Publications related to Regularized Bundle Methods for Convex and Non-Convex Risks

A Newton Frank-Wolfe method for constrained self-concordant minimization

We develop a new Newton Frank-Wolfe algorithm to solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO). Unlike L-smooth convex functions, where the Lipschitz continuity of the objective gradient holds gl ...

SPRINGER2021

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Integrating advanced discrete choice models in mixed integer linear optimization

Michel Bierlaire, Bernard Gendron

In many transportation systems, a mismatch between the associated design and planning decisions and the demand is typically encountered. A tailored system is not only appealing to operators, which could have a better knowledge of their operational costs, b ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Martin Jaggi, Sebastian Urban Stich, Amirkeivan Mohtashami

It has been experimentally observed that the efficiency of distributed training with stochastic gradient (SGD) depends decisively on the batch size and—in asynchronous implementations—on the gradient staleness. Especially, it has been observed that the spe ...

2021

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness not ...

2021

Mathematical Foundations of Robust and Distributionally Robust Optimization

Daniel Kuhn, Jianzhe Zhen, Wolfram Wiesemann

Robust and distributionally robust optimization are modeling paradigms for decision-making under uncertainty where the uncertain parameters are only known to reside in an uncertainty set or are governed by any probability distribution from within an ambigu ...

2021

Large Steps in Inverse Rendering of Geometry

Wenzel Alban Jakob, Baptiste Alexandre Marie Philippe Claude Nicolet

Inverse reconstruction from images is a central problem in many scientific and engineering disciplines. Recent progress on differentiable rendering has led to methods that can efficiently differentiate the full process of image formation with respect to mi ...

ASSOC COMPUTING MACHINERY2021

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Volkan Cevher, Shaul Nadav Hallak

This paper develops a methodology for regret minimization with stochastic first-order oracle feedback in online, constrained, non-smooth, non-convex problems. In this setting, the minimization of external regret is beyond reach for first-order methods, and ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Adaptive Gradient Descent without Descent

Konstantin Mishchenko

We present a strikingly simple proof that two rules are sufficient to automate gradient descent: 1) don’t increase the stepsize too fast and 2) don’t overstep the local curvature. No need for functional values, no line search, no information about the func ...

2020

Convergence without Convexity: Sampling, Optimization, and Games

Ya-Ping Hsieh

Many important problems in contemporary machine learning involve solving highly non- convex problems in sampling, optimization, or games. The absence of convexity poses significant challenges to convergence analysis of most training algorithms, and in some ...

EPFL2020

Regularized Bundle Methods for Convex and Non-Convex Risks

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A Newton Frank-Wolfe method for constrained self-concordant minimization

A first-order primal-dual method with adaptivity to local smoothness

Integrating advanced discrete choice models in mixed integer linear optimization

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Mathematical Foundations of Robust and Distributionally Robust Optimization

Large Steps in Inverse Rendering of Geometry

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Adaptive Gradient Descent without Descent

Convergence without Convexity: Sampling, Optimization, and Games

A first-order primal-dual method with adaptivity to local smoothness

Integrating advanced discrete choice models in mixed integer linear optimization

Mathematical Foundations of Robust and Distributionally Robust Optimization

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

A Newton Frank-Wolfe method for constrained self-concordant minimization

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Large Steps in Inverse Rendering of Geometry

Adaptive Gradient Descent without Descent

Convergence without Convexity: Sampling, Optimization, and Games