no code implementations • 1 Feb 2024 • Sven Klaassen, Jan Teichert-Kluge, Philipp Bach, Victor Chernozhukov, Martin Spindler, Suhas Vijaykumar
This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation.
1 code implementation • NeurIPS 2023 • Abhineet Agarwal, Anish Agarwal, Suhas Vijaykumar
Our goal is to learn unit-specific potential outcomes for any combination of these $p$ interventions, i. e., $N \times 2^p$ causal parameters.
no code implementations • 13 Feb 2023 • Rahul Singh, Suhas Vijaykumar
We provide uniform inference and confidence bands for kernel ridge regression (KRR), a widely-used non-parametric regression estimator for general data types including rankings, images, and graphs.
no code implementations • 17 May 2022 • Suhas Vijaykumar
The Frank-Wolfe algorithm has seen a resurgence in popularity due to its ability to efficiently solve constrained optimization problems in machine learning and high-dimensional statistics.
no code implementations • 17 May 2022 • Suhas Vijaykumar, Claire Lazar Reich
Modern algorithms for binary classification rely on an intermediate regression problem for computational tractability.
no code implementations • 13 Oct 2021 • Suhas Vijaykumar
This paper establishes non-asymptotic convergence of the cutoffs in Random serial dictatorship in an environment with many students, many schools, and arbitrary student preferences.
no code implementations • NeurIPS 2021 • Suhas Vijaykumar
Offset Rademacher complexities have been shown to provide tight upper bounds for the square loss in a broad class of problems including improper statistical learning and online learning.
no code implementations • 18 Feb 2020 • Claire Lazar Reich, Suhas Vijaykumar
Decision makers increasingly rely on algorithmic risk scores to determine access to binary treatments including bail, loans, and medical interventions.