5 citations · 5 across the 6 of their papers we have counts for
6 papers
How Does Distribution Matching Help Domain Generalization: An Information-theoretic Analysis
Yuxin Dong, Tieliang Gong, Hong Chen +3
Domain generalization aims to learn invariance across multiple training domains, thereby enhancing generalization against out-of-distribution data. While gradient or representation…
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…
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…