4 citations · 9 across the 18 of their papers we have counts for
12 papers · 1 filter
Active Attacks: Red-teaming LLMs via Adaptive Environments
Taeyoung Yun, Pierre-Luc St-Charles, Jinkyoo Park +2
We address the challenge of generating diverse attack prompts for large language models (LLMs) that elicit harmful behaviors (e.g., insults, sexual content) and are used for safety…
Diffusion Alignment as Variational Expectation-Maximization
Jaewoo Lee, Minsu Kim, Sanghyeok Choi +7
Diffusion alignment aims to optimize diffusion models for the downstream objective. While existing methods based on reinforcement learning or direct backpropagation achieve conside…
Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization
Kiyoung Om, Kyuil Sim, Taeyoung Yun +2
Optimizing high-dimensional black-box functions under black-box constraints is a pervasive task in a wide range of scientific and engineering problems. These problems are typically…
Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
Taeyoung Yun, Kiyoung Om, Jaewoo Lee +2
Optimizing high-dimensional and complex black-box functions is crucial in numerous scientific applications. While Bayesian optimization (BO) is a powerful method for sample-efficie…
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…
Adaptive teachers for amortized samplers
Minsu Kim, Sanghyeok Choi, Taeyoung Yun +7
Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnormalized density where exact sampling is in…
An Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management Systems
Taeyoung Yun, Kanghoon Lee, Sujin Yun +6
Complex urban road networks with high vehicle occupancy frequently face severe traffic congestion. Designing an effective strategy for managing multiple traffic lights plays a cruc…
Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization
Taeyoung Yun, Sujin Yun, Jaewoo Lee +1
Optimizing complex and high-dimensional black-box functions is ubiquitous in science and engineering fields. Unfortunately, the online evaluation of these functions is restricted d…
Pessimistic Backward Policy for GFlowNets
Hyosoon Jang, Yunhui Jang, Minsu Kim +2
This paper studies Generative Flow Networks (GFlowNets), which learn to sample objects proportionally to a given reward function through the trajectory of state transitions. In thi…
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…
Learning to Scale Logits for Temperature-Conditional GFlowNets
Minsu Kim, Joohwan Ko, Taeyoung Yun +6
GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, temperature-conditional GFlowNets can introduce…
Local Search GFlowNets
Minsu Kim, Taeyoung Yun, Emmanuel Bengio +4
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards. GFlowNets exhibit a remarkable ab…