352 citations · 568 across the 21 of their papers we have counts for
7 papers · 1 filter
Latent Diffusion Energy-Based Model for Interpretable Text Modeling
Peiyu Yu, Sirui Xie, Xiaojian Ma +6
Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling. Fueled by its flexibility in the formulation and st…
Adversarial Option-Aware Hierarchical Imitation Learning
Mingxuan Jing, Wenbing Huang, Fuchun Sun +4
It has been a challenge to learning skills for an agent from long-horizon unannotated demonstrations. Existing approaches like Hierarchical Imitation Learning(HIL) are prone to com…
HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving
Sirui Xie, Xiaojian Ma, Peiyu Yu +3
Humans learn compositional and causal abstraction, \ie, knowledge, in response to the structure of naturalistic tasks. When presented with a problem-solving task involving some obj…
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning
Mark Edmonds, Xiaojian Ma, Siyuan Qi +3
Learning transferable knowledge across similar but different settings is a fundamental component of generalized intelligence. In this paper, we approach the transfer learning chall…
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance
Mingxuan Jing, Xiaojian Ma, Wenbing Huang +4
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations…
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Chao Yang, Xiaojian Ma, Wenbing Huang +4
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations. In contrast to Learning from Demonstration (LfD) that involves…
Learning and Inferring Movement with Deep Generative Model
Mingxuan Jing, Xiaojian Ma, Fuchun Sun +1
Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks. In this paper, we propose an effective…