1.2k citations · 1.7k across the 52 of their papers we have counts for
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Active Learning for Regression by Inverse Distance Weighting
Alberto Bemporad
This paper proposes an active learning (AL) algorithm to solve regression problems based on inverse-distance weighting functions for selecting the feature vectors to query. The alg…
Training Recurrent Neural Networks by Sequential Least Squares and the Alternating Direction Method of Multipliers
Alberto Bemporad
This paper proposes a novel algorithm for training recurrent neural network models of nonlinear dynamical systems from an input/output training dataset. Arbitrary convex and twice-…
SCORE: Approximating Curvature Information under Self-Concordant Regularization
Adeyemi D. Adeoye, Alberto Bemporad
Optimization problems that include regularization functions in their objectives are regularly solved in many applications. When one seeks second-order methods for such problems, it…
Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering
Alberto Bemporad
This paper investigates the use of extended Kalman filtering to train recurrent neural networks with rather general convex loss functions and regularization terms on the network pa…
Piecewise linear regression and classification
Alberto Bemporad
This paper proposes a method for solving multivariate regression and classification problems using piecewise linear predictors over a polyhedral partition of the feature space. The…
Active preference learning based on radial basis functions
Alberto Bemporad, Dario Piga
This paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as…
Global optimization via inverse distance weighting and radial basis functions
Alberto Bemporad
Global optimization problems whose objective function is expensive to evaluate can be solved effectively by recursively fitting a surrogate function to function samples and minimiz…
Fitting Jump Models
A. Bemporad, V. Breschi, D. Piga +1
We describe a new framework for fitting jump models to a sequence of data. The key idea is to alternate between minimizing a loss function to fit multiple model parameters, and min…