Proposes a robust, fast recursive algorithm for separable NMF with theoretical guarantees under noise, outperforming existing methods in accuracy and efficiency.

Nicolas Gillis, Stephen A. Vavasis

The paper proposes a Bayesian optimization method using Gaussian processes, significantly enhancing hyperparameter tuning efficiency for ML algorithms.

Jasper Snoek, Hugo Larochelle, Ryan P. Adams

Entropy Search algorithm optimizes global search by maximizing information gain, enhancing efficiency.

Philipp Hennig, Christian J. Schuler

Proposes a Bayesian simulation-based optimal experimental design framework combining Polynomial Chaos and stochastic approximation, improving efficiency in nonlinear systems.

Xun Huan, Youssef M. Marzouk

The paper proposes minimax policies for combinatorial prediction games using Bregman projections and potential-based gradient descent to address worst-case minimax regret.

Jean-Yves Audibert, Sebastien Bubeck, Gabor Lugosi

Convex NMF model with l_{1,∞} regularization selects data subset as dictionary, enabling physically meaningful dimensionality reduction.

Ernie Esser, Michael Möller, Stanley Osher 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.

Proposes nonparanormal model using smooth transformations for high-dimensional sparse graph estimation, outperforming Gaussian models on non-Gaussian data.

Han Liu, John Lafferty, Larry Wasserman