2 citations · 3 across the 7 of their papers we have counts for
7 papers
Decision-focused Sparse Tangent Portfolio Optimization
Haeun Jeon, Seunghoon Choi, Hyunglip Bae +2
Sparse tangent portfolio optimization aims to learn an interpretable, low-cardinality portfolio in the tangency direction of the mean-variance frontier. However, the associated car…
Prediction Loss Guided Decision-Focused Learning
Haeun Jeon, Hyunglip Bae, Chanyeong Kim +2
Decision-making under uncertainty is often considered in two stages: predicting the unknown parameters, and then optimizing decisions based on predictions. While traditional predic…
A Cholesky decomposition-based asset selection heuristic for sparse tangent portfolio optimization
Hyunglip Bae, Haeun Jeon, Minsu Park +2
In practice, including large number of assets in mean-variance portfolios can lead to higher transaction costs and management fees. To address this, one common approach is to selec…
Return Prediction for Mean-Variance Portfolio Selection: How Decision-Focused Learning Shapes Forecasting Models
Junhyeong Lee, Haeun Jeon, Hyunglip Bae +1
Markowitz laid the foundation of portfolio theory through the mean-variance optimization (MVO) framework. However, the effectiveness of MVO is contingent on the precise estimation…
Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization
Chanyeong Kim, Jongwoong Park, Hyunglip Bae +1
Solving large-scale multistage stochastic programming (MSP) problems poses a significant challenge as commonly used stagewise decomposition algorithms, including stochastic dual dy…
Locally Convex Global Loss Network for Decision-Focused Learning
Haeun Jeon, Hyunglip Bae, Minsu Park +2
In decision-making problems under uncertainty, predicting unknown parameters is often considered independent of the optimization part. Decision-focused learning (DFL) is a task-ori…
Universal Approximation of Parametric Optimization via Neural Networks with Piecewise Linear Policy Approximation
Hyunglip Bae, Jang Ho Kim, Woo Chang Kim
Parametric optimization solves a family of optimization problems as a function of parameters. It is a critical component in situations where optimal decision making is repeatedly p…