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Publications associées (28)

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Linearly Convergent Frank-Wolfe with Backtracking Line-Search

Martin Jaggi

Structured constraints in Machine Learning have recently brought the Frank-Wolfe (FW) family of algorithms back into the spotlight. While the classical FW algorithm has poor local convergence properties, Away-steps FW and Pairwise FW have emerged as improv ...

ADDISON-WESLEY PUBL CO2020

Discrete-time regional pole-placement using convex approximations: Theory and application to a boost converter

Emilio Maddalena

The problem of allocating the closed-loop poles of linear systems in specific regions of the complex plane defined by discrete time-domain requirements is addressed. The resulting non-convex set is inner-approximated by a convex region described with linea ...

2019

Robust control of systems with sector nonlinearities via convex optimization: A data-driven approach

Alireza Karimi, Achille Nicoletti

In this paper, a new data-driven method for designing robust controllers is proposed for systemswith sector-bounded nonlinearities and multimodel uncertainties. The results from the circle criterion are used to generate necessary and sufficient convex cons ...

WILEY2019

On the convergence of stochastic primal-dual hybrid gradient

Volkan Cevher, Ahmet Alacaoglu

In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution and linear convergence with standard ...

2019

Tensor Robust Pca On Graphs

Pierre Vandergheynst, Nauman Shahid, Francesco Grassi

We propose a graph signal processing framework to overcome the computational burden of Tensor Robust PCA (TRPCA). Our framework also serves as a convex alternative to graph regularized tensor factorization methods. Our method is based on projecting a tenso ...

IEEE2019

Almost surely constrained convex optimization

Volkan Cevher, Ahmet Alacaoglu

We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need to be satisfied almost surely. We use smoothing and homotopy techniques to ha ...

2019

Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization

Daniel Kuhn, Wolfram Wiesemann, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh

A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these nominal distributions are themselves estimated from data, which makes them s ...

2019

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Nisheeth Vishnoi, Laura Elisa Celis, Vijay Keswani, Lingxiao Huang

Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying cla ...

ASSOC COMPUTING MACHINERY2019