Research map / Learning theory and algorithms / Combinatorial and search optimization
A Bandit Approach to Maximum Inner Product Search
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BoundedME with finite-list sampling without replacement instead of state-of-the-art MIPS methods | MIPS · Synthetic and real-world datasets | query speedup: 5--10× faster | Helped |
| Use BoundedME rather than LSH-MIPS, Greedy-MIPS, and PCA-MIPS | MIPS · Synthetic datasets | precision | Helped |
| Use BoundedME rather than the compared MIPS methods | MIPS · Netflix and Yahoo-Music | precision | Helped |
Closest papers by meaning
- Faster Maximum Inner Product Search in High Dimensions (ICML 2024 · similarity 0.92)
- Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits (NeurIPS 2024 · similarity 0.86)
- Balancing Performance and Costs in Best Arm Identification (NeurIPS 2025 · similarity 0.86)
- Near Optimal Best Arm Identification for Clustered Bandits (ICML 2025 · similarity 0.86)
- New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem (AAAI 2024 · similarity 0.85)
- The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits (NeurIPS 2026 · 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.