17 citations · 37 across the 14 of their papers we have counts for
14 papers
Bilevel optimization with a multi-objective lower-level problem: Risk-neutral and risk-averse formulations
Tommaso Giovannelli, Griffin Dean Kent, Luis Nunes Vicente
In this work, we propose different formulations and gradient-based algorithms for deterministic and stochastic bilevel problems with conflicting objectives in the lower level. Such…
An integrated assignment, routing, and speed model for roadway mobility and transportation with environmental, efficiency, and service goals
Tommaso Giovannelli, Luis Nunes Vicente
Managing all the mobility and transportation services with autonomous vehicles for users of a smart city requires determining the assignment of the vehicles to the users and their…
Convergence rates of the stochastic alternating algorithm for bi-objective optimization
Suyun Liu, Luis Nunes Vicente
Stochastic alternating algorithms for bi-objective optimization are considered when optimizing two conflicting functions for which optimization steps have to be applied separately…
Stochastic trust-region and direct-search methods: A weak tail bound condition and reduced sample sizing
Francesco Rinaldi, Luis Nunes Vicente, Damiano Zeffiro
Using tail bounds, we introduce a new probabilistic condition for function estimation in stochastic derivative-free optimization which leads to a reduction in the number of samples…
Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problems
Tommaso Giovannelli, Griffin Dean Kent, Luis Nunes Vicente
Two-level stochastic optimization formulations have become instrumental in a number of machine learning contexts such as continual learning, neural architecture search, adversarial…
The Sharpe predictor for fairness in machine learning
Suyun Liu, Luis Nunes Vicente
In machine learning (ML) applications, unfair predictions may discriminate against a minority group. Most existing approaches for fair machine learning (FML) treat fairness as a co…
Full-low evaluation methods for derivative-free optimization
Albert S. Berahas, Oumaima Sohab, Luis Nunes Vicente
We propose a new class of rigorous methods for derivative-free optimization with the aim of delivering efficient and robust numerical performance for functions of all types, from s…
A Stochastic Alternating Balance -Means Algorithm for Fair Clustering
Suyun Liu, Luis Nunes Vicente
In the application of data clustering to human-centric decision-making systems, such as loan applications and advertisement recommendations, the clustering outcome might discrimina…
Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach
Suyun Liu, Luis Nunes Vicente
In the application of machine learning to real-life decision-making systems, e.g., credit scoring and criminal justice, the prediction outcomes might discriminate against people wi…
Modeling Hessian-vector products in nonlinear optimization: New Hessian-free methods
Lili Song, Luis Nunes Vicente
In this paper, we suggest two ways of calculating interpolation models for unconstrained smooth nonlinear optimization when Hessian-vector products are available. The main idea is…
The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning
Suyun Liu, Luis Nunes Vicente
Optimization of conflicting functions is of paramount importance in decision making, and real world applications frequently involve data that is uncertain or unknown, resulting in…
Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization
Afonso S. Bandeira, Katya Scheinberg, Luis Nunes Vicente
Interpolation-based trust-region methods are an important class of algorithms for Derivative-Free Optimization which rely on locally approximating an objective function by quadrati…
On partial sparse recovery
Afonso S. Bandeira, Katya Scheinberg, Luis Nunes Vicente
We consider the problem of recovering a partially sparse solution of an underdetermined system of linear equations by minimizing the -norm of the part of the solution vecto…
Convergence of trust-region methods based on probabilistic models
Afonso S. Bandeira, Katya Scheinberg, Luis Nunes Vicente
In this paper we consider the use of probabilistic or random models within a classical trust-region framework for optimization of deterministic smooth general nonlinear functions.…