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20172024
most citedMultilevel Clustering via Wasserstein Means

42 citations · 73 across the 20 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.CV20221 cited

Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering

Duy M. H. Nguyen, Hoang Nguyen, Mai T. N. Truong +7

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs i…

stat.ML2022

Fast Approximation of the Generalized Sliced-Wasserstein Distance

Dung Le, Huy Nguyen, Khai Nguyen +2

Generalized sliced Wasserstein distance is a variant of sliced Wasserstein distance that exploits the power of non-linear projection through a given defining function to better cap…

cs.LG2022

Improving Generative Flow Networks with Path Regularization

Anh Do, Duy Dinh, Tan Nguyen +3

Generative Flow Networks (GFlowNets) are recently proposed models for learning stochastic policies that generate compositional objects by sequences of actions with the probability…

cs.LG2022

Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering

Xing Han, Tongzheng Ren, Jing Hu +2

We propose a novel approach to the problem of clustering hierarchically aggregated time-series data, which has remained an understudied problem though it has several commercial app…

stat.ML20221 cited

Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures

Tongzheng Ren, Fuheng Cui, Sujay Sanghavi +1

The Expectation-Maximization (EM) algorithm has been predominantly used to approximate the maximum likelihood estimation of the location-scale Gaussian mixtures. However, when the…

stat.ML2022

Improving Computational Complexity in Statistical Models with Second-Order Information

Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi +1

It is known that when the statistical models are singular, i.e., the Fisher information matrix at the true parameter is degenerate, the fixed step-size gradient descent algorithm t…