_eigenfoo

I wrote some of my thoughts on the BCS here. tldr: I don’t think BCS outperforms humans.

https://twitter.com/_eigenfoo/status/1528427650187943941

Upon more digging [1], the district court trial is currently scheduled for Feb 27 2023. It'll take a while!

[1] https://storage.courtlistener.com/recap/gov.uscourts.cand.31...

"Web scraping is legal" seems to be an overbroad interpretation - the Ninth Circuit is merely reaffirming a preliminary injunction in light of a recent and separate ruling by the Supreme Court. The opinion [1] only weighs the merits hiQ's request for injunctive relief, and doesn't say anything about the meatier topic of web scraping legality.

I think the real action will be the ruling of the district court.

[1] https://cdn.ca9.uscourts.gov/datastore/opinions/2022/04/18/1...

What do you use the history count for?

Hello, PyMC developer here. We're excited to give Theano a second life, and hope that this work will lend some staying power to the PyMC project in the probabilistic programming world.

As always, we're happy to accept to contributions! If you're looking to get involved, now is a great time. Please don't hesitate to speak up or reach out, either on the Theano-PyMC GitHub repo (https://github.com/pymc-devs/Theano-PyMC) or some other way (my website's in my bio).

Related links:

Video: https://www.youtube.com/watch?v=B4GQCBBsuNU

Migration guide: https://pip.pypa.io/en/latest/user_guide/#changes-to-the-pip...

Sign up for UX studies: https://bit.ly/pip-ux-studies

One major strength of SQL is its readability - it reads so much like English that a non-technical stakeholder could conceivably understand queries. Do you not find that this is a valuable thing that's lost with relational-algebra-esque syntax?

How do you compensate for the fact that you don't know the codebase that well? I feel like a lot of the value of interviews is that you see how a candidate thinks through a problem that you have also thought deeply about, which gives more fruitful discussions.

Can somebody please clarify - what exactly is this an outage of, and how serious is it?

Yes. They're pretty explicit about this:

So long as our Founders [...] meet a minimum ownership threshold on the applicable record date for a vote of the stockholders[...], these Founders will effectively control all matters submitted to a vote of the stockholders for the foreseeable future. This could delay, defer, or prevent a change of control, merger, consolidation, or sale of all or substantially all of our assets that our other stockholders support. Conversely, this concentrated control could allow our Founders to consummate a transaction that our other stockholders do not support.

Have you considered linking notes from one notebook to another? E.g. if you named each notebook and numbered pages, you could just point to "page X in notebook Y", and wouldn't need to transcribe. Would this be something helpful to you?

ProtonMail seems to be on a roll this month. They both released Bridge for Linux [1] and open sourced Bridge [2].

[1] https://protonmail.com/blog/proton-bridge-linux-launch/

[2] https://protonmail.com/blog/bridge-open-source/

Two contributors have announced their departures on the pytest-dev mailing list:

- https://mail.python.org/pipermail/pytest-dev/2020-April/0049...

- https://mail.python.org/pipermail/pytest-dev/2020-April/0049...

I'm not sure why this is circulating HN.

Most of us here are not medical or epidemiological experts at all, and most of the comments here are just people asking for an layman's explanation of this paper. Trying to ascribe meaning to highly technical results seems futile (at best) or irresponsible (at worst).

I've just used alias c=clear in my .bashrc and never looked back. Not even Ctrl+L feels as convenient as c+Enter

In case you still haven't found it, bashlog is here: https://github.com/jodavaho/bashlog

https://eigenfoo.xyz/

I mainly use my blog to motivate me to learn new things in machine learning/data science. It's been a pretty effective tactic!

Not free, but a lot of big corps seem to use Symphony: https://symphony.com/

Relevant comment from the ProtonMail team on Reddit: https://www.reddit.com/r/ProtonMail/comments/d1q7ee/security...

Thank you for pointing this out! This changes how I think about Ecosia.

EDIT: grammar

If you don't click on ads, or have an Adblocker installed, you generate no money for Ecosia. Thus, no trees get planted.

Do you have a source/reference for this?

Bandit algorithms can also be approached from a Bayesian point of view! This lets you quantify the uncertainty in your estimates (e.g. how uncertain you are than ad A has a higher CTR than ad B), which a lot of other bandit methods don't offer.

To toot my own horn a bit, I wrote a blog post about Bayesian bandits: https://eigenfoo.xyz/bayesian-bandits/

Just to throw in my two cents: there's also https://utteranc.es/. It takes a bit more effort to get up and running, but if you're comfortable with your comments being powered by GitHub issues, it's a great way to go!

NMF (and matrix factorization techniques more generally) are a really underrated machine learning technique. If you have a decent intuition for linear algebra, matrix factorizations are straightforward to understand, fairly easy to interpret, and can work wonders in applications far beyond recommender systems.

As the blog post mentions, NMF has a potential applications to text mining, which I've tried out here on Reddit posts: https://eigenfoo.xyz/reddit-clusters/

Yes! I haven't read all of it yet, but from what I've seen so far, the book spends a lot of time rigorously proving mathematical properties/bounds of various bandit algorithms. I love rigor as much as the next guy, but I also like seeing code :)

Incredible! Thanks for sharing the advice :)

Right! Sorry about that, forgot to add mathjax. Should be fixed soon...

If you're looking for just mathematical statistics, I liked Hogg McKean and Craig's "Introduction to Mathematical Statistics" (4th edition was much better than the ones after it, imo).

But if you're looking to learn prob/stats for applications to ML, most ML textbooks have a chapter or two reviewing the relevant stuff. I liked the first two chapters of Bishop's PRML for that.