arXiv Machine Learning
Jul 17
Learning Who to Treat When Treatment is Missing
arXiv:2607. 14346v1 Announce Type: new Abstract: Policy learning methods are increasingly used to inform treatment allocation under budget constraints.
Synthetic Nearest Neighbors: Extending Synthetic Controls for Matrix Completion with Missing Not at Random Data
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arXiv Machine Learning
Aug 18
Doubly robust nearest neighbors in factor models
arXiv:2211. 14297v4 Announce Type: replace-cross Abstract: We introduce and analyze an improved variant of nearest neighbors (NN) for estimation with missing data in latent factor models.
arXiv Machine Learning
Aug 26
Two-Sided Nearest Neighbors: An adaptive and minimax optimal procedure for matrix completion
arXiv:2411.12965v3 Announce Type: replace-cross Abstract: Nearest neighbor (NN) algorithms have been extensively used for missing data problems in recommender systems and sequential decision-making s...
arXiv Machine Learning
Jul 17
Causal Inference for Sequential Settings under Interference and Latent Confounding
arXiv:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.
arXiv Machine Learning
Jun 10
Sample-efficient inductive matrix completion with noise and inexact side-information
arXiv:2605. 17189v2 Announce Type: replace-cross Abstract: Inductive matrix completion (IMC) is a variant of low-rank matrix completion that incorporates row and column side-information.
arXiv Machine Learning
Sep 1
AI-Generated Measurements for Identification and Inference with Missing Data: A Weak Shadow Variable Approach
The paper introduces an assumption‑lean framework that uses AI‑generated measurements as weak shadow variables to identify and infer population quantities when data are missing not at random. Weak shadow variables are outcome‑informative proxies that are conditionally independent of missingness given the true outcome and covariates, and they do not need to predict missing outcomes accurately. The authors derive sharp bounds via linear programs and propose a localized penalized estimator with a subsampling algorithm for confidence intervals, demonstrating in semi‑synthetic experiments that the resulting intervals are substantially narrower and more accurate than classical MNAR methods.
arXiv Machine Learning
Jun 9
Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models
arXiv:2602. 16061v2 Announce Type: replace-cross Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNAR): users with stronger opinions are more likely to respond, so standard estimators are biased and the estimand is not identified without additional assumptions.
arXiv Machine Learning
Jul 14
The Spectral Structure of Latent Treatment Effects
arXiv:2607. 10926v1 Announce Type: new Abstract: Identifying heterogeneous treatment effects under unobserved confounding is central in observational causal inference.
arXiv Machine Learning
Jul 15
Linear Regression under Missing or Corrupted Coordinates
arXiv:2509. 19242v2 Announce Type: replace-cross Abstract: We study multivariate linear regression under Gaussian covariates in two settings, where data may be erased or corrupted by an adversary under a coordinate-wise budget.
arXiv Machine Learning
Jun 2
Riemannian Stochastic Optimization for Sufficient Dimension Reduction
arXiv:2606. 00413v1 Announce Type: cross Abstract: Sufficient dimension reduction (SDR) makes high-dimensional regression tractable by projecting the covariates onto a low-dimensional subspace that preserves the conditional mean of the response.
arXiv Machine Learning
Aug 3
Flow Matching with Missing Data
arXiv:2607. 28698v1 Announce Type: new Abstract: Flow matching assumes fully observed training data, which many real-world applications rarely provide.
arXiv Machine Learning
Sep 10
Low-Rank Plus Sparse Matrix Transfer Learning under Growing Representations and Ambient Dimensions
The paper introduces a transfer learning framework for structured matrix estimation when both the ambient dimension and the intrinsic representation grow over time. It models the target parameter as an embedded source component plus low‑rank innovations and sparse edits, and proposes an anchored alternating projection estimator that preserves the transferred subspace while estimating only the new components. Deterministic error bounds are derived that separate target noise, representation growth, and source estimation error, showing improved rates when rank and sparsity increments are small, and the framework is applied to Markov transition matrix estimation and structured covariance estimation with theoretical guarantees and empirical validation.