Donlapark Ponnoprat

Publications

Preprints

1. Minimax Rates of Estimation for Optimal Transport Map between Infinite-Dimensional Spaces
   [abstract] [arxiv]
   D. Ponnoprat, M. Imaizumi
   arXiv, 2025
   We investigate the estimation of an optimal transport map between probability measures on an infinite-dimensional space and reveal its minimax optimal rate. Optimal transport theory defines distances within a space of probability measures, utilizing an optimal transport map as its key component. Estimating the optimal transport map from samples finds several applications, such as simulating dynamics between probability measures and functional data analysis. However, some transport maps on infinite-dimensional spaces require exponential-order data for estimation, which undermines their applicability. In this paper, we investigate the estimation of an optimal transport map between infinite-dimensional spaces, focusing on optimal transport maps characterized by the notion of $\gamma$-smoothness. Consequently, we show that the order of the minimax risk is polynomial rate in the sample size even in the infinite-dimensional setup. We also develop an estimator whose estimation error matches the minimax optimal rate. With these results, we obtain a class of reasonably estimable optimal transport maps on infinite-dimensional spaces and a method for their estimation. Our experiments validate the theory and practical utility of our approach with application to functional data analysis.

Conference papers

1. Minimax Optimal Estimation of Transport-Growth Pairs in Unbalanced Optimal Transport
   [abstract] [arxiv]
   D. Ponnoprat, N. Isobe, M. Imaizumi
   NeurIPS, 2026
   Unbalanced optimal transport (UOT) extends classical optimal transport to measures with different total masses, but statistical guarantees for Monge-type estimation remain limited. We study unbalanced transport with quadratic cost and Kullback-Leibler marginal penalties and argue that the natural population target is not a map alone, but a transport-growth pair. Consequently, we develop two estimators for the transport-growth pairs under several setups: an optimal transport plan-based estimator for a general case, and a kernel-based estimator for a case with smooth densities. We also show that an error of the estimator achieves the minimax optimal rate by deriving a matching lower bound of the minimax risk. Our main technical contribution is a value-based stability reduction that converts perturbations of the UOT objective into transport and growth risks through a UOT gap condition. These results provide a statistical foundation for Monge-type estimation in unbalanced optimal transport.


2. Conditional Counterfactual Mean Embeddings: Doubly Robust Estimation and Learning Rates
   [abstract] [arxiv]
   T. Anancharoenkij, D. Ponnoprat
   NeurIPS, 2026
   A complete understanding of heterogeneous treatment effects involves characterizing the full conditional distribution of potential outcomes. To this end, we propose the Conditional Counterfactual Mean Embeddings (CCME), a framework that embeds conditional distributions of counterfactual outcomes into a reproducing kernel Hilbert space (RKHS). Under this framework, we develop a two-stage meta-estimator for CCME that accommodates any RKHS-valued regression in each stage. Based on this meta-estimator, we develop three practical CCME estimators: (1) Ridge Regression estimator, (2) Deep Feature estimator that parameterizes the feature map by a neural network, and (3) Neural-Kernel estimator that performs RKHS-valued regression, with the coefficients parameterized by a neural network. We provide finite-sample convergence rates for all estimators, establishing that they possess the double robustness property. Our experiments demonstrate that our estimators accurately recover distributional features including multimodal structure of conditional counterfactual distributions.


3. Counting Graphlets of Size k under Local Differential Privacy
   [abstract] [paper]
   V. Suppakitpaisarn, D. Ponnoprat, N. Hirankarn, Q. Hillebrand
   AISTATS, 2025
   The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing work focuses on small graphlets like triangles or $k$-stars. In this paper, we propose a non-interactive, locally differentially private algorithm capable of counting graphlets of any size $k$. When $n$ is the number of nodes in the input graph, we show that the expected $\ell^2$ error of our algorithm is $O(n^{k−1})$. Additionally, we prove that there exists a class of input graphs and graphlets of size $k$ for which any non-interactive counting algorithm incurs an expected $\ell^2$ error of $\Omega(n^{k−1})$, demonstrating the optimality of our result. Furthermore, we establish that for certain input graphs and graphlets, any locally differentially private algorithm must have an expected $\ell^2$ error of $\Omega(n^{k-1.5})$. Our experimental results show that our algorithm is more accurate than the classical randomized response method.


4. Detecting Anomalous LAN Activities under Differential Privacy
   [abstract] [arxiv]
   N. Rattanavipanon, D. Ponnoprat, H. Ochiai, K. Tantayakul, T. Angchuan, S. Kamolphiwong
   NDSS, 2022
   Anomaly detection has emerged as a popular technique for detecting malicious activities in local area networks (LANs). Various aspects of LAN anomaly detection have been widely studied. Nonetheless, the privacy concern about individual users or their relationship in LAN has not been thoroughly explored in the prior work. In some realistic cases, the anomaly detection analysis needs to be carried out by an external party, located outside the LAN. Thus, it is important for the LAN admin to release LAN data to this party in a private way in order to protect privacy of LAN users; at the same time, the released data must also preserve the utility of being able to detect anomalies. This paper investigates the possibility of privately releasing ARP data that can later be used to identify anomalies in LAN. We present four approaches, namely naïve, histogram-based, naïve-$\delta$ and histogram-based-$\delta$, and show that they satisfy different levels of differential privacy—a rigorous and provable notion for quantifying privacy loss in a system. Our real-world experimental results confirm practical feasibility of our approaches. With a proper privacy budget, all of our approaches preserve more than 75% utility of detecting anomalies in the released data.

Journal papers

1. coverforest: Conformal Predictions with Random Forest in Python
   [abstract] [paper] [arxiv] [code]
   P. Meehinkong, D. Ponnoprat
   arXiv, 2025
   Conformal prediction provides a framework for uncertainty quantification, specifically in the forms of prediction intervals and sets with distribution-free guaranteed coverage. While recent cross-conformal techniques such as CV+ and Jackknife+-after-bootstrap achieve better data efficiency than traditional split conformal methods, they incur substantial computational costs due to required pairwise comparisons between training and test samples' out-of-bag scores. Observing that these methods naturally extend from ensemble models, particularly random forests, we leverage existing optimized random forest implementations to enable efficient cross-conformal predictions.

   We present coverforest, a Python package that implements efficient conformal prediction methods specifically optimized for random forests. coverforest supports both regression and classification tasks through various conformal prediction methods, including split conformal, CV+, Jackknife+-after-bootstrap, and adaptive prediction sets. Our package leverages parallel computing and Cython optimizations to speed up out-of-bag calculations. Our experiments demonstrate that coverforest's predictions achieve the desired level of coverage. In addition, its training and prediction times can be faster than an existing implementation by 2--9 times. The source code for coverforest is hosted on GitHub at https://github.com/donlapark/coverforest.



2. Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack
   [abstract] [arxiv] [paper] [code]
   C. Lumbut, D. Ponnoprat
   Journal of Information Security and Applications, 2025
   This study investigates privacy leakage in dimensionality reduction methods through a novel machine learning-based reconstruction attack. Employing an informed adversary threat model, we develop a neural network capable of reconstructing high-dimensional data from low-dimensional embeddings.

   We evaluate six popular dimensionality reduction techniques: principal component analysis (PCA), sparse random projection (SRP), multidimensional scaling (MDS), Isomap, t-distributed stochastic neighbor embedding ($t$-SNE), and uniform manifold approximation and projection (UMAP). Using both MNIST and NIH Chest X-ray datasets, we perform a qualitative analysis to identify key factors affecting reconstruction quality. Furthermore, we assess the effectiveness of an additive noise mechanism in mitigating these reconstruction attacks. Our experimental results on both datasets reveal that the attack is effective against deterministic methods (PCA and Isomap). but ineffective against methods that employ random initialization (SRP, MDS, $t$-SNE and UMAP). The experimental results also show that, for PCA and Isomap, our reconstruction network produces higher quality outputs compared to a previously proposed network.

   We also study the effect of additive noise mechanism to prevent the reconstruction attack. Our experiment shows that, when adding the images with large noises before performing PCA or Isomap, the attack produced severely distorted reconstructions. In contrast, for the other four methods, the reconstructions still show some recognizable features, though they bear little resemblance to the original images.



3. Uniform Confidence Bands for Optimal Transport Map on One-Dimensional Data
   [abstract] [paper]
   D. Ponnoprat, R. Okano, M. Imaizumi
   Electronic Journal of Statistics, 2024
   We develop a statistical inference method for an optimal transport map between distributions on real numbers with uniform confidence bands. The concept of optimal transport (OT) is used to measure distances between distributions, and OT maps are used to construct the distance. OT has been applied in many fields in recent years, and its statistical properties have attracted much interest. In particular, since the OT map is a function, a uniform norm-based statistical inference is significant for visualization and interpretation. In this study, we derive a limit distribution of a uniform norm of an estimation error for the OT map, and then develop a uniform confidence band based on it. In addition to our limit theorem, we develop a bootstrap method with kernel smoothing, then also derive its validation and guarantee on an asymptotic coverage probability of the confidence band. Our proof is based on the functional delta method and the representation of OT maps on the reals.


4. Universal Consistency of Wasserstein $k$-NN Classifier: a Negative and Some Positive Results
   [abstract] [paper]
   D. Ponnoprat
   Information and Inference, 2023
   We study the $k$-nearest neighbour classifier ($k$⁠-NN) of probability measures under the Wasserstein distance. We show that the $k$-NN classifier is not universally consistent on the space of measures supported in $(0,1)$⁠. As any Euclidean ball contains a copy of $(0,1)$⁠, one should not expect to obtain universal consistency without some restriction on the base metric space, or the Wasserstein space itself. To this end, via the notion of $\sigma$-finite metric dimension, we show that the $k$-NN classifier is universally consistent on spaces of discrete measures (and more generally, $\sigma$-finite uniformly discrete measures) with rational mass. In addition, by studying the geodesic structures of the Wasserstein spaces for $p=1$ and $p=2$⁠, we show that the $k$-NN classifier is universally consistent on spaces of measures supported on a finite set, the space of Gaussian measures and spaces of measures with finite wavelet series densities.


5. Dirichlet Mechanism for Differentially Private KL Divergence Minimization
   [abstract] [paper] [code]
   D. Ponnoprat
   TMLR, 2023
   Given an empirical distribution $f(x)$ of sensitive data $x$, we consider the task of minimizing $F(y) = D_{\operatorname{KL}}(f(x)\Vert y)$ over a probability simplex, while protecting the privacy of $x$. We observe that, if we take the exponential mechanism and use the KL divergence as the loss function, then the resulting algorithm is the Dirichlet mechanism that outputs a single draw from a Dirichlet distribution. Motivated by this, we propose a Rényi differentially private (RDP) algorithm that employs the Dirichlet mechanism to solve the KL divergence minimization task. In addition, given $f(x)$ as above and $\hat{y}$ an output of the Dirichlet mechanism, we prove a probability tail bound on $D_{\operatorname{KL}(f(x)\Vert \hat{y})$, which is then used to derive a lower bound for the sample complexity of our RDP algorithm. Experiments on real-world datasets demonstrate advantages of our algorithm over Gaussian and Laplace mechanisms in supervised classification and maximum likelihood estimation.


6. Short-Term Daily Precipitation Forecasting With Seasonally-Integrated Autoencoder
   [abstract] [arxiv] [code]
   D. Ponnoprat
   Applied Soft Computing, 2021
   Short-term precipitation forecasting is essential for planning of human activities in multiple scales, ranging from individuals’ planning, urban management to flood prevention. Yet the short-term atmospheric dynamics are highly nonlinear that it cannot be easily captured with classical time series models. On the other hand, deep learning models are good at learning nonlinear interactions, but they are not designed to deal with the seasonality in time series. In this study, we aim to develop a forecasting model that can both handle the nonlinearities and detect the seasonality hidden within the daily precipitation data. To this end, we propose a seasonally-integrated autoencoder (SSAE) consisting of two long short-term memory (LSTM) autoencoders: one for learning short-term dynamics, and the other for learning the seasonality in the time series. Our experimental results show that not only does the SSAE outperform various time series models regardless of the climate type, but it also has low output variance compared to other deep learning models. The results also show that the seasonal component of the SSAE helped improve the correlation between the forecast and the actual values from 4% at horizon 1 to 37% at horizon 3.


7. Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Multi-Phase CT Scans
   [abstract] [paper] [pdf]
   D. Ponnoprat, P. Inkeaw, J. Chaijaruwanich, P. Traisathit, P. Sripan, N. Inmutto, W. Na Chiangmai, D. Pongnikorn, I. Chitapanarux
   Medical & Biological Engineering & Computing, 2020
   Liver and bile duct cancers are leading causes of worldwide cancer death. The most common ones are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Influencing factors and prognosis of HCC and ICC are different. Precise classification of these two liver cancers is essential for treatment and prevention plans. The aim of this study is to develop a machine-based method that differentiates between the two types of liver cancers from multi-phase abdominal computerized tomography (CT) scans. The proposed method consists of two major steps. In the first step, the liver is segmented from the original images using a convolutional neural network model, together with task-specific pre-processing and post-processing techniques. In the second step, by looking at the intensity histograms of the segmented images, we extract features from regions that are discriminating between HCC and ICC, and use them as an input for classification using support vector machine model. By testing on a dataset of labeled multi-phase CT scans provided by Maharaj Nakorn Chiang Mai Hospital, Thailand, we have obtained 88% in classification accuracy. Our proposed method has a great potential in helping radiologists diagnosing liver cancer.


8. Small Data Well-Posedness for Derivative Nonlinear Schrödinger Equations
   [abstract] [paper]
   D. Ponnoprat
   Journal of Differential Equations, 2018
   We study the local and global solutions of the generalized derivative nonlinear Schrödinger equation $i\partial_t u+\Delta u = P(u,\overline{u},\partial_x u,\partial_x \overline{u})$, where each monomial in $P$ is of degree $3$ or higher, in low-regularity Sobolev spaces without using a gauge transformation. Instead, we use a solution decomposition technique introduced in Bejenaru, Ionescu, Kenig & Tataru (2011) during the perturbative argument to deal with the loss on derivative in nonlinearity. It turns out that when each term in $P$ contains only one derivative, the equation is locally well-posed in $H^{\frac{1}{2}}$, otherwise we have a local well-posedness in $H^{\frac{3}{2}}$. If each monomial in $P$ is of degree $5$ or higher, the solution can be extended globally. By restricting to equations to the form $i\partial_t u+\Delta u = \partial_x P(u,\overline{u})$ with the quintic nonlinearity, we were able to obtain the global well-posedness in the critical Sobolev space.