Publications related to Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Michel Bierlaire

In this paper, we present a spatial branch and bound algorithm to tackle the continuous pricing problem, where demand is captured by an advanced discrete choice model (DCM). Advanced DCMs, like mixed logit or latent class models, are capable of modeling de ...

2023

Localizing Unsynchronized Sensors With Unknown Sources

Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the source ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

Volkan Cevher, Kimon Antonakopoulos, Ali Kavis

This work proposes a universal and adaptive second-order method for minimizing second-order smooth, convex functions. Our algorithm achieves O(σ/T‾‾√) convergence when the oracle feedback is stochastic with variance σ2, and improves its convergence to O(1/ ...

2022

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

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Ahmet Alacaoglu

Stochastic gradient descent (SGD) and randomized coordinate descent (RCD) are two of the workhorses for training modern automated decision systems. Intriguingly, convergence properties of these methods are not well-established as we move away from the spec ...

EPFL2021

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

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

Fast Projection Onto Convex Smooth Constraints

Maryam Kamgarpour, Andreas Krause, Ilnura Usmanova

The Euclidean projection onto a convex set is an important problem that arises in numerous constrained optimization tasks. Unfortunately, in many cases, computing projections is computationally demanding. In this work, we focus on projection problems where ...

PMLR2021

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

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

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Localizing Unsynchronized Sensors With Unknown Sources

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

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

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Integrating advanced discrete choice models in mixed integer linear optimization

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

Fast Projection Onto Convex Smooth Constraints

Convergence without Convexity: Sampling, Optimization, and Games

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Integrating advanced discrete choice models in mixed integer linear optimization

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

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

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

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

Fast Projection Onto Convex Smooth Constraints

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Convergence without Convexity: Sampling, Optimization, and Games

Localizing Unsynchronized Sensors With Unknown Sources