Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.
Finite-time convergence rates of nonlinear two-time-scale stochastic approximation under Markovian noise.arXiv preprint arXiv:2104.01627
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Under nested local linearity, nonlinear two-time-scale SA achieves finite-time decoupled convergence; nonlinearity in the slow update alone can destroy it.
citing papers explorer
-
Structure from Strategic Interaction & Uncertainty: Risk Sensitive Games for Robust Preference Learning
Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.
-
Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation
Under nested local linearity, nonlinear two-time-scale SA achieves finite-time decoupled convergence; nonlinearity in the slow update alone can destroy it.