ACID employs transformer neural networks for conditional independence testing, achieving high accuracy and efficiency across diverse datasets.

Bao Duong, Nu Hoang, Thin Nguyen

The paper introduces a nested kernel quadrature estimator to reduce sample requirements and improve convergence speed.

Zonghao Chen, Masha Naslidnyk, François-Xavier Briol

Weighted quantization using MMD via gradient flows shows significant MMD reduction in experiments.

Ayoub Belhadji, Daniel Sharp, Youssef Marzouk

CARROT predicts model cost and accuracy, achieving minimax optimal routing; outperforms baselines on SPROUT dataset.

Seamus Somerstep, Felipe Maia Polo, Allysson Flavio Melo de Oliveira et al.

Proposes an efficient Orlicz-Sobolev transport method for unbalanced measures on graphs, using binary search for fast computation.

Tam Le, Truyen Nguyen, Hideitsu Hino et al.

This review summarizes self-supervised learning methods for time series anomaly detection, introduces a taxonomy, and discusses future challenges.

Aitor Sánchez-Ferrera, Borja Calvo, Jose A. Lozano

Introduced MCNI method for uncertainty quantification in neural networks, outperforming baseline models.

Xueqiong Yuan, Jipeng Li, Ercan Engin Kuruoglu

Proposes Deep Feature IV (DFIV) achieving minimax optimal rates in nonparametric IV regression, adaptive to complex functions.

Juno Kim, Dimitri Meunier, Arthur Gretton et al.

PEMC integrates ML predictors with Monte Carlo, reducing variance by 30-55% while maintaining unbiasedness and efficiency.

Fengpei Li, Haoxian Chen, Jiahe Lin et al.

DNCIT combines learned image embeddings with nonparametric CITs and validates MRI–behavior null findings in UK Biobank data.

Marco Simnacher, Xiangnan Xu, Hani Park et al.

LRDS leverages prior mode locations to improve multi-modal distribution sampling efficiency.

Maxence Noble, Louis Grenioux, Marylou Gabrié et al.

A Gaussian process-based approach for causal intervention function uncertainty quantification, improving posterior coverage and calibration.

Hugh Dance, Peter Orbanz, Arthur Gretton

FSBM leverages limited pre-aligned pairs to improve distribution matching efficiency and generalization in semi-supervised Schrödinger bridge framework.

Panagiotis Theodoropoulos, Nikolaos Komianos, Vincent Pacelli et al.

ACSSM combines multi-marginal Doob transform and stochastic optimal control for irregular time series modeling.

Byoungwoo Park, Hyungi Lee, Juho Lee

Proposes Schrödinger bridge-based deep generative model for efficient high-quality conditional sampling, outperforming existing diffusion methods.

Hanwen Huang

Proposes 'Sinkhorn bridge' for efficient Schrödinger bridge estimation via static entropic OT potentials, avoiding iterative diffusion simulations.

Aram-Alexandre Pooladian, Jonathan Niles-Weed

SharpBalance preserves ensemble diversity while reducing sharpness, reaching 76.12% on CIFAR-10 versus 75.45% for SAM.

Haiquan Lu, Xiaotian Liu, Yefan Zhou et al.

Proposes Entropic Optimal Transport eigenmaps for nonlinear alignment and joint embedding of high-dimensional datasets, with theoretical guarantees.

Boris Landa, Yuval Kluger, Rong Ma

This paper analyzes spectral regularization algorithms for vector-valued learning, confirming the saturation effect of ridge regression and establishing optimal upper bounds.

Dimitri Meunier, Zikai Shen, Mattes Mollenhauer et al.

Introduces an energy-based kernel framework for spectral learning of unbounded diffusion generators, providing dimension-free bounds and avoiding spurious eigenvalues.

Vladimir R. Kostic, Karim Lounici, Helene Halconruy et al.