Introduces Sinkhorn linearization and spectral proxy, unifying statistical and algorithmic theory of feature-parameterized inverse OT with a core spectral bound.

Han Dong, Jiaming Li, Yongqiang Gong et al.

Combining ridge regression with minimum description length enables low false positive neighborhood selection in high-dimensional networks.

Lourens Waldorp

This survey systematically reviews conditional independence (CI) tests in constraint-based causal discovery, analyzing assumptions, robustness, and scalability in high-dimensional and mixed data.

Pavel Averin, Theodoros Moysiadis, Ioannis Katakis

This study demonstrates that iterative erasure count is not an affine-invariant concept dimension, highlighting the dependence on measurement procedures and geometric transformations.

Tingan Jin, Shuhang Dong, Haosong Li et al.

Proposes VANEB framework, integrating heteroskedastic parameter-dependent EB for personalized federated learning, achieving superior performance on MNIST and CIFAR-10.

Jae Ho Chang, Arnab Auddy, Subhadeep Paul

UCB-HARE algorithm minimizes regret under strict fairness, matching theoretical lower bounds.

Dhruv Sarkar, Soumyadeep Dutta, Sayak Ray Chowdhury

Study finds PA replacement metrics are robust at N=1 but inflate at N>1; recommends PR metrics.

Zongye Lyu

This paper derives exponential approximation bounds for analytic functions using ReLU networks, highlighting depth's dominance over width in the (N,L) parameterization.

Yanming Lai, Defeng Sun, Yang Wang

Ribbon: Influence-function-based scalable approximation for Bayesian uncertainty, improving calibration and efficiency.

Graham Gibson, John Tipton, Kellin Rumsey et al.

This paper develops a theoretical framework for synthetic augmentation in score-based imbalanced classification, showing limited gains under well-specified models and potential improvements under model misspecification.

Zhengchi Ma, Pengfei Lyu, Anru R. Zhang

Proposes SSH-Net, a deep neural network integrated with cause-specific competing risks model, for failure time distribution prediction on GPU data, leveraging hierarchical data structures.

Jie Min, Yueyao Wang, Mengkun Chen

TimeLAVA evaluates time series data quality using Selective Wavelet-based Wasserstein discrepancy.

Wenqin Liu, Weizhi Quan, Aoqi Zuo et al.

ProtoX-AD is a prototype-based self-explainable time series anomaly detection framework that achieves comparable performance to black-box models by leveraging transformation-aware latent representations.

Aitor Sánchez-Ferrera, Elisabeth Wetzer, Kristoffer Wickstrøm et al.

This paper introduces two calibration strategies—post-hoc calibration and in-training adaptation—for Bayesian prediction under label shift, validated through synthetic experiments.

Seungjin Choi

Proposed a spectral knowledge transfer framework with two-step embedding, improving low-dimensional representations in rare disease EHRs.

Feiqing Huang, Zongqi Xia, Rong Ma et al.

Introduces Itô maps for arbitrary-step SDE sampling, enabling conditional sampling and control, enhancing diversity and efficiency.

Zhengkai Pan, Peter Potaptchik, Wenxi Yao et al.

Introduces complexity minimization in meta-learning, with theoretical proof of the data scaling law, improving few-shot adaptation as pre-training data grows.

Kazuto Fukuchi, Ryuichiro Hataya, Kota Matsui

Introduced a causal representation learning method to enhance recommendation systems' distribution generalization, achieving significant user engagement improvement on Spotify.

Yorgos Felekis, Michael O'Riordan, Oriol Corcoll et al.

Proposes a sparse activation-based neural scaling law model, revealing double descent peaks and asymmetry due to sparsity effects.

John Sous, Michael Winer

Proposes KDE-gradient conservative drifting with finite-particle convergence rates up to N^{-(2-β)/(2(d+4-β))}

Krishnakumar Balasubramanian