arXiv subjects

Suman Cha

Publications and source records attributed to Suman Cha.

4 recordsLinked to original sources

arXiv2026

FUSE: Feature-Wise Unified Specialization with Cross-Column Exchange for Mixed-Type Tabular Flow Matching

Generating mixed-type tabular data requires jointly modeling diverse feature distributions and their complex cross-column dependencies. Variational flow matching handles distinct endpoints via factorized distributions, yet leaves feature-specific processing and cross-column interactions implicit within a shared backbone. We introduce Feature-wise Unified Specialization with cross-column Exchange (FUSE) to explicitly separate these roles. FUSE applies separate adaptive mixture modules to numerical and categorical features, allowing each feature to combine shared specialized subnetworks, while joint attention preserves information exchange across all columns. We also characterize the excess population risk from restricted conditioning contexts and bound the continuous Wasserstein generation error by endpoint-prediction risk. Comprehensive experiments on eight tabular datasets demonstrate that FUSE achieves strong and consistent performance across distributional fidelity and downstream utility metrics.

cs.LG↗

arXiv2026

More Permutations Do Not Always Increase Power: Non-monotonicity in Monte Carlo Permutation Tests

Monte Carlo permutation tests are a cornerstone of valid, model-free statistical inference. A widely held practical intuition is that increasing the number of sampled permutations improves test performance, in particular that statistical power tends to increase with the Monte Carlo budget. In this paper, we show that these intuitions are false in general. Leveraging the saw-toothed structure of power arising from distributional discreteness, we provide a simple structural explanation for why power can decrease as the number of sampled permutations increases, and we prove that such decreases occur infinitely often as the Monte Carlo budget grows.

stat.CO↗

arXiv2025

Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification

Class imbalance in supervised classification often degrades model performance by biasing predictions toward the majority class, particularly in critical applications such as medical diagnosis and fraud detection. Traditional oversampling techniques, including SMOTE and its variants, generate synthetic minority samples via local interpolation but fail to capture global data distributions in high-dimensional spaces. Deep generative models based on GANs offer richer distribution modeling yet suffer from training instability and mode collapse under severe imbalance. To overcome these limitations, we introduce an oversampling framework that learns a parametric transformation to map majority samples into the minority distribution. Our approach minimizes the maximum mean discrepancy (MMD) between transformed and true minority samples for global alignment, and incorporates a triplet loss regularizer to enforce boundary awareness by guiding synthesized samples toward challenging borderline regions. We evaluate our method on 29 synthetic and real-world datasets, demonstrating consistent improvements over classical and generative baselines in AUROC, G-mean, F1-score, and MCC. These results confirm the robustness, computational efficiency, and practical utility of the proposed framework for imbalanced classification tasks.

stat.ML↗

arXiv2024

General Frameworks for Conditional Two-Sample Testing

We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accounting for confounding factors. This problem commonly arises in various applications, such as domain adaptation and algorithmic fairness, where comparing two groups is essential while controlling for confounding variables. We begin by establishing a hardness result for conditional two-sample testing, demonstrating that no valid test can have significant power against any single alternative without proper assumptions. We then introduce two general frameworks that implicitly or explicitly target specific classes of distributions for their validity and power. Our first framework allows us to convert any conditional independence test into a conditional two-sample test in a black-box manner, while preserving the asymptotic properties of the original conditional independence test. The second framework transforms the problem into comparing marginal distributions with estimated density ratios, which allows us to leverage existing methods for marginal two-sample testing. We demonstrate this idea in a concrete manner with classification and kernel-based methods. Finally, simulation studies are conducted to illustrate the proposed frameworks in finite-sample scenarios.

stat.ML↗