66 citations · 84 across the 6 of their papers we have counts for
6 papers
Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
Hyeongmin Cho, Sangkyun Lee
Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning suc…
Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control
Sangkyun Lee, Piotr Sobczyk, Malgorzata Bogdan
In this paper, we propose a new estimation procedure for discovering the structure of Gaussian Markov random fields (MRFs) with false discovery rate (FDR) control, making use of th…
Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems
Sangkyun Lee, Jeonghyun Lee
Deep neural networks (DNNs) have been quite successful in solving many complex learning problems. However, DNNs tend to have a large number of learning parameters, leading to a lar…
Sparse Portfolio Selection via the sorted -Norm
Philipp J. Kremer, Sangkyun Lee, Malgorzata Bogdan +1
We introduce a financial portfolio optimization framework that allows us to automatically select the relevant assets and estimate their weights by relying on a sorted -Norm…
Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with the Ordered l1-Norm
Sangkyun Lee, Damian Brzyski, Malgorzata Bogdan
In this paper we propose a primal-dual proximal extragradient algorithm to solve the generalized Dantzig selector (GDS) estimation problem, based on a new convex-concave saddle-poi…
Approximate Stochastic Subgradient Estimation Training for Support Vector Machines
Sangkyun Lee, Stephen J. Wright
Subgradient algorithms for training support vector machines have been quite successful for solving large-scale and online learning problems. However, they have been restricted to l…