no code implementations • 10 Apr 2016 • Luis E. Ortiz, Boshen Wang, Ze Gong
Almost all of the work in graphical models for game theory has mirrored previous work in probabilistic graphical models.
no code implementations • 26 May 2015 • Joshua Belanich, Luis E. Ortiz
Answers to the open problems can have immediate significant impact to (1) cementing previously established results on asymptotic convergence properties of Optimal AdaBoost, for finite datasets, which in turn can be the start to any convergence-rate analysis; (2) understanding the weak-hypotheses class of effective decision stumps generated from data, which we have empirically observed to be significantly smaller than the typically obtained class, as well as the effect on the weak learner's running time and previously established improved bounds on the generalization performance of Optimal AdaBoost classifiers; and (3) shedding some light on the "self control" that AdaBoost tends to exhibit in practice.
no code implementations • 6 May 2015 • Luis E. Ortiz
This note provides several characterizations of graphical potential games by leveraging well-known results from the literature on probabilistic graphical models.
no code implementations • NeurIPS 2014 • Mohammad T. Irfan, Luis E. Ortiz
For a special case of our model, we show that an equilibrium point always exists and that the equilibrium interest rates are unique.
no code implementations • NeurIPS 2014 • Hau Chan, Luis E. Ortiz
Like traditional IDS games, originally introduced by economists and risk-assessment experts Heal and Kunreuther about a decade ago, generalized IDS games model agents’ voluntary investment decisions when facing potential direct risk and transfer risk exposure from other agents.
no code implementations • 12 Nov 2014 • Luis E. Ortiz
This short paper concerns discretization schemes for representing and computing approximate Nash equilibria, with emphasis on graphical games, but briefly touching on normal-form and poly-matrix games.
no code implementations • 5 Dec 2012 • Joshua Belanich, Luis E. Ortiz
We provide constructive proofs of several arbitrarily accurate approximations of Optimal AdaBoost; prove that they exhibit certain cycling behavior in finite time, and that the resulting dynamical system is ergodic; and establish sufficient conditions for the same to hold for the actual Optimal-AdaBoost update.
no code implementations • NeurIPS 2009 • Jean Honorio, Dimitris Samaras, Nikos Paragios, Rita Goldstein, Luis E. Ortiz
Locality information is crucial in datasets where each variable corresponds to a measurement in a manifold (silhouettes, motion trajectories, 2D and 3D images).
no code implementations • NeurIPS 2007 • Luis E. Ortiz
This paper proposes constraint propagation relaxation (CPR), a probabilistic approach to classical constraint propagation that provides another view on the whole parametric family of survey propagation algorithms SP(ρ), ranging from belief propagation (ρ = 0) to (pure) survey propagation(ρ = 1).