Zephyr314

12 ms·

61.
by Zephyr314 10y ago
We don't explicitly share information at the experiment level like that for privacy reasons. All user data is explicitly isolated from other users, on top of the fact that due to the black box nature of our optimizer we often do not kn
62.
by Zephyr314 10y ago
We have a free academic tier as well: https://sigopt.com/edu
63.
by Zephyr314 10y ago
Great question. SigOpt is a hosted, scalable ensemble of different Bayesian optimization methods. Many of these methods use Gaussian Processes (GPs) as part of the modeling aspect of their Sequential Model Based Optimization (SMBO). We&#x27
64.
by Zephyr314 10y ago
Thanks for the heads up! We'll make sure to use DataFrames in our next Spark example. As mentioned before, we thought this would be a cool application of Bayesian optimization (especially since the Spark docs advocate the exponentially
65.
by Zephyr314 10y ago
Great question. As you point out, all of these parameters (rank, num iterations and the actual reg. term) can in some way contribute to the regularization of the reconstruction of the ratings matrix. We thought it would be interesting to
66.
by Zephyr314 10y ago
Great questions! The Spark hyperparameter selection uses an exhaustive grid search approach, which can take a long time (complexity grows exponentially with number of parameters to tune) and produce poor results when compared to other metho
67.
by Zephyr314 10y ago
I'm one of the co-founders of SigOpt (YC W15) and am happy to answer any questions about this post (or anything about SigOpt). More info on the methods behind SigOpt can be found at https://sigopt.com/research .
68.
Bayesian Optimization for Collaborative Filtering with MLlib(blog.sigopt.com)
42 points by Zephyr314 10y ago|30 comments
69.
by Zephyr314 10y ago
SigOpt (YC W15) | San Francisco, CA | ONSITE (FULLTIME / INTERNS + VISA OK) We're an optimization platform helping people get the most out of their models, simulations, and industrial processes as efficiently as possible via an en
70.
Breaking Free of the Grid (Bayesian Opt vs. Grid Search, History and Comparisons)(blog.sigopt.com)
1 points by Zephyr314 10y ago|0 comments
71.
by Zephyr314 10y ago
> "The best minds of my generation are thinking about how to make people click ads." There is a strong argument that as long as they continue to publish and produce open source (which they have been doing) then you could think
72.
by Zephyr314 10y ago
Hi, I'm one of the co-founders of SigOpt (YC W15) and am happy to answer any questions about this post or anything about the methods used. More info on the Bayesian optimization behind SigOpt can be found on our research page [1]. The
73.
Deep Neural Network Optimization with SigOpt and Nervana Cloud(blog.sigopt.com)
1 points by Zephyr314 10y ago|1 comments
74.
by Zephyr314 10y ago
I'm one of the co-founders of SigOpt (YC W15) and am happy to answer any questions about this post or the underlying Bayesian optimization methods. For more info about the underlying research check out https://sigopt.com&#x2
75.
Winning on Wall Street: Tuning Trading Models with Bayesian Optimization(blog.sigopt.com)
2 points by Zephyr314 10y ago|1 comments
76.
by Zephyr314 10y ago
There are lots of tools to help with the hyperparameter problem to make that faster/cheaper as well. This problem is often orthogonal to the domain expertise required to do good feature selection. Scikit-learn implements things like gr
77.
by Zephyr314 10y ago
Co-founder of SigOpt (YC W15) here. Happy to answer any questions about this module or the math behind it. More info on our research page [1]. [1]: https://sigopt.com/research
78.
Show HN: One line Bayesian optimization of scikit_learn model hyperparameters(github.com)
4 points by Zephyr314 10y ago|2 comments
79.
by Zephyr314 11y ago
Hi, I'm one of the co-founders of SigOpt (YC W15) here. I'm happy to answer questions about this post or anything about SigOpt. More info on the methods we use can be found on our research page as well [1]. [1]: https:/&#x2F
80.
TensorFlow ConvNets on a Budget with Bayesian Optimization(blog.sigopt.com)
3 points by Zephyr314 11y ago|1 comments
81.
by Zephyr314 11y ago
As you pointed out, it is all about a balance, and every feature has different tradeoffs. SigOpt was designed to unlock the power of Bayesian optimization for anyone doing machine learning. We believe that you shouldn't need to be an e
82.
by Zephyr314 11y ago
This is where the reporting of "failures" as mentioned above can be helpful. Any suggestion that violates your system of equations based constraints can be instantly reported as a "failure," and this will be taken into a
83.
by Zephyr314 11y ago
SigOpt allows specifying constraint ranges for every parameter. If the parameter space isn't a tensor product you can report constraint violating suggestions as failures via our API [1] and SigOpt will take that into account as it opti
84.
by Zephyr314 11y ago
We've found that SigOpt compares very well to spearmint, as well as MOE [1], which I wrote and open sourced around the same time spearmint was open sourced. We have a paper coming out soon comparing SigOpt rigorously to standard method
85.
by Zephyr314 11y ago
We provide a very similar interface to Whetlab and I would be happy to get you set up on SigOpt. We offer a free trial to get started [1] and a free academic plan [2]. [1]: https://sigopt.com/get_started [2]: https:/&
86.
by Zephyr314 11y ago
We were the first company to launch (Whetlab was bought and shut down before they got out of private beta). We're currently the only active company offering it as a service. While hyperparameter optimization is one of the most common u
87.
by Zephyr314 11y ago
Thanks for the note! We're using Bayesian optimization to tune both the hyperparameters of the unsupervised model and the supervised model, but you are correct that they are being done in unison with the overall accuracy being the targ
88.
by Zephyr314 11y ago
One of the co-founders of SigOpt (YC W15) here. I'm happy to answer and questions about this post or the methods used. More info on the Bayesian methods behind this can be found at sigopt.com/research as well!
89.
Unsupervised Learning with Even Less Supervision Using Bayesian Optimization(blog.sigopt.com)
85 points by Zephyr314 11y ago|21 comments
90.
by Zephyr314 11y ago
Great points. Stay tuned for a post sometime soon where we do something similar with Wall Street, where you can build hedging and adversarial trades into the model itself.

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