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Hyeonah Kim

Postdoctorat - UdeM
Superviseur⋅e principal⋅e
Co-supervisor
Sujets de recherche
Apprentissage profond
IA pour la science

Publications

Improved Off-policy Reinforcement Learning in Biological Sequence Design
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,
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Improved Off-policy Reinforcement Learning in Biological Sequence Design
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
Improved Off-policy Reinforcement Learning in Biological Sequence Design
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
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
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.
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
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.
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
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.