From the 1 of 21 linked papers with an AI index.
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Low-Rank Thinning
Annabelle Michael Carrell, Albert Gong, Abhishek Shetty +2
The goal in thinning is to summarize a dataset using a small set of representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel Halving and Compress can match t…
Learning Counterfactual Distributions via Kernel Nearest Neighbors
Kyuseong Choi, Jacob Feitelberg, Caleb Chin +2
Consider a setting with multiple units (e.g., individuals, cohorts, geographic locations) and outcomes (e.g., treatments, times, items), where the goal is to learn a multivariate d…
Two-Sided Nearest Neighbors: An adaptive and minimax optimal procedure for matrix completion
Tathagata Sadhukhan, Manit Paul, Raaz Dwivedi
Nearest neighbor (NN) algorithms have been extensively used for missing data problems in recommender systems and sequential decision-making systems. Prior theoretical analysis has…
Counterfactual inference in sequential experiments
Raaz Dwivedi, Katherine Tian, Sabina Tomkins +3
We consider after-study statistical inference for sequentially designed experiments wherein multiple units are assigned treatments for multiple time points using treatment policies…
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space
Jacob Feitelberg, Kyuseong Choi, Anish Agarwal +1
We study the problem of distributional matrix completion: Given a sparsely observed matrix of empirical distributions, we seek to impute the true distributions associated with both…
Adaptively-weighted Nearest Neighbors for Matrix Completion
Tathagata Sadhukhan, Manit Paul, Raaz Dwivedi
In this technical note, we introduce and analyze AWNN: an adaptively weighted nearest neighbor method for performing matrix completion. Nearest neighbor (NN) methods are widely use…