58 citations · 63 across the 5 of their papers we have counts for
5 papers
Robust and Fast Measure of Information via Low-rank Representation
Yuxin Dong, Tieliang Gong, Shujian Yu +2
The matrix-based Rényi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This i…
Computationally Efficient Approximations for Matrix-based Renyi's Entropy
Tieliang Gong, Yuxin Dong, Shujian Yu +1
The recently developed matrix based Renyi's entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi definite (PSD) matrices in…
Markov subsampling based Huber Criterion
Tieliang Gong, Yuxin Dong, Hong Chen +2
Subsampling is an important technique to tackle the computational challenges brought by big data. Many subsampling procedures fall within the framework of importance sampling, whic…
Regularized Modal Regression on Markov-dependent Observations: A Theoretical Assessment
Tielang Gong, Yuxin Dong, Hong Chen +3
Modal regression, a widely used regression protocol, has been extensively investigated in statistical and machine learning communities due to its robustness to outliers and heavy-t…
A Bayesian Federated Learning Framework with Online Laplace Approximation
Liangxi Liu, Xi Jiang, Feng Zheng +4
Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training, without the need to s…