103 citations · 114 across the 6 of their papers we have counts for
6 papers
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit +3
Faced with problems of increasing complexity, recent research in Bayesian Optimisation (BO) has focused on adapting deep probabilistic models as flexible alternatives to Gaussian P…
Learning Geometric Constraints in Task and Motion Planning
Tianyu Ren, Alexander Imani Cowen-Rivers, Haitham Bou Ammar +1
Searching for bindings of geometric parameters in task and motion planning (TAMP) is a finite-horizon stochastic planning problem with high-dimensional decision spaces. A robot man…
Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flows
Vincent Moens, Aivar Sootla, Haitham Bou Ammar +1
We present a method for conditional sampling for pre-trained normalizing flows when only part of an observation is available. We derive a lower bound to the conditioning variable l…
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Yaodong Yang, Jun Luo, Ying Wen +5
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum f…
Efficient Semi-Implicit Variational Inference
Vincent Moens, Hang Ren, Alexandre Maraval +3
In this paper, we propose CI-VI an efficient and scalable solver for semi-implicit variational inference (SIVI). Our method, first, maps SIVI's evidence lower bound (ELBO) to a for…
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Ming Zhou, Jun Luo, Julian Villella +34
Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently i…