254 citations · 882 across the 66 of their papers we have counts for
6 papers · 1 filter
Domain Adaptation under Missingness Shift
Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton
Rates of missing data often depend on record-keeping policies and thus may change across times and locations, even when the underlying features are comparatively stable. In this pa…
Domain Adaptation under Open Set Label Shift
Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton
We introduce the problem of domain adaptation under Open Set Label Shift (OSLS) where the label distribution can change arbitrarily and a new class may arrive during deployment, bu…
Median Regularity and Honest Inference
Arun Kumar Kuchibhotla, Sivaraman Balakrishnan, Larry Wasserman
We introduce a new notion of regularity of an estimator called median regularity. We prove that uniformly valid (honest) inference for a functional is possible if and only if there…
Minimax rates for heterogeneous causal effect estimation
Edward H. Kennedy, Sivaraman Balakrishnan, James M. Robins +1
Estimation of heterogeneous causal effects - i.e., how effects of policies and treatments vary across subjects - is a fundamental task in causal inference. Many methods for estimat…
Local permutation tests for conditional independence
Ilmun Kim, Matey Neykov, Sivaraman Balakrishnan +1
In this paper, we investigate local permutation tests for testing conditional independence between two random vectors and given . The local permutation test determines t…
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton +2
Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions that may cause performance drops. In this work…