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BayesianOptimization Alternatives
Similar projects and alternatives to BayesianOptimization
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Hyperactive
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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nhentai-favorites-auto-pagination
This is an infinity randomly picker doujinshi from yours favorite list with auto scroll and pagination
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Gradient-Free-Optimizers
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
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optimization-tutorial
Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better BayesianOptimization alternative or higher similarity.
BayesianOptimization reviews and mentions
Posts with mentions or reviews of BayesianOptimization.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-26.
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How best to compress a list of objective function evaluations in numerical optimization?
Yes but that’s a pretty broad label- is there a specific implementation you’re working with (for example ) that pinpoints the memory overhead you want to shrink?
- It's so fun and useful to me
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[P] Bonsai: Bayesian Optimization for Gradient Boosted Trees
Sure, I’m only aware of the Bayesian Optimization package (https://github.com/fmfn/BayesianOptimization), but if you can recommend some other GP-based methods that integrate well with Gradient boosted machines, that would be nice.
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How to optimize multiple variables to minimize the output?
I've previously used Bayesian optimisation for this kind of problem, if you're working in python this is a pretty great starting point (https://github.com/fmfn/BayesianOptimization). Black box optimisation is, to the best of my knowledge, a pretty large field and certainly a very difficult problem. You could certainly do a lot worse than BayesOpt.
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Gradient-Free-Optimizers A collection of modern optimization methods in Python
This looks super interesting, I have previously considered using the Bayesian Optimization[0] package for some work, but the ability to switch out the underlying algorithms is appealing.
Perhaps a bit of a far out question - I would be interested in using this for optimizing real-world (ie slow, expensive, noisy) processes. A caveat with this is that the work is done in batches (eg N experiments at a time). Is there a mechanism by which I could feed in my results from previous rounds and have the algorithm suggest the next N configurations that are sufficiently uncorrelated to explore promising space without bunching on top of each-other? My immediate read is that I could use the package to pick the next optimal point, but would then have to lean on a random search for the remainder of the batch?
0: https://github.com/fmfn/BayesianOptimization
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A note from our sponsor - InfluxDB
www.influxdata.com | 25 Apr 2024
Stats
Basic BayesianOptimization repo stats
5
7,481
6.1
24 days ago
bayesian-optimization/BayesianOptimization is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of BayesianOptimization is Python.
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