This is our first post to reddit from World Wide Technology’s Business and Analytics Advisors team, and this thread seemed like the right place to start. We would love to get everyone’s thoughts on our AI R&D program we started about a year and half ago, in particular the white papers we have been publishing on our website. Our data science and engineering teams have been working diligently on topics we know our customers are only thinking about now (or will be thinking about soon), and will probably be a reality in 3-5 years. We want to stay ahead of the market and make sure we are learning and sharing our findings across a number of areas. This is a blog post talking about why we are doing R&D and at the bottom are links to the abstracts of the 6 papers we have published so far. Please kindly register for the website to download the papers. We promise we won’t bombard you with spam!

We look forward to your thoughts!

https://www.wwt.com/all-blog/uncovering-ai-research-development/

I have seen that for xgboost you can write your own loss function, and have even seen the example on the xgboost github. What I am wondering is there an example somewhere about how to go about developing that code?

In other words, say I have some metric (other than say squared loss) that I want my model to optimize and use to determine the weights. It looks like I need to determine the gradient and hessian, but I’m not sure how to figure this out.

We recently released a preprint analyzing policy iteration on LQR. Here’s the arxiv paper: https://arxiv.org/abs/1905.12842

And a tl;dr explanation on Twitter: https://twitter.com/Krauth/status/1134630118930997249

Hi, I am using XGboost regressor for a personal project. Initially I used a data set with measurements from 01.Jan.2016 to 24.Dec.2018 and I got those results : MAE = 2.332 , MSE = 7.764 for testing data. I recently got from the same source, the same data set but with measurements from 01.Jan.2016 up to 14.May.2019 and for testing data I got those results : MAE = 2.729 , MSE = 12.002. I have tuned the hyperparameters, in both cases, using the same method through cv. I tried to adjust the parameters more for the second data set but I did not get better results. Even if the differences are not very high, the fact that I used a larger data set could have affected the performance or is something I have overlooked?

Code on GitHub: https://github.com/qubvel/efficientnet

I’ve been assigned by my company to interview some ML research & engineer candidates. However, I myself am not an ML expert, although I’m fairly familiar with ML at the “hobbyist” level. For instance, I’ve implemented RL algorithms in Pytorch and I’m comfortable reading many ML papers, but I’m still struggling to understand Transformer. Outside of ML I would consider myself fairly strong in C++/Python, probably more so than the average ML candidate.

How can a non-ML-expert effectively interview ML candidates? What questions would you ask? If you were applying, what questions would you want someone who isn’t an ML expert to ask you?

I was recently reading this paper:

​

Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning

​

First thing I wanted to discuss was the use of the term control. They had 3 “control groups” (OSI, Depressed, General) and they had a group of cases. In terms of model construction and validation they say:

​

A total of 1,470 adolescents with ICD codes for suicide and self-inflicted injury (i.e. E950–E959) were identified for model development and validation.

​

So to my understanding, they built the model using cases and OSI, then when testing discrimination, they mix in the cases with the control group of interest to see how well they can identify them? Or based on the general aim of their paper, are they trying to detect suicide attempts within the controls. I don’t necessarily see this because they don’t state how many true cases are within each group. I’m used to the use of controls in terms of measuring an effect of a treatment and less so in terms of prediction models (I’m more used to training vs testing datasets or people not even doing that).

​

Another confusing issue, was the fact that they used their control (OSI) as part of the model development and validation. Generally speaking, shouldn’t it be separate (i.e. you have training and testing). I was also confused by their Figures 4A-B in terms of “depressed control comparison” (4A) vs “general control comparison” (4B). I thought the only thing being compared is cases to controls.

​

Finally, I’m not sure how much of a concern this is, but I found it interesting (see charts) that BMI and Age were generally some of the best predictors. In a sense, while the algorithms don’t care and will just choose what best discriminates, I find it concerning that these kind of superficial predictors, which in some ways (more so BMI than age) have little to do with suicide attempts, are considered the most powerful. I am not sure if this kind of information implies anything about the generalizability of the model where one wouldn’t be able to find such systematic difference in terms of BMI and Age.

This is a place to share machine learning research papers, journals, and articles that you’re reading this week. If it relates to what you’re researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you’ve read.

Please try to provide some insight from your understanding and please don’t post things which are present in wiki.

Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links.

Previous weeks :

Most upvoted papers two weeks ago:

/u/mowrilow: This website

Besides that, there are no rules, have fun.

Hello.

This is project a guideline for basic use and installation of kubeflow in AWS.

Kubeflow is a Cloud Native platform for machine learning based on Google’s internal machine learning pipelines to ml-serving, Devops, distributed training, etc.

Thanks

​

https://github.com/graykode/aws-kubeflow

https://i.imgur.com/7lCmYQt.jpg

https://i.imgur.com/KSSVkGT.jpg

This popped up on my feed this morning and I thought it was interesting/horrifying.