After a long and winding road, our work on uncertainty estimation for online regression forests is now published in @JmlrOrg ! Big thanks to my co-authors @gdfm7 and Henrik for sticking through and getting this over the finish line! https://t.co/cb5PQfSI0f

Excited to be speaking at the Stanford Graph Learning workshop about GNNs for Spotify RecSys applications! The event is great with lots of interesting talks! Livestream here:

A summer endeavor, developing MLC: the first open lecture series on ML compilation. Machine learning compilation is an emerging field for systematic optimization and deployment of AI workloads. Hope to share adventures and fun with the community https://t.co/SQ6nKvw0XL 🚀

In our latest episode of “How to Fix the Internet,” comedian and host of @WTFpod @marcmaron and @ProducerMcD tell never-before-shared details of the battle to save podcasting from a patent troll, in a case that EFF took all the way to the Supreme Court.

ICYMI: The best science career advice we heard in 2021 from @alliekmiller, @DalianaLiu, @sneha_rajana, @crislopeslara, @thvasilo, and many others. Read the full article here: https://t.co/eMxk2YGma4

Me on my way to a conference

I'm starting a series of blog posts on "optimization nuggets": short and beautiful proofs in optimization (let me know what you think!). First one shows that SGD converges exponentially fast to a neighborhood of the solution: https://t.co/WFVI5KSiS3

Well this is depressing 🥲

This is a very well-written intro to the topic of automated reasoning, and glad to see the techniques making their way onto real products. Logic geeks will be happy to see this :)

Whoever made this is my new favorite person.

Don't enjoy the doomscroll news on the right on the Twitter website? Current filter to get rid of them (you're already using uBlock Origin right?) https://t.co/LYQnRvSJnm##div.r-1udh08x.r-1ifxtd0.r-rs99b7.r-1phboty.r-1867qdf.r-k0dy70.r-1ysxnx4.css-1dbjc4n:nth-of-type(3)

Join Amazon Scholars Michael I. Jordan, @mkearnsupenn, and Amazon VP and distinguished scientist Bernhard Schölkopf, as they discuss the history of machine learning in the past decade, its social impacts, the role of causal reasoning, and more. Register: https://t.co/Fp9KFBRmrI

"It works on My Machine" - MLOps edition:

Excited to announce that we've finally released @XGBoostProject v1.0.0! This cannot happen without contributions from over 390 contributors and the support from the community. Thank you! #MachineLearning Release notes: https://t.co/JMbbaIwArr GitHub repo: https://t.co/Z0ukD2gsum

Graph neural networks are driving lots of progress in machine learning by extending deep learning approaches to complex graph data and applications. Let’s take a look at a few methods ↓

What if I told you that fine-tuning T5-Large (0.8B params) on a couple hundred examples could outperform GPT-3 (175B params) on a bunch of tasks?

I am excited to share my latest work: 8-bit optimizers – a replacement for regular optimizers. Faster 🚀, 75% less memory 🪶, same performance📈, no hyperparam tuning needed 🔢. 🧵/n Paper: https://t.co/V5tjOmaWvD Library: https://t.co/JAvUk9hrmM Video: https://t.co/TWCNpCtCap

MLOps? more like YAMLOps mirite

Happy to see @ApacheArrow performance and memory use benefits coming to @XGBoostProject

I'm delighted to share that our paper "Fairness in Ranking under Uncertainty" has been accepted to #NeurIPS2021. This is joint work with my advisor Thorsten Joachims and David Kempe from USC. Looking forward to sharing the camera ready version soon! 📄