Summary of Active Sequential Posterior Estimation For Sample-efficient Simulation-based Inference, by Sam Griesemer et al.
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
by Sam Griesemer, Defu Cao, Zijun Cui, Carolina Osorio, Yan Liu
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed active sequential neural posterior estimation (ASNPE) approach tackles the challenges of systematically performing inference under simulation models. By integrating an active learning scheme into existing posterior estimation pipelines, ASNPE improves sample efficiency with low computational overhead. This method outperforms well-tuned benchmarks and state-of-the-art posterior estimation methods on large-scale real-world traffic networks and various simulation-based inference (SBI) benchmark environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ASNPE is a new way to do computer simulations that helps us get better results by asking the right questions. It’s like solving a puzzle, but instead of pieces, we use lots of little bits of information from many different places. This makes it easier and faster to figure out what’s going on in complex systems like traffic flow. The idea is simple: instead of just guessing or using old data, ASNPE lets us choose which pieces of information are most important for solving the puzzle. This helps us get better answers and use our computers more efficiently. |
Keywords
» Artificial intelligence » Active learning » Inference
