And you can interact with this library with a whole ecosystem of clients :
- A web client : directly on bender's website, you can visualize the optimization process on nice graphs; and compare the performances of different models on the same problem with a ranking board that ultimately allows you to pick the best model with the best hyperparameters set.
- A python one, a R one : it allows you to get automatic suggestions of hyperparameters set to test within your code.
Hi! I wrote that post! Another friend pointed out yesterday this other post about Gaussian Processes: https://www.jgoertler.com/visual-exploration-gaussian-proces... I think that post has some fun visualizations for showing how different kernels work, but I tend to prefer my explanation. Would love to get more eyes on it and feedback specifically about whether I have any mistakes in there!
I was frustrated with how difficult I found making animations in matplotlib so I wrote something to make it easy and called it celluloid. I found the idea in plotnine and simply took out the plotnine specific code and generalized it some more (adding support for subplots).
The goal is that your visualization code shouldn't need to modified at all or as little as possible. With celluloid you take "photos" of your visualization to create each frame. Once all the frames have been captured you can create an animation with one call. The readme has more details.
Spatial co-location pattern mining refers to the task of discovering the group of objects or events that co-occur at many places. Extracting these patterns from spatial data is very difficult due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation. We model the co-location pattern discovery as a clique enumeration problem over a neighborhood graph (which is materialized using a distributed graph database). Further, we propose three new traversal based algorithms, namely CliqueEnumG, CliqueEnumK and CliqueExtend. These algorithms allow for a trade-off between time and memory requirements and support interactive data analysis without having to recompute all the intermediate results.
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Hi there -- one of the authors of the paper here. Optimal stopping problems constitute an important class of stochastic control problems, with many real applications, such as pricing financial options. Typically they are solved using approximate dynamic programming methods, which involve coming up with some approximation of the value function or the continuation value function.
In this paper, we take a different approach, where we represent the stopping policy as a tree, and propose a methodology for learning this tree from the data; so in the same way that one comes up with a tree for predicting a binary label in classification, or predicting a continuous value in regression, one obtains a tree that prescribes an action for each possible state. We show using a standard benchmark problem in option pricing that these tree policies perform very well, while being as simple and interpretable as tree models used in other areas of machine learning. We appreciate any questions or comments!
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