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This paper studies stochastic linear bandits where the agent only observes a random subset of action coordinates, proving that sublinear regret is possible when actions have low intrinsic dimension, and proposes the TOFU-POV algorithm with theoretical guarantees.
This paper proposes pattern-aware graph neural networks that explicitly encode missingness patterns alongside observed values, achieving an average improvement of 17% in balanced accuracy and 22% in F1-macro across seven UCI datasets.
Proposes CAGI, a framework that integrates clustering and generative adversarial networks to improve missing data imputation by exploiting latent subgroup structures, achieving superior performance on benchmark datasets.
SafeImpute proposes a reliable imputation framework for irregular clinical data using graph neural networks and conformal selection to control the false discovery rate of clinically unacceptable errors.
Presents a diffusion-based approach for generating irregular clinical time series that jointly models laboratory values and their observation patterns, using the DACMI benchmark from MIMIC-III. The model captures clinically meaningful dependencies between patient physiology and testing behavior under MNAR-like missingness.
This paper introduces MedicalRec, a transformer-based recommender system that suggests optimal models for medical image classification tasks without retraining, built on a dataset (MedicalRec-Bench) compiled from 3,000 articles with over 5,000 records.
GiFlow is a graph-informed flow matching framework for spatiotemporal imputation that replaces Gaussian priors with a graph-informed prior, and uses a hybrid vector field model combining spatial attention, temporal attention, and spatiotemporal propagation. It outperforms state-of-the-art methods on synthetic and real-world datasets.
Proposes ReTAMamba, a method using reliability-aware temporal aggregation with Mamba for irregular clinical time series prediction, achieving significant AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.
This paper proposes a two-stage sampling design where LLM evaluations are used to augment, rather than replace, human ratings, and provides guidance on determining sample sizes for human and LLM reviews using a doubly robust estimator from missing data literature.
This paper identifies the problem of missing observations in inverse reinforcement learning (IRL) that can make expert actions appear suboptimal, and develops a practical algorithm to quantify the minimal perturbations needed for expert actions to appear optimal, validated on synthetic tasks, cancer treatment simulation, and ICU data.