313 citations · 462 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Wasserstein Robust Reinforcement Learning
Mohammed Amin Abdullah, Hang Ren, Haitham Bou Ammar +4
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes $\text{W}\text{R}…
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Ying Wen, Yaodong Yang, Rui Luo +2
Humans are capable of attributing latent mental contents such as beliefs or intentions to others. The social skill is critical in daily life for reasoning about the potential conse…
Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series
Qiang Zhang, Rui Luo, Yaodong Yang +1
Volatility is a quantity of measurement for the price movements of stocks or options which indicates the uncertainty within financial markets. As an indicator of the level of risk…
A Neural Stochastic Volatility Model
Rui Luo, Weinan Zhang, Xiaojun Xu +1
In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation o…