Summary of Cost-aware Simulation-based Inference, by Ayush Bharti et al.
Cost-aware simulation-based inference
by Ayush Bharti, Daolang Huang, Samuel Kaski, François-Xavier Briol
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for estimating parameters of complex models in science and engineering, called simulation-based inference (SBI), has been improved upon by introducing cost-aware methods that reduce computational costs. Specifically, neural SBI and approximate Bayesian computation techniques are optimized through a combination of rejection and self-normalized importance sampling. This approach leads to significant reductions in the number of expensive simulations required, making it more feasible for real-world applications. The proposed method is demonstrated on various models from epidemiology to telecommunications engineering, showcasing its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand complex systems like how diseases spread or how phone networks work. To do this, scientists and engineers use a technique called simulation-based inference (SBI). But SBI can be very slow because it needs to simulate many scenarios to get the right answers. Our team has developed new methods that make SBI faster by being smart about which simulations to run. We tested these methods on different models and found they work well for understanding systems like disease outbreaks or phone networks. |
Keywords
» Artificial intelligence » Inference
