Viewing profile — Zephyr314

Do you guys have examples of people actually using this in production? I'm curious how it scales beyond dev.

Very cool to see what used to take a team years to build in a simple, intuitive OSS package. Getting a stack like this up and running in 20 lines of python out of the box would hav…

Hi, I am one of the founders of SigOpt (acquired by Intel in 2020) and I am happy to answer any questions people may have! You can also jump right to the code here: https://github.…

Hi, I'm one of the founders of SigOpt (Scott Clark) and still working with the team after our acquisition by Intel in 2020. I am happy to answer questions. I am incredibly proud th…

There are also several papers and blog posts diving into details and tradeoffs of different Bayesian optimization approaches and components here [0]. Example: Covariance Kernels fo…

This NVIDIA post goes into extending Bayesian Optimization to multiple metrics [0]. It shows how you can use efficient optimization to find a good Pareto Frontier[1]. [0]: https://…

hyperopt also uses TPEs [0], this may be a variant/fork of that. [0]: http://hyperopt.github.io/hyperopt/

This can be a very difficult market, but there are a handful of different projects designed to help with this. In-Q-Tel [0] has been accelerating adoption of new technology for the…

Which in this case nets you >$1M [0] vs the 80k quoted above. This also assumes no retention grants over 6 rounds. [0]: 0.02*0.8^6*$200,000,000 = $1,048,576

I agree that this isn't as common for most end-to-end "AutoML" systems that take a CSV, do light feature engineering/combinations, pipe it into a random forest / GBDT, and then out…

To be fair, the hyperparameter tuning behind these AutoML systems are getting fairly robust. Google bases theirs on Vizier [0]. The Amazon Sagemaker group has people from the gpyop…

At SigOpt (YC W15, disclaimer: I'm a founder) we use GPs among other Bayesian and global optimization techniques for black box parameter optimization. In our blog we go over GP int…

This is close to how Bayesian optimization usually works, except you can sample many functions from the GP at once and apply different "acquisition functions" instead of just looki…

While a lot of Bayesian optimization methods use GPs (MOE, Spearmint, BayesOpt, etc) some use TPEs as well (most notably, hyperopt [0]), and some ensemble these methods and others …

For graduate students out there that would rather be doing research than "graduate student gradient descent" (or, high dimensional, non-convex optimization in your head), SigOpt (Y…

SigOpt (YC W15) has an academic program that allows full access to their optimization platform for academic use [1]. Hundreds of academics around the world have used it and there a…

It would be interesting to see what would happen if you also tried to tune the ensemble towards a specific task in the same way that you could tune a single model. We've definitely…

General employee lockup [1] ends at 6 months usually. A tank right before/at that time could signal what the insiders think and the long term prospects in general. [1]: https://www…

Hi, I'm Scott Clark, co-founder of SigOpt (YC W15). We provide hyperparameter optimization as a service. We have some references to recent articles we've presented at NIPS, ICML, a…

Hi, I'm one of the founders of SigOpt (YC W15). This is part 3 of a 3 part series we've done on uncertainty in modeling and optimization (Part 1 and 2 here [0] and here [1]). Let m…