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Think from an information theory perspective. It is rarely true that you cannot say anything more about the data than what is assumed by classical algorithms. W
by ssivark 2y ago
Think from an information theory perspective. It is rarely true that you cannot say anything more about the data than what is assumed by classical algorithms. We almost always have some more information depending on the specific domain under consideration. Eg: Sorting a list of ages might be very different from sorting a list of account balances.
Any time I have information that reduces the entropy of the dataset, I want to be able to leverage that into runtime improvements of algorithms for pertinent questions. And it would be great to develop a structured framework for that instead of handling special cases in an ad-hoc manner.
- ssivark 2y agoAs one example of such a more general framework -- (variants of) belief propagation might be a good answer if dataset constraints could be cleanly formulated as distributions to be reasoned with.
- oxavier 2y agoMy work is about inferring solutions to Constraint Satisfaction Problem by using belief propagation in the corresponding constraint network, a few keywords caught my eye here :) Do you have any illustrative example so I can understand better what you are hinting at? Cheers