We have developed some relatively general methods for mechanistic estimation competitive with sampling by studying problems that are expressible as expectations of random products. This includes several different estimation problems, such as random halfspace intersections, random #3-SAT and random permanents. In this post, we will give a high-level introduction to these methods before sharing some more detailed notes. This is intended as an interim technical update and will be relatively light on motivation: for a broader discussion of this line of research, see our prior post.

Random instances of the matching sampling principle

All of the problems discussed in this post can be thought of particular choices of "architecture" in our matching sampling principle. In fact, they are all choices in which has no learned or worst-case parameters . They still have random parameters, which are captured in the "context" variable , making them similar to randomly-initialized networks rather than trained networks. Note that when is missing from , no "explanation" is required by the estimation algorithm , which reflects the fact that there is no "structure" in a randomly-initialized network that needs to be pointed out.

Why study random instances of the matching sampling principle? [...]

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Outline:

(00:46) Random instances of the matching sampling principle

(02:02) Expectations of random products

(03:13) Deduction-projection estimators

(04:59) Mechanistic sketching

(06:03) Detailed notes

(08:36) Conclusion

The original text contained 4 footnotes which were omitted from this narration.

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Narrated by TYPE III AUDIO.