5 citations · 9 across the 13 of their papers we have counts for
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Understanding the Generalization Ability of Deep Learning Algorithms: A Kernelized Renyi's Entropy Perspective
Yuxin Dong, Tieliang Gong, Hong Chen +1
Recently, information theoretic analysis has become a popular framework for understanding the generalization behavior of deep neural networks. It allows a direct analysis for stoch…
Optimal Randomized Approximations for Matrix based Renyi's Entropy
Yuxin Dong, Tieliang Gong, Shujian Yu +1
The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions…
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…