[FRIAM] Weighted Ensemble

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In my ignorance, I've thought of weighted ensemble (WE) as a specific kind of novelty search. E.g. weighting toward trajectories that exhibit anomalies. Is that what you mean by it?

Also, for each of the 5 you're interested in, do you have convenient example cites for each/any of them? In particular, (2) and (3)? Or are these just ideas of places where you think WE should apply?

For my part, no. I haven't used WE in particular. I have a friend who's worked on identifying mechanical anomalies from audio (recordings of machines as they hum). He may have used it. I'll ask. 

On 8/29/21 1:07 PM, Jon Zingale wrote:
> I am presently working on learning weighted ensemble <https://arxiv.org/pdf/1906.00856.pdf> sampling techniques and was curious if any here have worked with them before. The technique seems promising and has enjoyed quite a bit of success (even above MCMC <https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo>) in circles concerned with reaction rates for rare events.
> 
> Some points of interest for me include:
> 
>  1. A better sampling of fringe-outlier works/art from streaming services.
>  2. An alternative (bin-based sampling) to globally defined "fitness" measures in evolutionary modeling.
>  3. An application of diffusion-limited aggregation to general search (especially in the face of limited resources)
>  4. An application of linear logic to optimization problems in conformation prediction <https://en.wikipedia.org/wiki/Protein_structure_prediction>.
>  5. Investigation of dynamical properties, such as distribution of trajectories with "high winding number", on strange attractors.
> 
> 
> While I am just beginning to grok the technique, I thought it might be fruitful to ask here.
> 
> Jon


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