no code implementations • 3 Oct 2023 • Shakeeb Khan, Tatiana Komarova, Denis Nekipelov
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).
no code implementations • 3 Feb 2023 • Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution.
no code implementations • 3 Feb 2022 • Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
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.
no code implementations • 6 Oct 2021 • Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
We propose a new problem setting to study the sequential interactions between a recommender system and a user.
no code implementations • 25 Jun 2020 • Tatiana Komarova, Denis Nekipelov
We show that identification becomes possible if the target parameter can be deterministically located within the random set.
no code implementations • ICML 2020 • Yiding Feng, Ekaterina Khmelnitskaya, Denis Nekipelov
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.
3 code implementations • 13 Jun 2018 • Denis Nekipelov, Vira Semenova, Vasilis Syrgkanis
This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR).
no code implementations • NeurIPS 2017 • Darrell Hoy, Denis Nekipelov, Vasilis Syrgkanis
The notion of the price of anarchy takes a worst-case stance to efficiency analysis, considering instance independent guarantees of efficiency.