Boosting as a Metaphor for Algorithm Design

By Kevin Leyton-Brown, Eugene Nudelman, Galen Andrew, Jim McFadden, Yoav Shoham. 2003

80 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Computer Networks and Communications

Citations in the corpus

Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches (2019, background): “Another approach is to model the ASP as a regression problem, in which one tries to predict the performance of each algorithm for a given instance and the best one is selected (Xu et al., 2008; Leyton-Brown et al., 2003b; Battistutta et al., 2017).”
Cross-disciplinary perspectives on meta-learning for algorithm selection (2009, methodology): “Throughout a series of papers over several years, Leyton-Brown and co-authors extended the methodology to consider algorithm selection in algorithm portfolios [Leyton-Brown et al. 2003a], and turned their attention to the SAT problem [Nudelman et al. 2004].”
SATzilla: Portfolio-based Algorithm Selection for SAT (2008, methodology): “Eugene Nudelman, Alex Devkar and Yoav Shoham were Kevin and Holger’s co-authors on the papers that first introduced SATzilla (Nudelman et al., 2004a, 2004b); this work grew out of a project on automated algorithm selection that involved Galen Andrew and Jim McFadden, in addition to Kevin, Eugene and”
Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection (2010, unknown): “Alternatively, we might try to build a new algorithm to complement the portfolio, which has been dubbed “boosting as a metaphor for algorithm design” (Leyton-Brown et al. 2003a).”
Algorithm runtime prediction: Methods & evaluation (2012, methodology): “An EPM for one or more algorithms can be used to set the parameters of existing benchmark generators in order to create instances that are hard for the algorithms in question [74,76].”
Instance-Specific Algorithm Configuration (2014, background): “The inception of algorithm portfolios [40, 67, 87, 125] has had a dramatic impact on constraint programming, operations research, and many other fields.”
Automated Algorithm Configuration and Parameter Tuning (2012, methodology): “…to be applied to a given problem instance based on properties of that instance determined just before attempting to solve it (see, e.g., Rice, 1976; Leyton-Brown et al, 2003; Guerri and Milano, 2004; Xu et al, 2007, 2008); similarly, per-instance algorithm configuration methods use instance…”