103 citations · 273 across the 23 of their papers we have counts for
12 papers · 1 filter
Measuring the Non-Transitivity in Chess
Ricky Sanjaya, Jun Wang, Yaodong Yang
It has long been believed that Chess is the \emph{Drosophila} of Artificial Intelligence (AI). Studying Chess can productively provide valid knowledge about complex systems. Althou…
Revisiting the Characteristics of Stochastic Gradient Noise and Dynamics
Yixin Wu, Rui Luo, Chen Zhang +2
In this paper, we characterize the noise of stochastic gradients and analyze the noise-induced dynamics during training deep neural networks by gradient-based optimizers. Specifica…
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…
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Ying Wen, Hui Chen, Yaodong Yang +4
Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration. Nonetheless, wh…
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Ming Zhou, Ziyu Wan, Hanjing Wang +6
Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms, which produces a self-generated se…
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval +9
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By…