A projected gradient descent algorithm for noisy inductive matrix completion achieves linear convergence and stable recovery at sample complexity governed by side-information dimension, extending to inexact side-information with optimal error degradation.
arXiv preprint arXiv:2206.04044 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ML 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A T-estimation-based procedure for adaptive density estimation and optimal control in offline contextual MDPs without stationarity, providing oracle risk bounds under two loss functions and finite-sample cost guarantees.
citing papers explorer
-
Sample-efficient inductive matrix completion with noise and inexact side-information
A projected gradient descent algorithm for noisy inductive matrix completion achieves linear convergence and stable recovery at sample complexity governed by side-information dimension, extending to inexact side-information with optimal error degradation.
-
Adaptive Estimation and Optimal Control in Offline Contextual MDPs without Stationarity
A T-estimation-based procedure for adaptive density estimation and optimal control in offline contextual MDPs without stationarity, providing oracle risk bounds under two loss functions and finite-sample cost guarantees.