130 citations · 317 across the 27 of their papers we have counts for
6 papers · 1 filter
Optimal Activation Functions for the Random Features Regression Model
Jianxin Wang, José Bento
The asymptotic mean squared test error and sensitivity of the Random Features Regression model (RFR) have been recently studied. We build on this work and identify in closed-form t…
How is Distributed ADMM Affected by Network Topology?
Guilherme França, José Bento
When solving consensus optimization problems over a graph, there is often an explicit characterization of the convergence rate of Gradient Descent (GD) using the spectrum of the gr…
Markov Chain Lifting and Distributed ADMM
Guilherme França, José Bento
The time to converge to the steady state of a finite Markov chain can be greatly reduced by a lifting operation, which creates a new Markov chain on an expanded state space. For a…
Tuning Over-Relaxed ADMM
Guilherme França, José Bento
The framework of Integral Quadratic Constraints (IQC) reduces the computation of upper bounds on the convergence rate of several optimization algorithms to a semi-definite program…
An Explicit Rate Bound for the Over-Relaxed ADMM
Guilherme França, José Bento
The framework of Integral Quadratic Constraints of Lessard et al. (2014) reduces the computation of upper bounds on the convergence rate of several optimization algorithms to semi-…
On the trade-off between complexity and correlation decay in structural learning algorithms
José Bento, Andrea Montanari
We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish…