Zobrazeno 1 - 10
of 286
pro vyhledávání: '"Royset, Johannes"'
Labeling errors in datasets are common, if not systematic, in practice. They naturally arise in a variety of contexts-human labeling, noisy labeling, and weak labeling (i.e., image classification), for example. This presents a persistent and pervasiv
Externí odkaz:
http://arxiv.org/abs/2405.20531
Stochastic optimization problems are generally known to be ill-conditioned to the form of the underlying uncertainty. A framework is introduced for optimal control problems with partial differential equations as constraints that is robust to inaccura
Externí odkaz:
http://arxiv.org/abs/2405.00176
Autor:
Deride, Julio, Royset, Johannes O.
Solutions of an optimization problem are sensitive to changes caused by approximations or parametric perturbations, especially in the nonconvex setting. This paper investigates the ability of substitute problems, constructed from Rockafellian functio
Externí odkaz:
http://arxiv.org/abs/2404.18097
For parameterized mixed-binary optimization problems, we construct local decision rules that prescribe near-optimal courses of action across a set of parameter values. The decision rules stem from solving risk-adaptive training problems over classes
Externí odkaz:
http://arxiv.org/abs/2310.09844
In many recent works, there is an increased focus on designing algorithms that seek flatter optima for neural network loss optimization as there is empirical evidence that it leads to better generalization performance in many datasets. In this work,
Externí odkaz:
http://arxiv.org/abs/2310.00488
In multi-agent search planning for a randomly moving and camouflaging target, we examine heterogeneous searchers that differ in terms of their endurance level, travel speed, and detection ability. This leads to a convex mixed-integer nonlinear progra
Externí odkaz:
http://arxiv.org/abs/2309.02629
We propose a method for finding a cumulative distribution function (cdf) that minimizes the (regularized) distance to a given cdf, while belonging to an ambiguity set constructed relative to another cdf and, possibly, incorporating soft information.
Externí odkaz:
http://arxiv.org/abs/2309.00070
Autor:
Warner, Steven M., Royset, Johannes O.
Publikováno v:
Military Operations Research, 2024 Jan 01. 29(1), 31-44.
Externí odkaz:
https://www.jstor.org/stable/27300954
Autor:
Royset, Johannes O.
Uncertainty is prevalent in engineering design, data-driven problems, and decision making broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to address uncertainty by formulating and solving conservative optimizati
Externí odkaz:
http://arxiv.org/abs/2212.00856
Autor:
Warner, Steven M., Royset, Johannes O.
The Synthetic Theater Operations Research Model (STORM) simulates theater-level conflict and requires inputs about utilization of surveillance satellites to search large geographical areas. We develop a mixed-integer linear optimization model that pr
Externí odkaz:
http://arxiv.org/abs/2210.11370