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Found 9 papers, 0 papers with code
Instance-dependent uniform tail bounds for empirical processes
We formulate a uniform tail bound for empirical processes indexed by a class of functions, in terms of the individual deviations of the functions rather than the worst-case deviation in the considered class.
Decentralized Feature-Distributed Optimization for Generalized Linear Models
We consider the "all-for-one" decentralized learning problem for generalized linear models.
Max-Linear Regression by Scalable and Guaranteed Convex Programming
When the $k$ linear components are equally likely to achieve the maximum, our result shows that a sufficient number of observations scales as $k^{2}p$ up to a logarithmic factor.
Low-Rank Matrix Estimation From Rank-One Projections by Unlifted Convex Optimization
We study an estimator with a convex formulation for recovery of low-rank matrices from rank-one projections.
Convex Programming for Estimation in Nonlinear Recurrent Models
We propose a formulation for nonlinear recurrent models that includes simple parametric models of recurrent neural networks as a special case.
Estimation from Non-Linear Observations via Convex Programming with Application to Bilinear Regression
We propose a computationally efficient estimator, formulated as a convex program, for a broad class of non-linear regression problems that involve difference of convex (DC) non-linearities.
Solving Equations of Random Convex Functions via Anchored Regression
We consider the question of estimating a solution to a system of equations that involve convex nonlinearities, a problem that is common in machine learning and signal processing.
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation
We propose a flexible convex relaxation for the phase retrieval problem that operates in the natural domain of the signal.
Learning Model-Based Sparsity via Projected Gradient Descent
As an example we elaborate on application of the main results to estimation in Generalized Linear Model.
