Nov 14, 2020 · In this chapter, we survey recent work that helps put data-driven combinatorial algorithm design on firm foundations. We provide strong computational and statistical performance guarantees, both for the batch and online scenarios where a collection of typical problem instances from the given application are presented either all at once or in an ...

Data-driven models are a class of computational models that primarily rely on historical data collected throughout a system's or process' lifetime to establish relationships between input, internal, and output variables.

Jun 1, 2020 · We model the problem of identifying a good algorithm from data as a statistical learning problem. Our framework captures several state-of-the-art empirical and theoretical approaches to the problem, and our results identify conditions under which these approaches are guaranteed to perform well.

Aug 10, 2020 · Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access.

Apr 4, 2018 · We must take a data-driven problem, to spot check algorithms, to grid search algorithm parameters and to quickly find methods that yield good results, reliably and fast.

Data-Driven Algorithm Design By Rishi Gupta and Tim Roughgarden DOI:10.1145/3394625 Abstract The best algorithm for a computational problem generally depends on the “relevant inputs,” a concept that depends on the application domain and often defies formal articu-lation. Although there is a large literature on empirical

Dec 5, 2021 · To solve a class of distributed data-driven problems, this work proposes a federated data-driven optimization framework that emphasizes on collaborative surrogate construction, surrogate management and privacy protection in a distributed environment in the presence of noisy and non-independently identically distributed (non-IID) data.

Nov 8, 2017 · Data-driven algorithm design, that is, choosing the best algorithm for a specific application, is a crucial problem in modern data science. Practitioners often optimize over a parameterized algorithm family, tuning parameters based on problems from their domain.

Jul 15, 2024 · More than a decade ago, advances in algorithm configuration (Hoos 2011) paved the way for the use of historical data to modify an algorithm’s (typically fixed, static) parameters.

Aug 28, 2024 · With the rise of data-driven algorithms, both industrial practitioners and academicians have aimed at understanding how one can use past information to make better future decisions.