mccourt

7 ms·

1.
by mccourt 5y ago
This is a cool post, and I really appreciate the link/references to other content. At one point I played around with parametrizing procedural generation of mazes to produce a desired length/complexity balance ( https://
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by mccourt 5y ago
You can really do training in just hours? I didn't realize how efficient current systems are for this.
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by mccourt 6y ago
This blog post lists a bunch of gradient-free optimization packages, some genetic and some Bayesian: https://sigopt.com/blog/comparison-bayesian-packages-hyperpa... . Nothing from the mathematical programming community
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by mccourt 6y ago
Great example of the fact that different circumstances require different approaches, and the fact that brute force is increasingly impractical for combinatorially complex problems. Thanks for this reference.
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by mccourt 6y ago
A very reasonable point and, certainly, the direction that parts of the computational community have embraced over the years. I will use integration as an example: classic computational methods were focused on trying to make strong assumpt
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NeurIPS 2020 Optimization Competition(bbochallenge.com)
77 points by mccourt 6y ago|23 comments
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by mccourt 7y ago
I always appreciate articles emphasizing the importance of hyperparameter optimization; thank you for writing this. The discussion on learning rate is nice additional point to mention, though I find it a bit misleading -- earlier in the di
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by mccourt 8y ago
This is a good article, but I wish it went a bit further into the complexities facing Hong Kong as it seeks its place in a 21st century where access to Chinese investments is no longer throttled by access to Hong Kong. I first lived in Hon
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Computational and Data Science Fellowships (ACM SIGHPC)(sighpc.org)
1 points by mccourt 9y ago|0 comments
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How Many Dimensions Is High Dimensions?(sinews.siam.org)
1 points by mccourt 10y ago|0 comments
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by mccourt 10y ago
Bayesian optimization, I am familiar with, but Q-learning, not so much. If anyone has good references on or introductions to Q-learning I would appreciate it.
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Q-learning plus Bayesian optimization for better learning(blog.sigopt.com)
4 points by mccourt 10y ago|1 comments
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by mccourt 10y ago
Yeah, I think that's probably the split - folks from computer science/discrete math on one side and folks from engineering on the other. I grew up in math, but I was on the numerical analysis side so I definitely ended up on the
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by mccourt 10y ago
Do you have a reference for fitting matrix-valued time series with nonlinear criteria? I'm familiar with the standard Box-Jenkins methods but I usually see that done with linear least-squares methods. I'd love to up my game on t
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by mccourt 10y ago
I'd also like to throw in some work by a former colleague of mine at Argonne, Sven Leyffer on nonlinear programming: - A compendium he co-edited named (appropriately enough) Mixed Integer Nonlinear Programming - A review paper he co-au
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by mccourt 10y ago
And I can absolutely agree that, as more criteria arise, the mechanism for linear scalarization probably becomes more fragile (subject to inconsistent behavior from the coefficients). As a result, something less sensitive but more robust,
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by mccourt 10y ago
Good call - if the problem is well behaved then small changes in gamma should be able to use the previous solution as an initial guess. And I absolutely agree with the robustness idea you're talking about; I was hinting at it when I w
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by mccourt 10y ago
That's very helpful. Thanks a lot!
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by mccourt 10y ago
That strategy can be viable; it's discussed in the Wikipedia article: https://en.wikipedia.org/wiki/Multi-objective_optimization#N... As is suggested there, though, implementing this no-preference strategy require
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by mccourt 10y ago
Good call. That's a simpler example than where I was going.
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by mccourt 10y ago
You make a perfectly accurate point that, in practice, it is unlikely one would be able to make such a prediction without significant info about the model. The above comment was meant in more of a post hoc "we've executed our mul
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by mccourt 10y ago
Our customers who are working with multicriteria problems have, thus far, had primarily two criteria, thus we have been helping them manage their two criteria problems into a scalar setting. As such, we do not, at this moment, permit the l
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by mccourt 10y ago
You are absolutely correct that some sort of lexicographic ordering could exist: https://en.wikipedia.org/wiki/Lexicographical_order#Finite_s... If such an ordering did exist, then we could certainly apply that orderin
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by mccourt 10y ago
First off, I am very hesitant to say anything about biconvex problems - I only see them in passing and they are definitely not in my wheelhouse. If anyone out there is an expert, or even just has a solid (basic) reference on biconvex probl
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by mccourt 10y ago
I wrote this post and am also happy to comment. Hopefully we'll be following this up soon with a post on treating robustness and cost simultaneously in a multicriteria setting. Also, special thanks again to Devon Sigler at the Univer
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by mccourt 10y ago
There's a couple different points there, so lemme see if I can answer each of them. First off, the use of the term "kernel trick" appears, I think, primarily within the machine learning community. It refers to the idea that
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by mccourt 10y ago
Dear apathy (love the name), I wrote that post and am really glad that you liked it. I had another more recent one that focused on a different topic but had a solid paragraph right at the beginning that also stepped through some of the his
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by mccourt 10y ago
I have been waiting for someone to do this analysis; I had been thinking for a while that there was something more "boring" about his iPhone tweets. Thanks!
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by mccourt 10y ago
Great article and great paper. Similar set of questions that get asked as part of AutoML, including the idea of hyper-heuristics, but in the AI community instead of data science. Thanks for the insights.
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by mccourt 10y ago
That's very helpful. Thanks for the link.

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