Research map / Bandits and online learning / RL theory and MDPs
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery (ICML 2024 · similarity 0.91)
- Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation (NeurIPS 2024 · similarity 0.91)
- Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits (ICML 2024 · similarity 0.90)
- Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning (NeurIPS 2025 · similarity 0.89)
- Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems (NeurIPS 2022 · similarity 0.89)
- The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure (NeurIPS 2024 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.