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Robust Uncertainty Bounds in Reproducing Kernel Hilbert Spaces: A Convex Optimization Approach

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Robust design optimization of gas-lubricated herringbone grooved journal bearings: Surrogate modeling and experimental validation

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This study experimentally validates robust designs for herringbone grooved journal bearings (HGJBs) in micro-turbocompressors, addressing manufacturing deviations that lead to rotor instabilities. Experiments on a Pelton-driven rotor system focused on maxi ...

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Slack-variable Approach for Variational Quantum Semidefinite Programming

Zoë Holmes

Solving optimization problems is a key task for which quantum computers could possibly provide a speedup over the best known classical algorithms. Particular classes of optimization problems including semidefinite programming (SDP) and linear programming ( ...

AMER PHYSICAL SOC2025

Impact of Data Processing on Fairness in Supervised Learning

Negar Kiyavash

We study the impact of pre and postprocessing for reducing discrimination in data-driven decision makers. We first analyze the fundamental trade-off between fairness and accuracy in a preprocessing approach, and propose a design for a preprocessing module ...

IEEE2021

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

A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization

Volkan Cevher, Quoc Tran Dinh

We propose a new and low per-iteration complexity first-order primal-dual optimization framework for a convex optimization template with broad applications. Our analysis relies on a novel combination of three classic ideas applied to the primal-dual gap fu ...

SIAM Publications2018