23 citations · 32 across the 11 of their papers we have counts for
11 papers
Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
Hyeonah Kim, Minsu Kim, Celine Roget +5
The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in pract…
Test-Time Search in Neural Graph Coarsening Procedures for the Capacitated Vehicle Routing Problem
Yoonju Sim, Hyeonah Kim, Changhyun Kwon
The identification of valid inequalities, such as the rounded capacity inequalities (RCIs), is a key component of cutting plane methods for the Capacitated Vehicle Routing Problem…
Neural Genetic Search in Discrete Spaces
Hyeonah Kim, Sanghyeok Choi, Jiwoo Son +2
Effective search methods are crucial for improving the performance of deep generative models at test time. In this paper, we introduce a novel test-time search method, Neural Genet…
Improved Off-policy Reinforcement Learning in Biological Sequence Design
Hyeonah Kim, Minsu Kim, Taeyoung Yun +4
Designing biological sequences with desired properties is challenging due to vast search spaces and limited evaluation budgets. Although reinforcement learning methods use proxy mo…
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim, Sanghyeok Choi, Hyeonah Kim +3
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search. Our method…
Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
Hyeonah Kim, Minsu Kim, Sanghyeok Choi +1
The challenge of discovering new molecules with desired properties is crucial in domains like drug discovery and material design. Recent advances in deep learning-based generative…
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
Jiwoo Son, Minsu Kim, Hyeonah Kim +1
This paper proposes Meta-SAGE, a novel approach for improving the scalability of deep reinforcement learning models for combinatorial optimization (CO) tasks. Our method adapts pre…
A Neural Separation Algorithm for the Rounded Capacity Inequalities
Hyeonah Kim, Jinkyoo Park, Changhyun Kwon
The cutting plane method is a key technique for successful branch-and-cut and branch-price-and-cut algorithms that find the exact optimal solutions for various vehicle routing prob…
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Federico Berto, Chuanbo Hua, Junyoung Park +30
Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement lear…
Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context
Jiwoo Son, Minsu Kim, Sanghyeok Choi +2
Min-max routing problems aim to minimize the maximum tour length among multiple agents by having agents conduct tasks in a cooperative manner. These problems include impactful real…
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
Hyeonah Kim, Minsu Kim, Sungsoo Ahn +1
Deep reinforcement learning (DRL) has significantly advanced the field of combinatorial optimization (CO). However, its practicality is hindered by the necessity for a large number…