26 citations · 39 across the 18 of their papers we have counts for
18 papers
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…
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…
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…
A Classical View on Benign Overfitting: The Role of Sample Size
Junhyung Park, Patrick Bloebaum, Shiva Prasad Kasiviswanathan
Benign overfitting is a phenomenon in machine learning where a model perfectly fits (interpolates) the training data, including noisy examples, yet still generalizes well to unseen…
UKTA: Unified Korean Text Analyzer
Seokho Ahn, Junhyung Park, Ganghee Go +5
Evaluating writing quality is complex and time-consuming often delaying feedback to learners. While automated writing evaluation tools are effective for English, Korean automated w…
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…
Benign Overfitting for Regression with Trained Two-Layer ReLU Networks
Junhyung Park, Patrick Bloebaum, Shiva Prasad Kasiviswanathan
We study the least-square regression problem with a two-layer fully-connected neural network, with ReLU activation function, trained by gradient flow. Our first result is a general…
Products, Abstractions and Inclusions of Causal Spaces
Simon Buchholz, Junhyung Park, Bernhard Schölkopf
Causal spaces have recently been introduced as a measure-theoretic framework to encode the notion of causality. While it has some advantages over established frameworks, such as st…
Particle swarm optimization with Applications to Maximum Likelihood Estimation and Penalized Negative Binomial Regression
Sisi Shao, Junhyung Park, Weng Kee Wong
General purpose optimization routines such as nlminb, optim (R) or nlmixed (SAS) are frequently used to estimate model parameters in nonstandard distributions. This paper presents…
Kernel-based independence tests for causal structure learning on functional data
Felix Laumann, Julius von Kügelgen, Junhyung Park +2
Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics…
A continuous Structural Intervention Distance to compare Causal Graphs
Mihir Dhanakshirur, Felix Laumann, Junhyung Park +1
Understanding and adequately assessing the difference between a true and a learnt causal graphs is crucial for causal inference under interventions. As an extension to the graph-ba…
A Measure-Theoretic Axiomatisation of Causality
Junhyung Park, Simon Buchholz, Bernhard Schölkopf +1
Causality is a central concept in a wide range of research areas, yet there is still no universally agreed axiomatisation of causality. We view causality both as an extension of pr…
Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes
Junhyung Park, Krikamol Muandet
This paper provides some first steps in developing empirical process theory for functions taking values in a vector space. Our main results provide bounds on the entropy of classes…
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
Junhyung Park, Uri Shalit, Bernhard Schölkopf +1
We propose to analyse the conditional distributional treatment effect (CoDiTE), which, in contrast to the more common conditional average treatment effect (CATE), is designed to en…
Regularised Least-Squares Regression with Infinite-Dimensional Output Space
Junhyunng Park, Krikamol Muandet
This short technical report presents some learning theory results on vector-valued reproducing kernel Hilbert space (RKHS) regression, where the input space is allowed to be non-co…
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings
Junhyung Park, Krikamol Muandet
We present an operator-free, measure-theoretic approach to the conditional mean embedding (CME) as a random variable taking values in a reproducing kernel Hilbert space. While the…