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Found 11 papers, 0 papers with code
Uniform Risk Bounds for Learning with Dependent Data Sequences
This paper extends standard results from learning theory with independent data to sequences of dependent data.
Margin theory for the scenario-based approach to robust optimization in high dimension
This paper deals with the scenario approach to robust optimization.
Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses
This paper proposes a simple approach to derive efficient error bounds for learning multiple components with sparsity-inducing regularization.
Rademacher Complexity and Generalization Performance of Multi-category Margin Classifiers
This capacity measure is then linked to the metric entropy through the chaining method.
On the exact minimization of saturated loss functions for robust regression and subspace estimation
This paper deals with robust regression and subspace estimation and more precisely with the problem of minimizing a saturated loss function.
Error Bounds for Piecewise Smooth and Switching Regression
For switching regression, the decomposition can be performed directly at the level of the Rademacher complexities, which yields bounds with a linear dependency on the number of modes.
Global optimization for low-dimensional switching linear regression and bounded-error estimation
The paper provides global optimization algorithms for two particularly difficult nonconvex problems raised by hybrid system identification: switching linear regression and bounded-error estimation.
On the complexity of switching linear regression
This technical note extends recent results on the computational complexity of globally minimizing the error of piecewise-affine models to the related problem of minimizing the error of switching linear regression models.
On the complexity of piecewise affine system identification
Previous work showed that a global solution could be obtained for continuous PWA maps with a worst-case complexity exponential in the number of data.
Sparse phase retrieval via group-sparse optimization
This paper deals with sparse phase retrieval, i. e., the problem of estimating a vector from quadratic measurements under the assumption that few components are nonzero.
Finding sparse solutions of systems of polynomial equations via group-sparsity optimization
In particular, we show how these solutions can be recovered from group-sparse solutions of a derived system of linear equations.
