50 citations · 102 across the 4 of their papers we have counts for
12 papers
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…
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…
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}…
Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
Rui Luo, Qiang Zhang, Yaodong Yang +1
In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated mode…
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…
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…