Bayesian optimization efficiently maximizes expensive black-box functions using Gaussian processes and acquisition functions, reducing sample complexity.

Eric Brochu, Vlad M. Cora, Nando de Freitas

Kernel PCA-based nonlinear control system reduction in RKHS, capturing essential dynamics via data-driven Gramian diagonalization.

Jake Bouvrie, Boumediene Hamzi

By analyzing the zero-fluctuation limit, the paper links entropy minimization to Monge-Kantorovich optimal transport via Gamma-convergence and large deviations.

Christian Léonard

Tropp improves analysis of subsampled randomized Hadamard transform, achieving optimal constants for embedding dimension bounds.

Joel A. Tropp

MESA is an open-source 1D stellar evolution code with modular microphysics, adaptive mesh, and parallelism, enabling detailed simulations from low-mass to massive stars.

Bill Paxton, Lars Bildsten, Aaron Dotter et al.

Proposed a dynamic recombination algorithm for high-order cubature on Wiener space, reducing particle explosion.

C. Litterer, T. Lyons

Generalizes Feynman tree theorem to L-loop amplitudes via on-shell phase space integrals, exploiting causality and response functions.

Simon Caron-Huot

Interpolation of scattered data on embedded submanifolds using restricted positive definite kernels with Sobolev error estimates.

Edward Fuselier, Grady Wright

Distributed dual averaging algorithm's convergence rate scales inversely with network spectral gap, enabling efficient large-scale optimization.

John Duchi, Alekh Agarwal, Martin Wainwright

Homophily and contagion are generically confounded in observational social network studies, requiring strong assumptions to distinguish.

Cosma Rohilla Shalizi, Andrew C. Thomas

Optimization under unknown constraints using Gaussian processes and Bayesian learning, applied to healthcare policy.

Robert B. Gramacy, Herbert K. H. Lee

Randomized truthful mechanism via single call to monotone allocation rule.

Moshe Babaioff, Robert D. Kleinberg, Aleksandrs Slivkins

Introduces LinUCB, a linear contextual Bandit algorithm, achieving 12.5% click rate lift on Yahoo! dataset, suitable for large-scale personalized news recommendation.

Lihong Li, Wei Chu, John Langford et al.

Constructs adaptive confidence bands in density estimation using wavelet/kernel methods, leveraging extreme value theory to ensure honest coverage over a 'generic' subset.

Evarist Giné, Richard Nickl

Exp4.P algorithm achieves supervised learning-like guarantees in contextual bandit problems, significantly reducing regret.

Alina Beygelzimer, John Langford, Lihong Li et al.

This paper links test martingales with Bayes factors and p-values, introducing functions to limit evidence exaggeration, enabling systematic conversion between them.

Glenn Shafer, Alexander Shen, Nikolai Vereshchagin et al.

Proposes a universal framework using local explanation vectors to analyze classification decisions for any classifier.

David Baehrens, Timon Schroeter, Stefan Harmeling et al.

Randomized algorithms for low-rank matrix approximation enable fast, robust processing of massive data sets, outperforming classical methods in speed and scalability.

Nathan Halko, Per-Gunnar Martinsson, Joel A. Tropp

Using CoRoT data, identified 358 candidate B pulsators, revealing a new class of low-amplitude B-type pulsators.

P. Degroote, C. Aerts, M. Ollivier et al.

Proposed quantum algorithm prepares thermal Gibbs state with time upper bound D^α, α linked to free energy density.

David Poulin, Pawel Wocjan