Alexander V Outkin
Biographic Data
| ID | 8819500 |
|---|---|
| NAME | Alexander V Outkin |
| GIVEN NAMES | Alexander V |
| FAMILY NAME | Outkin |
| SIGNATURE | OUTKIN A V |
| AFFILIATIONS | Los Alamos National Laboratory |
| ORCID | 0000-0001-6922-8343 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2006 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Increasing Trust in Development Processes Using Robust, Data-Driven Markov Games
In this article, we consider how to increase trust in development processes in which there is a risk for adversarial manipulation, and the adversary's objectives and resources are either ill-specified, imprecisely specified, or unknown. In such problems, we must hedge against the risk of misapprehension of attacker objectives and resources, which is further complicated in the absence of adversarial training data. We show how to model dynamic agen…
Neural networks and contagion
We analyze local as well as global interaction and contagion in population games, using the formalism of neural networks. In contrast to much of the literature, a state encodes not only the frequency of play, but also the spatial pattern of play. Stochastic best response dynamics with logistic noise gives rise to a log-linear or logit response model. The stationary distribution is of the Gibbs-Boltzmann type. The long-run equilibria are the maxim…
No prominent works on this page.
Neural networks and contagion
We analyze local as well as global interaction and contagion in population games, using the formalism of neural networks. In contrast to much of the literature, a state encodes not only the frequency of play, but also the spatial pattern of play. Stochastic best response dynamics with logistic noise gives rise to a log-linear or logit response model. The stationary distribution is of the Gibbs-Boltzmann type. The long-run equilibria are the maxim…
Increasing Trust in Development Processes Using Robust, Data-Driven Markov Games
In this article, we consider how to increase trust in development processes in which there is a risk for adversarial manipulation, and the adversary's objectives and resources are either ill-specified, imprecisely specified, or unknown. In such problems, we must hedge against the risk of misapprehension of attacker objectives and resources, which is further complicated in the absence of adversarial training data. We show how to model dynamic agen…
Artificial Intelligence (2 works) · Computer Science (2 works) · Mathematics (2 works) · Adversarial Robustness in Machine Learning (1 work) · Adversarial system (1 work) · Adversary (1 work) · Artificial neural network (1 work) · Computer security (1 work) · Demography (1 work) · Econometrics (1 work)