes
Site web
Google Scholar
GitHub

Publications

Improved Off-policy Reinforcement Learning in Biological Sequence Design
Hyeonah Kim
Minsu Kim
Taeyoung YUN
Sanghyeok Choi
Emmanuel Bengio
Alex Hernandez-Garcia
Jinkyoo Park
Designing biological sequences with desired properties is a significant challenge due to the combinatorially vast search space and the high … (voir plus)cost of evaluating each candidate sequence. To address these challenges, reinforcement learning (RL) methods, such as GFlowNets, utilize proxy models for rapid reward evaluation and annotated data for policy training. Although these approaches have shown promise in generating diverse and novel sequences, the limited training data relative to the vast search space often leads to the misspecification of proxy for out-of-distribution inputs. We introduce
2025-10-06
Proceedings of the 42nd International Conference on Machine Learning (publié)
doi.org
arxiv.org
Improved Off-policy Reinforcement Learning in Biological Sequence Design
Hyeonah Kim
Minsu Kim
Taeyoung YUN
Sanghyeok Choi
Emmanuel Bengio
Alex Hernandez-Garcia
Jinkyoo Park
Designing biological sequences with desired properties is challenging due to vast search spaces and limited evaluation budgets. Although rei… (voir plus)nforcement learning methods use proxy models for rapid reward evaluation, insufficient training data can cause proxy misspecification on out-of-distribution inputs. To address this, we propose a novel off-policy search,
2025-10-06
Proceedings of the 42nd International Conference on Machine Learning (publié)
proceedings.mlr.press
Adaptive teachers for amortized samplers
Minsu Kim
Sanghyeok Choi
Taeyoung YUN
Emmanuel Bengio
Leo Feng
Jarrid Rector-Brooks
Sungsoo Ahn
Jinkyoo Park
Nikolay Malkin
Yoshua Bengio
Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnorma… (voir plus)lized density where exact sampling is intractable. When sampling is implemented as a sequential decision-making process, reinforcement learning (RL) methods, such as generative flow networks, can be used to train the sampling policy. Off-policy RL training facilitates the discovery of diverse, high-reward candidates, but existing methods still face challenges in efficient exploration. We propose to use an adaptive training distribution (the Teacher) to guide the training of the primary amortized sampler (the Student) by prioritizing high-loss regions. The Teacher, an auxiliary behavior model, is trained to sample high-error regions of the Student and can generalize across unexplored modes, thereby enhancing mode coverage by providing an efficient training curriculum. We validate the effectiveness of this approach in a synthetic environment designed to present an exploration challenge, two diffusion-based sampling tasks, and four biochemical discovery tasks demonstrating its ability to improve sample efficiency and mode coverage.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim
Sanghyeok Choi
Jiwoo Son
Hyeonah Kim
Jinkyoo Park
Yoshua Bengio
2025-01-22
aistats.org/AISTATS/2025/Conference (poster)
doi.org
openreview.net
Improved Off-policy Reinforcement Learning in Biological Sequence Design
Hyeonah Kim
Minsu Kim
Taeyoung YUN
Sanghyeok Choi
Emmanuel Bengio
Alex Hern'andez-Garc'ia
Jinkyoo Park
Designing biological sequences with desired properties is a significant challenge due to the combinatorially vast search space and the high … (voir plus)cost of evaluating each candidate sequence. To address these challenges, reinforcement learning (RL) methods, such as GFlowNets, utilize proxy models for rapid reward evaluation and annotated data for policy training. Although these approaches have shown promise in generating diverse and novel sequences, the limited training data relative to the vast search space often leads to the misspecification of proxy for out-of-distribution inputs. We introduce
2024-10-06
ArXiv (prépublication)
doi.org
arxiv.org
Adaptive teachers for amortized samplers
Minsu Kim
Sanghyeok Choi
Taeyoung YUN
Emmanuel Bengio
Leo Feng
Jarrid Rector-Brooks
Sungsoo Ahn
Jinkyoo Park
Nikolay Malkin
Yoshua Bengio
Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnorma… (voir plus)lized density where exact sampling is intractable. When sampling is implemented as a sequential decision-making process, reinforcement learning (RL) methods, such as generative flow networks, can be used to train the sampling policy. Off-policy RL training facilitates the discovery of diverse, high-reward candidates, but existing methods still face challenges in efficient exploration. We propose to use an adaptive training distribution (the \teacher) to guide the training of the primary amortized sampler (the \student). The \teacher, an auxiliary behavior model, is trained to sample high-loss regions of the \student and can generalize across unexplored modes, thereby enhancing mode coverage by providing an efficient training curriculum. We validate the effectiveness of this approach in a synthetic environment designed to present an exploration challenge, two diffusion-based sampling tasks, and four biochemical discovery tasks demonstrating its ability to improve sample efficiency and mode coverage. Source code is available at https://github.com/alstn12088/adaptive-teacher.
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
Adaptive teachers for amortized samplers
Minsu Kim
Sanghyeok Choi
Taeyoung YUN
Emmanuel Bengio
Leo Feng
Jarrid Rector-Brooks
Sungsoo Ahn
Jinkyoo Park
Nikolay Malkin
Yoshua Bengio
Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnorma… (voir plus)lized density where exact sampling is intractable. When sampling is implemented as a sequential decision-making process, reinforcement learning (RL) methods, such as generative flow networks, can be used to train the sampling policy. Off-policy RL training facilitates the discovery of diverse, high-reward candidates, but existing methods still face challenges in efficient exploration. We propose to use an adaptive training distribution (the \teacher) to guide the training of the primary amortized sampler (the \student). The \teacher, an auxiliary behavior model, is trained to sample high-loss regions of the \student and can generalize across unexplored modes, thereby enhancing mode coverage by providing an efficient training curriculum. We validate the effectiveness of this approach in a synthetic environment designed to present an exploration challenge, two diffusion-based sampling tasks, and four biochemical discovery tasks demonstrating its ability to improve sample efficiency and mode coverage. Source code is available at https://github.com/alstn12088/adaptive-teacher.
2024-10-02
ArXiv (prépublication)
doi.org
arxiv.org
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim
Sanghyeok Choi
Jiwoo Son
Hyeonah Kim
Jinkyoo Park
Yoshua Bengio
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference an… (voir plus)d parallel stochastic search. Our method first leverages Generative Flow Networks (GFlowNets) to amortize a \emph{multi-modal} prior distribution over combinatorial solution space that encompasses both high-reward and diversified solutions. This prior is iteratively updated via parallel stochastic search in the spirit of Ant Colony Optimization (ACO), leading to the posterior distribution that generates near-optimal solutions. Extensive experiments across seven combinatorial optimization problems demonstrate GFACS's promising performances.
2024-03-11
ArXiv (prépublication)
doi.org
arxiv.org
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim
Sanghyeok Choi
Jiwoo Son
Hyeonah Kim
Jinkyoo Park
Yoshua Bengio
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference an… (voir plus)d parallel stochastic search. Our method first leverages Generative Flow Networks (GFlowNets) to amortize a \emph{multi-modal} prior distribution over combinatorial solution space that encompasses both high-reward and diversified solutions. This prior is iteratively updated via parallel stochastic search in the spirit of Ant Colony Optimization (ACO), leading to the posterior distribution that generates near-optimal solutions. Extensive experiments across seven combinatorial optimization problems demonstrate GFACS's promising performances.
2024-03-11
ArXiv (prépublication)
doi.org
arxiv.org
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim
Sanghyeok Choi
Jiwoo Son
Hyeonah Kim
Jinkyoo Park
Yoshua Bengio
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference an… (voir plus)d parallel stochastic search. Our method first leverages Generative Flow Networks (GFlowNets) to amortize a \emph{multi-modal} prior distribution over combinatorial solution space that encompasses both high-reward and diversified solutions. This prior is iteratively updated via parallel stochastic search in the spirit of Ant Colony Optimization (ACO), leading to the posterior distribution that generates near-optimal solutions. Extensive experiments across seven combinatorial optimization problems demonstrate GFACS's promising performances.
2024-03-11
ArXiv (prépublication)
doi.org
arxiv.org