Introduced spectral bandit algorithms for smooth graph functions, achieving linear and sublinear scaling in effective dimension.

Michal Valko, Rémi Munos, Branislav Kveton et al.

Adaptive kernel selection enhances stability and accuracy of kernelized diffusion maps.

Othmane Aboussaad, Adam Miraoui, Boumediene Hamzi et al.

Bias-aware simulation-based inference framework addresses selection bias, enhancing estimation accuracy.

Jonas Arruda, Sophie Chervet, Paula Staudt et al.

Kometo algorithm achieves fast learning rates in multi-fidelity optimization without known smoothness or fidelity assumptions.

Come Fiegel, Victor Gabillon, Michal Valko

Structural interpretability in SVMs using truncated orthogonal polynomial kernels reveals model complexity.

Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. Huertas

Amortized Optimal Transport using sliced potentials enhances OT plan prediction efficiency across multiple measure pairs.

Minh-Phuc Truong, Khai Nguyen

Proposes a semiparametric efficiency-based debiased machine learning framework for valid conformal prediction intervals under runtime confounding.

Keith Barnatchez, Kevin P. Josey, Rachel C. Nethery et al.

Fast interpretable autoregressive estimation using neural network backpropagation, achieving 12.6x speedup.

Anaísa Lucena, Ana Martins, Armando J. Pinho et al.

OmniAnomaly and PCA perform comparably on the SMD dataset, especially without point adjustment.

Bruna Alves, Ana Martins, Armando J. Pinho et al.

Study the mechanism of diffusion models learning data statistics from simple to complex using the mixed cumulant model.

Lorenzo Bardone, Claudia Merger, Sebastian Goldt

VecMol generates 3D molecules using vector-field representations, avoiding explicit graph generation and enhancing geometry-chemistry coherence.

Yuchen Hua, Xingang Peng, Jianzhu Ma et al.

The paper refines and extends the batched kernelized bandits problem, optimizing batch numbers and regret bounds.

Chenkai Ma, Keqin Chen, Jonathan Scarlett

Efficient approximation of analytic and L^p functions using height-augmented ReLU networks, significantly improving approximation rates.

ZeYu Li, FengLei Fan, TieYong Zeng

Introduced smooth-rank algorithm to optimize bipartite ranking with continuous distributions, enhancing ROC curve precision.

James Cheshire, Stephan Clémençon

Proposes a nonparametric distribution calibration method based on conditional kernel mean embeddings, significantly improving calibration accuracy.

Ádám Jung, Domokos M. Kelen, András A. Benczúr

Analyzed convergence rates of PCA for multiple probability measures under sparse and dense sampling, revealing a transition phenomenon; optimal in dense regime.

Gachon Erell, Jérémie Bigot, Elsa Cazelles

This work proves that Transformers can achieve minimax optimal nonparametric regression rates with Θ(log n) parameters, requiring Ω(n^{2α/(2α+d)} log^3 n) pretraining sequences.

Michelle Ching, Ioana Popescu, Nico Smith et al.

Proposed a PAC-Bayesian framework optimizing anisotropic Gaussian posteriors for tighter generalization bounds.

Xinping Yi, Gaojie Jin, Xiaowei Huang et al.

This review discusses diffusion models in simulation-based inference, focusing on training, inference, and evaluation, highlighting guidance, score composition, and flow matching techniques.

Jonas Arruda, Niels Bracher, Ullrich Köthe et al.

Proposes GenSDR, leveraging generative models with conditional velocity fields for nonlinear SDR, ensuring full information recovery at both sample and population levels.

Shuntuo Xu, Zhou Yu, Jian Huang