activity
20162026
most citedAre deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison

11 citations · 12 across the 5 of their papers we have counts for

collaborators

11 papers

cs.SE2026

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini +82

AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not…

econ.GN2022

Gender gaps in frontier entrepreneurship? Evidence from 1901 Oklahoma land lottery winners

Jason Poulos

The paper investigates gender differences in entrepreneurship by exploiting a large-scale land lottery in Oklahoma at the turn of the 20 century. Lottery winners clai…

stat.ME2021

Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment

Jason Poulos, Andrea Albanese, Andrea Mercatanti +1

We propose a method of retrospective counterfactual imputation in panel data settings with later-treated and always-treated units, but no never-treated units. We use the observed o…

econ.GN2021★ 1 cited

Amnesty Policy and Elite Persistence in the Postbellum South: Evidence from a Regression Discontinuity Design

Jason Poulos

This paper investigates the impact of Reconstruction-era amnesty policy on the officeholding and wealth of elites in the postbellum South. Amnesty policy restricted the political a…

cs.LG2021★ 11 cited

Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison

Zhenhua Wang, Olanrewaju Akande, Jason Poulos +1

Multiple imputation (MI) is a popular approach for dealing with missing data arising from non-response in sample surveys. Multiple imputation by chained equations (MICE) is one of…

cs.AI2020

Adversarial Machine Learning: Bayesian Perspectives

David Rios Insua, Roi Naveiro, Victor Gallego +1

Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting machine learning (ML) systems against security threats: in certain scenarios there may be advers…