Summary of Generalizing Stochastic Smoothing For Differentiation and Gradient Estimation, by Felix Petersen et al.
Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation
by Felix Petersen, Christian Borgelt, Aashwin Mishra, Stefano Ermon
First submitted to arxiv on: 10 Oct 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 This paper proposes a novel approach to estimating gradients for stochastic relaxations of non-differentiable functions. The conventional method uses smoothing with a differentiable density distribution and full support, but this framework requires reduced assumptions, eliminating the need for these conditions. A general framework is developed for relaxation and gradient estimation of black-box functions. The authors also present variance reduction techniques from three orthogonal perspectives, demonstrating their effectiveness through empirical benchmarks on various distributions and strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to estimate gradients when we can’t directly measure them. Imagine trying to find the steepness of a mountain by throwing balls down it and measuring where they land. This method works for some problems but not others. The authors create a new way to smooth out these problems, making it possible to estimate gradients without needing perfect information about every little detail. They test their approach on different tasks like sorting, finding the shortest path, and even simulating how tiny particles move. |
