activity
20182020
most citedProbabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning

50 citations · 51 across the 2 of their papers we have counts for

collaborators

5 papers

cs.AI20201 cited

Learning to Infer User Hidden States for Online Sequential Advertising

Zhaoqing Peng, Junqi Jin, Lan Luo +11

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both im…

cs.LG2019

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}…

cs.LG201950 cited

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…

stat.ML2018

Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling

Rui Luo, Qiang Zhang, Yuanyuan Liu

We propose a new sampler that integrates the protocol of parallel tempering with the Nosé-Hoover (NH) dynamics. The proposed method can efficiently draw representative samples from…

cs.LG2018

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…