26 citations · 35 across the 6 of their papers we have counts for
6 papers
Stochastic Scale Invariant Power Iteration for KL-divergence Nonnegative Matrix Factorization
Cheolmin Kim, Youngseok Kim, Diego Klabjan
We introduce a mini-batch stochastic variance-reduced algorithm to solve finite-sum scale invariant problems which cover several examples in machine learning and statistics such as…
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
Youngseok Kim, Wei Wang, Peter Carbonetto +1
We introduce a new empirical Bayes approach for large-scale multiple linear regression. Our approach combines two key ideas: (i) the use of flexible "adaptive shrinkage" priors, wh…
Scale Invariant Power Iteration
Cheolmin Kim, Youngseok Kim, Diego Klabjan
Power iteration has been generalized to solve many interesting problems in machine learning and statistics. Despite its striking success, theoretical understanding of when and how…
Effective Network Compression Using Simulation-Guided Iterative Pruning
Dae-Woong Jeong, Jaehun Kim, Youngseok Kim +2
Existing high-performance deep learning models require very intensive computing. For this reason, it is difficult to embed a deep learning model into a system with limited resource…
Bayesian Model Selection with Graph Structured Sparsity
Youngseok Kim, Chao Gao
We propose a general algorithmic framework for Bayesian model selection. A spike-and-slab Laplacian prior is introduced to model the underlying structural assumption. Using the not…
A fast algorithm for maximum likelihood estimation of mixture proportions using sequential quadratic programming
Youngseok Kim, Peter Carbonetto, Matthew Stephens +1
Maximum likelihood estimation of mixture proportions has a long history, and continues to play an important role in modern statistics, including in development of nonparametric emp…