Kim, Seyoung

16 publications

AISTATS 2023 Factorial SDE for Multi-Output Gaussian Process Regression Daniel P. Jeong, Seyoung Kim

AISTATS 2022 Doubly Mixed-Effects Gaussian Process Regression Jun Ho Yoon, Daniel P. Jeong, Seyoung Kim

JMLR 2022 EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation Jun Ho Yoon, Seyoung Kim

UAI 2020 EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices Jun Ho Yoon, Seyoung Kim

AISTATS 2020 Multi-Level Gaussian Graphical Models Conditional on Covariates Gi Bum Kim, Seyoung Kim

AISTATS 2016 Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models Calvin McCarter, Seyoung Kim

NeurIPS 2014 On Sparse Gaussian Chain Graph Models Calvin McCarter, Seyoung Kim

NeurIPS 2013 A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables Jing Xiang, Seyoung Kim

AISTATS 2012 Joint Estimation of Structured Sparsity and Output Structure in Multiple-Output Regression via Inverse-Covariance Regularization Kyung-Ah Sohn, Seyoung Kim

UAI 2011 Smoothing Proximal Gradient Method for General Structured Sparse Learning Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing

ICML 2010 Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity Seyoung Kim, Eric P. Xing

NeurIPS 2009 Heterogeneous Multitask Learning with Joint Sparsity Constraints Xiaolin Yang, Seyoung Kim, Eric P. Xing

UAI 2008 Feature Selection via Block-Regularized Regression Seyoung Kim, Eric P. Xing

NeurIPS 2006 Hierarchical Dirichlet Processes with Random Effects Seyoung Kim, Padhraic Smyth

JMLR 2006 Segmental Hidden Markov Models with Random Effects for Waveform Modeling Seyoung Kim, Padhraic Smyth

UAI 2004 Modeling Waveform Shapes with Random E Ects Segmental Hidden Markov Models Seyoung Kim, Padhraic Smyth, Stefan Luther