124 citations
- University of ChicagoUS6 papers
- Banco Central de Chile1 paper
- City University of Hong KongHK1 paper
- Columbia UniversityUS1 paper
- Cornell UniversityUS1 paper
- George Mason UniversityUS1 paper
- Google (United States)US1 paper
- Phillips AcademyUS1 paper
- Princeton UniversityUS1 paper
- Risk Engineering (Bulgaria)BG1 paper
- Risk Management Solutions (United Kingdom)GB1 paper
- Stanford UniversityUS1 paper
13 papers
Gambits: Theory and Evidence
Shiva Maharaj, Nicholas Polson, Christian Turk
Gambits are central to human decision-making. Our goal is to provide a theory of Gambits. A Gambit is a combination of psychological and technical factors designed to disrupt predi…
Financing Entrepreneurship and Innovation in China
Lin William Cong, Charles M. C. Lee, Yuanyu Qu +1
This study reports on the current state-of-affairs in the funding of entrepreneurship and innovations in China and provides a broad survey of academic findings on the subject. We a…
Inference for Dependent Data with Learned Clusters
Jianfei Cao, Christian Hansen, Damian Kozbur +1
This paper presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependen…
Deep Learning Partial Least Squares
Nicholas Polson, Vadim Sokolov, Jianeng Xu
High dimensional data reduction techniques are provided by using partial least squares within deep learning. Our framework provides a nonlinear extension of PLS together with a dis…
Randomization Inference of Periodicity in Unequally Spaced Time Series with Application to Exoplanet Detection
Panos Toulis, Jacob Bean
The estimation of periodicity is a fundamental task in many scientific areas of study. Existing methods rely on theoretical assumptions that the observation times have equal or i.i…
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
Filip Hanzely, Boxin Zhao, Mladen Kolar
We investigate the optimization aspects of personalized Federated Learning (FL). We propose general optimizers that can be applied to numerous existing personalized FL objectives,…