14 citations · 82 across the 28 of their papers we have counts for
4 papers · 1 filter
The Proximal Robbins-Monro Method
Panos Toulis, Thibaut Horel, Edoardo M. Airoldi
The need for parameter estimation with massive datasets has reinvigorated interest in stochastic optimization and iterative estimation procedures. Stochastic approximations are at…
Stochastic gradient descent methods for estimation with large data sets
Dustin Tran, Panos Toulis, Edoardo M. Airoldi
We develop methods for parameter estimation in settings with large-scale data sets, where traditional methods are no longer tenable. Our methods rely on stochastic approximations,…
Incentive-Compatible Experimental Design
Panos Toulis, David C. Parkes, Elery Pfeffer +1
We consider the design of experiments to evaluate treatments that are administered by self-interested agents, each seeking to achieve the highest evaluation and win the experiment.…
Towards stability and optimality in stochastic gradient descent
Panos Toulis, Dustin Tran, Edoardo M. Airoldi
Iterative procedures for parameter estimation based on stochastic gradient descent allow the estimation to scale to massive data sets. However, in both theory and practice, they su…