316 citations · 661 across the 65 of their papers we have counts for
6 papers · 1 filter
-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…
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Akash Srivastava, Yamini Bansal, Yukun Ding +15
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the…
The Computational Limits of Deep Learning
Neil C. Thompson, Kristjan Greenewald, Keeheon Lee +1
Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, t…
Gaussian-Smooth Optimal Transport: Metric Structure and Statistical Efficiency
Ziv Goldfeld, Kristjan Greenewald
Optimal transport (OT), and in particular the Wasserstein distance, has seen a surge of interest and applications in machine learning. However, empirical approximation under Wasser…