Publications

Privacy

1. 

   Minimax optimal two-sample testing under local differential privacy

   Jongmin Mun, Seungwoo Kwak, and Ilmun Kim

   Journal of Machine Learning Research, 2025

   Summary Published Version Slides Python Package

   Federated learning, where user data remains on local devices and only weight updates are transmitted, has become the industry standard for training neural networks on sensitive data. Federated analytics builds on this infrastructure, enabling data science insights without transmitting raw data. However, recent studies show that hijacking weight updates or data summaries can allow recovery of the original raw data. To address this privacy risk, we propose a private A/B testing method that transmits noisy data summaries within the federated learning framework. We begin with multinomial data, introducing private permutation tests using privacy mechanisms like the Laplace mechanism, discrete Laplace mechanism, and Google’s RAPPOR mechanism. We extend our approach to continuous data using binning and analyze uniform separation rates under local differential privacy (LDP). Our tests rigorously control Type I error, satisfy LDP constraints, and achieve minimax separation rates, highlighting the inherent privacy-utility trade-offs in private testing.

Imbalance

1. 

   Weighted support vector machine for extremely imbalanced data

   Jongmin Mun, Sungwan Bang, and Jaeoh Kim

   Computational Statistics & Data Analysis, Mar 2025

   Summary Published Version

   Data augmentation and loss function adjustments are two common techniques for imbalanced classification. In cases of extreme class imbalance, it is often standard practice to combine these two approaches. However, determining the optimal oversampling ratio and the degree of asymmetry in the loss function typically relies on heuristics. We further consider the scenario where the minority class consists of subgroups and propose a straightforward method for combining data augmentation and loss function adjustments. This method serves as a sample-based approximation of the population-level asymptotically Bayes optimal oracle procedure.

2. 

   Sex differences in autism spectrum disorder using class imbalance adjusted functional connectivity

   Jong Young Namgung, Jongmin Mun, Yeongjun Park, Jaeoh Kim, and Bo-yong Park

   NeuroImage, Dec 2024

   Preliminary version presented at the 2024 International Conference of the IEEE Engineering in Medicine and Biology Society

   Summary Published Version

   Functional connectivity refers to the correlations between neural signal time series across different brain regions, in contrast to the physical (structural) connections between them. We are interested in understanding how autism causally affects functional connectivity, and whether this effect differs between sexes. From a statistical perspective, this problem corresponds to estimating heterogeneous treatment effects (HTEs). Because autism spectrum disorder (ASD) is diagnosed more frequently in males than in females, the number of female participants in the autism group is substantially smaller. As a result, female samples contribute less to the HTE estimation algorithm, leading to potentially unstable estimates for females. To address this subgroup imbalance, we employ oversampling using a cluster-aware generative model. The augmented data increase the representation of female samples in the HTE estimation process, thereby improving the stability and reliability of HTE estimates for females. Our results reveal that several brain regions exhibit significant HTEs that were not detectable prior to oversampling. Furthermore, we demonstrate that the identified HTEs are not random noise but have clear biological meaning: they are strongly associated with higher-order cognitive control functions, rather than lower-level sensory processing, and are linked to autism-related genes.

3. 

   Prediction of forest fire risk for artillery military training using weighted support vector machine for imbalanced data

   Ji Hyun Nam, Jongmin Mun, Seongil Jo, and Jaeoh Kim

   Journal of Classification, Mar 2024

   Summary Published Version

   Artillery training inherently poses wildfire risks. Predictive modeling of these wildfires faces two key challenges: the scarcity of wildfire cases and the limited granularity of meteorological data during training. We address the first challenge by augmenting the data using a Gaussian mixture generative model and adjusting the loss function of a support vector machine. To tackle the second challenge, we integrate the Republic of Korea Army (ROKA) dataset with the Korea Meteorological Administration database. Our resulting model achieves a 99% improvement in balanced classification metrics compared to previous models.

Hi-dim

1. Hybrid Partial Least Squares Regression with Multiple Functional and Scalar Predictors

   Jongmin Mun, and Jeong Hoon Jang

   arxiv preprint, Jan 2026

   Summary Preprint

   Motivated by renal imaging studies that combine renogram curves with pharmacokinetic and demographic covariates, we propose Hybrid partial least squares (Hybrid PLS) for simultaneous supervised dimension reduction and regression in the presence of cross-modality correlations. The proposed approach embeds multiple functional and scalar predictors into a unified hybrid Hilbert space and rigorously extends the nonlinear iterative PLS (NIPALS) algorithm. This theoretical development is complemented by a sample-level algorithm that incorporates roughness penalties to control smoothness. By exploiting the rank-one structure of the resulting optimization problem, the algorithm admits a computationally efficient closed-form solution that requires solving only linear systems at each iteration. We establish fundamental geometric properties of the proposed framework, including orthogonality of the latent scores and PLS directions. Extensive numerical studies on synthetic data, together with an application to a renal imaging study, validate these theoretical results and demonstrate the method’s ability to recover predictive structure under intermodal multicollinearity, yielding parsimonious low-dimensional representations.

2. High-Dimensional Sparse Clustering via Iterative Semidefinite Programming Relaxed K-Means

   Jongmin Mun, Paromita Dubey, and Yingying Fan

   arxiv preprint, May 2025

   Summary Slides Preprint

   We propose an iterative algorithm for clustering high-dimensional data, where the true signal lies in a much lower-dimensional space. Our method alternates between feature selection and clustering, without requiring precise estimation of sparse model parameters. Feature selection is performed by thresholding a rough estimate of the discriminative direction, while clustering is carried out via a semidefinite programming (SDP) relaxation of K-means. In the isotropic case, the algorithm is motivated by the minimax separation bound for exact recovery of cluster labels using varying sparse subsets of features. This bound highlights the critical role of variable selection in achieving exact recovery. We further extend the algorithm to settings with unknown sparse precision matrices, avoiding full model parameter estimation by computing only the minimally required quantities. Across a range of simulation settings, we find that the proposed iterative approach outperforms several state-of-the-art methods, especially in higher dimensions.

Brain

1. 

   In-vivo integration of soft neural probes through high-resolution printing of liquid electronics on the cranium

   Young-Geun Park, Yong Won Kwon, Chin Su Koh, Enji Kim, Dong Ha Lee, Sumin Kim, Jongmin Mun, Yeon-Mi Hong, Sanghoon Lee, Ju-Young Kim, Jae-Hyun Lee, Hyun Ho Jung, Jinwoo Cheon, Jin Woo Chang, and Jang-Ung Park

   Nature Communications, Feb 2024

   Summary Published Version

   To enable high-quality neural recordings in freely moving animals, our materials science team developed a liquid metal–based neural probe that can be directly printed onto the brain, along with elastic electronic circuits. To validate the fidelity of the signals recorded by this neural probe, we statistically assessed the long-term stability of signal clustering and examined the correlations between micro-scale neuronal activity and macro-level brain dynamics during mouse behavioral experiments involving visual and motor stimuli.