Research map / Learning theory and algorithms / Submodular and game algorithms
Composable Coresets for Determinant Maximization: Greedy is Almost Optimal
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
- Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm (ICML 2019 · similarity 0.95)
- Faster Greedy MAP Inference for Determinantal Point Processes (ICML 2017 · similarity 0.89)
- Lazy and Fast Greedy MAP Inference for Determinantal Point Process (NeurIPS 2022 · similarity 0.89)
- Small coresets via negative dependence: DPPs, linear statistics, and concentration (NeurIPS 2024 · similarity 0.89)
- Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling (NeurIPS 2026 · similarity 0.87)
- State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives (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.
