A nonconvex l1/2-regularized nonnegative matrix factorization method with ADMM solver and detection estimation improves sparse network recovery under imperfect observations compared to baselines.
Exact matrix completion via convex opti- mization
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A low-rank matrix estimation method in a reward-free RL framework learns shared representations across linear MDPs and yields near-optimal policies with characterized regret bounds under relaxed feature assumptions.
citing papers explorer
-
Sparse Network Inference under Imperfect Detection and its Application to Ecological Networks
A nonconvex l1/2-regularized nonnegative matrix factorization method with ADMM solver and detection estimation improves sparse network recovery under imperfect observations compared to baselines.
-
Provable Multi-Task Reinforcement Learning: A Representation Learning Framework with Low Rank Rewards
A low-rank matrix estimation method in a reward-free RL framework learns shared representations across linear MDPs and yields near-optimal policies with characterized regret bounds under relaxed feature assumptions.