199 citations · 1.1k across the 75 of their papers we have counts for
4 papers · 1 filter
Minimum Excess Risk in Bayesian Learning
Aolin Xu, Maxim Raginsky
We analyze the best achievable performance of Bayesian learning under generative models by defining and upper-bounding the minimum excess risk (MER): the gap between the minimum ex…
Learning Recurrent Neural Net Models of Nonlinear Systems
Joshua Hanson, Maxim Raginsky, Eduardo Sontag
We consider the following learning problem: Given sample pairs of input and output signals generated by an unknown nonlinear system (which is not assumed to be causal or time-invar…
Partially Observed Discrete-Time Risk-Sensitive Mean Field Games
Naci Saldi, Tamer Basar, Maxim Raginsky
In this paper, we consider discrete-time partially observed mean-field games with the risk-sensitive optimality criterion. We introduce risk-sensitivity behaviour for each agent vi…
Function approximation by neural nets in the mean-field regime: Entropic regularization and controlled McKean-Vlasov dynamics
Belinda Tzen, Maxim Raginsky
We consider the problem of function approximation by two-layer neural nets with random weights that are "nearly Gaussian" in the sense of Kullback-Leibler divergence. Our setting i…