147 citations · 182 across the 12 of their papers we have counts for
18 papers
Outlier-Robust Group Inference via Gradient Space Clustering
Yuchen Zeng, Kristjan Greenewald, Kangwook Lee +2
Traditional machine learning models focus on achieving good performance on the overall training distribution, but they often underperform on minority groups. Existing methods can i…
Measuring Generalization with Optimal Transport
Ching-Yao Chuang, Youssef Mroueh, Kristjan Greenewald +2
Understanding the generalization of deep neural networks is one of the most important tasks in deep learning. Although much progress has been made, theoretical error bounds still o…
Entropic Causal Inference: Identifiability and Finite Sample Results
Spencer Compton, Murat Kocaoglu, Kristjan Greenewald +1
Entropic causal inference is a framework for inferring the causal direction between two categorical variables from observational data. The central assumption is that the amount of…
-Variance: A Clustered Notion of Variance
Justin Solomon, Kristjan Greenewald, Haikady N. Nagaraja
We introduce -variance, a generalization of variance built on the machinery of random bipartite matchings. -variance measures the expected cost of matching two sets of sa…
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation
Kristjan Greenewald, Dmitriy Katz-Rogozhnikov, Karthik Shanmugam
The estimation of causal treatment effects from observational data is a fundamental problem in causal inference. To avoid bias, the effect estimator must control for all confounder…
Active Structure Learning of Causal DAGs via Directed Clique Tree
Chandler Squires, Sara Magliacane, Kristjan Greenewald +3
A growing body of work has begun to study intervention design for efficient structure learning of causal directed acyclic graphs (DAGs). A typical setting is a causally sufficient…