21 citations · 28 across the 7 of their papers we have counts for
7 papers
Noise-Augmented Regularization of Tensor Regression with Tucker Decomposition
Tian Yan, Yinan Li, Fang Liu
Tensor data are multi-dimension arrays. Low-rank decomposition-based regression methods with tensor predictors exploit the structural information in tensor predictors while signifi…
Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models
Yinan Li, Fang Liu
We propose the AdaPtive Noise Augmentation (PANDA) procedure to regularize the estimation and inference of generalized linear models (GLMs). PANDA iteratively optimizes the objecti…
Noise-Augmented Privacy-Preserving Empirical Risk Minimization with Dual-purpose Regularizer and Privacy Budget Retrieval and Recycling
Yinan Li, Fang Liu
We propose Noise-Augmented Privacy-Preserving Empirical Risk Minimization (NAPP-ERM) that solves ERM with differential privacy guarantees. Existing privacy-preserving ERM approache…
Continuous-time Markov-switching GARCH Process with Robust and Efficient State Path and Volatility Estimation
Yinan Li, Fang Liu
We propose a continuous-time Markov-switching generalized autoregressive conditional heteroskedasticity (COMS-GARCH) process for handling irregularly spaced time series (TS) with m…
AdaPtive Noisy Data Augmentation (PANDA) for Simultaneous Construction of Multiple Graph Models
Yinan Li, Xiao Liu, Fang Liu
We extend the data augmentation technique PANDA by Li et al. (2018) that regularizes single graph estimation to jointly learning multiple graphical models with various node types i…
PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models
Yinan Li, Xiao Liu, Fang Liu
We propose an AdaPtive Noise Augmentation (PANDA) technique to regularize the estimation and construction of undirected graphical models. PANDA iteratively optimizes the objective…
Whiteout: Gaussian Adaptive Noise Regularization in Deep Neural Networks
Yinan Li, Fang Liu
Noise injection (NI) is an efficient technique to mitigate over-fitting in neural networks (NNs). The Bernoulli NI procedure as implemented in dropout and shakeout has connections…