Research map / Learning theory and algorithms / Prediction and decision losses
Online MAP Inference of Determinantal Point Processes
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
- Faster Greedy MAP Inference for Determinantal Point Processes (ICML 2017 · similarity 0.91)
- One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes (ICML 2022 · similarity 0.91)
- Lazy and Fast Greedy MAP Inference for Determinantal Point Process (NeurIPS 2022 · similarity 0.88)
- Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes (ICLR 2021 · similarity 0.88)
- State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives (NeurIPS 2026 · similarity 0.85)
- Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project (NeurIPS 2025 · similarity 0.84)
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.
