9 papers
A fine-grained look at causal effects in causal spaces
Junhyung Park, Yuqing Zhou
The notion of causal effect is fundamental across many scientific disciplines. Traditionally, quantitative researchers have studied causal effects at the level of variables; for ex…
Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest
Jeffrey Näf, Junhyung Park, Herbert Susmann
The conditional average treatment effect (CATE) is a commonly targeted statistical parameter for measuring the effect of a treatment conditional on covariates. However, the CATE wi…
Counterfactual Spaces
Junhyung Park, Fanny Yang, Thomas Icard
We mathematically axiomatise the stochastics of counterfactuals, by introducing two related frameworks, called counterfactual probability spaces and counterfactual causal spaces, w…
Ensemble-Based Global Search Framework for the Design Optimization of Fabrication-Constrained Freeform Devices
Seokhwan Min, Junhyung Park, Jonghwa Shin
Although freeform devices with complex internal structures promise drastic increases in performance, the discreteness of the set of available materials presents challenges for grad…
Stochastic Deep Graph Clustering for Practical Group Formation
Junhyung Park, Hyungjin Kim, Seokho Ahn +1
While prior work on group recommender systems (GRSs) has primarily focused on improving recommendation accuracy, most approaches assume static or predefined groups, making them uns…
On the sample complexity of semi-supervised multi-objective learning
Tobias Wegel, Geelon So, Junhyung Park +1
In multi-objective learning (MOL), several possibly competing prediction tasks must be solved jointly by a single model. Achieving good trade-offs may require a model class $\mathc…