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Found 8 papers, 1 papers with code
On Optimal Set Estimation for Partially Identified Binary Choice Models
We illustrate the general problem in the context of a semiparametric binary choice model with discrete covariates as an example of a model which is partially identified as shown in, e. g. Bierens and Hartog (1988).
How Bad is Top-$K$ Recommendation under Competing Content Creators?
Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution.
Learning from a Learning User for Optimal Recommendations
In real-world recommendation problems, especially those with a formidably large item space, users have to gradually learn to estimate the utility of any fresh recommendations from their experience about previously consumed items.
Learning the Optimal Recommendation from Explorative Users
We propose a new problem setting to study the sequential interactions between a recommender system and a user.
Identification and Formal Privacy Guarantees
We show that identification becomes possible if the target parameter can be deterministically located within the random set.
Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models
Discrete choice models with unobserved heterogeneity are commonly used Econometric models for dynamic Economic behavior which have been adopted in practice to predict behavior of individuals and firms from schooling and job choices to strategic decisions in market competition.
Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models
This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR).
Welfare Guarantees from Data
The notion of the price of anarchy takes a worst-case stance to efficiency analysis, considering instance independent guarantees of efficiency.
