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Found 8 papers, 1 papers with code
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation.
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
Our goal is to learn unit-specific potential outcomes for any combination of these $p$ interventions, i. e., $N \times 2^p$ causal parameters.
Kernel Ridge Regression Inference
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.
Frank Wolfe Meets Metric Entropy
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.
Classification as Direction Recovery: Improved Guarantees via Scale Invariance
Modern algorithms for binary classification rely on an intermediate regression problem for computational tractability.
Stability and Efficiency of Random Serial Dictatorship
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.
Localization, Convexity, and Star Aggregation
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.
A Possibility in Algorithmic Fairness: Can Calibration and Equal Error Rates Be Reconciled?
Decision makers increasingly rely on algorithmic risk scores to determine access to binary treatments including bail, loans, and medical interventions.
