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Hyeonah Kim

Postdoctorat - UdeM
Superviseur⋅e principal⋅e
Alex Hernandez-Garcia
Co-supervisor
Yoshua Bengio
Sujets de recherche
Apprentissage profond
GFlowNets
IA pour la science
Optimisation combinatoire

Publications

Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
Hyeonah Kim
Minsu Kim
Celine ROGET
D. Biton
Louis Vaillancourt
Yves V. Brun
Yoshua Bengio
Alex Hernandez-Garcia
2026-02-03
arXiv (prépublication)
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,
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
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.
2025-01-21
aistats.org/AISTATS/2025/Conference (poster)
doi.org
proceedings.mlr.press