6 citations · 6 across the 7 of their papers we have counts for
7 papers
VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects
Xiangbo Gao, Sicong Jiang, Bangya Liu +12
As AI-assisted video creation becomes increasingly practical, instruction-guided video editing has become essential for refining generated or captured footage to meet professional…
A Theory of Multi-Agent Generative Flow Networks
Leo Maxime Brunswic, Haozhi Wang, Shuang Luo +3
Generative flow networks utilize a flow-matching loss to learn a stochastic policy for generating objects from a sequence of actions, such that the probability of generating a patt…
Generative Models in Decision Making: A Survey
Xinyu Shao, Jianping Zhang, Haozhi Wang +9
Generative models have fundamentally reshaped the landscape of decision-making, reframing the problem from pure scalar reward maximization to high-fidelity trajectory generation an…
Meta Generative Flow Networks with Personalization for Task-Specific Adaptation
Xinyuan Ji, Xu Zhang, Wei Xi +3
Multi-task reinforcement learning and meta-reinforcement learning have been developed to quickly adapt to new tasks, but they tend to focus on tasks with higher rewards and more fr…
Multi-agent Policy Reciprocity with Theoretical Guarantee
Haozhi Wang, Yinchuan Li, Qing Wang +2
Modern multi-agent reinforcement learning (RL) algorithms hold great potential for solving a variety of real-world problems. However, they do not fully exploit cross-agent knowledg…
CFlowNets: Continuous Control with Generative Flow Networks
Yinchuan Li, Shuang Luo, Haozhi Wang +1
Generative flow networks (GFlowNets), as an emerging technique, can be used as an alternative to reinforcement learning for exploratory control tasks. GFlowNet aims to generate dis…
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies
Haozhi Wang, Qing Wang, Yunfeng Shao +3
Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize polic…