9 citations · 18 across the 7 of their papers we have counts for
9 papers
What Would it Cost to End Extreme Poverty?
Roshni Sahoo, Joshua Blumenstock, Paul Niehaus +2
We study poverty minimization via direct transfers, framing this as a statistical learning problem while retaining the information constraints faced by real-world programs. Using n…
Ward identity preserving local ultraviolet counterterms for photoproduction at two loops in QCD
Charalampos Anastasiou, Julia Karlen, Roshni Sahoo +2
We review the construction of locally finite two-loop amplitude integrands for photoproduction via quark annihilation, presented in arXiv:2509.07805. Building on established techni…
General finite two-loop amplitude integrand for photoproduction in quark annihilation
Charalampos Anastasiou, Julia Karlen, Roshni Sahoo +2
The construction of integrands free of infrared and ultraviolet singularities may enable the application of numerical methods to evaluate loop amplitudes that are inaccessible with…
Data Fusion for High-Resolution Estimation
Amy Guan, Roshni Sahoo, Joshua Salomon +2
High-resolution estimates of population health indicators are critical for precision public health. We propose a method for high-resolution estimation that fuses distinct data sour…
Policy Learning under Biased Sample Selection
Lihua Lei, Roshni Sahoo, Stefan Wager
Practitioners often use data from a randomized controlled trial to learn a treatment assignment policy that can be deployed on a target population. A recurring concern in doing so…
Learning from a Biased Sample
Roshni Sahoo, Lihua Lei, Stefan Wager
The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decisi…
Policy Learning with Competing Agents
Roshni Sahoo, Stefan Wager
Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. When agents can respond strategically to s…
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
Shengjia Zhao, Michael P. Kim, Roshni Sahoo +2
When facing uncertainty, decision-makers want predictions they can trust. A machine learning provider can convey confidence to decision-makers by guaranteeing their predictions are…
Unsupervised Domain Adaptation in the Absence of Source Data
Roshni Sahoo, Divya Shanmugam, John Guttag
Current unsupervised domain adaptation methods can address many types of distribution shift, but they assume data from the source domain is freely available. As the use of pre-trai…