Andrei KucharavySwiss AI Days 2026 Peer-Reviewed Papers Track Submissions are Open!
And as the publication chair this year, I am really excited about it.
https://ai-days.swiss-ai-center.ch/en/call-for-contributions/papers
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\๏/egI've been tagging my tweets #appliedml rather than #amldgenai23 but same gist 🤗
"Aggressive empiricism in our culture is our enduring advantage" @mosaicml
-- Jonathan Franklin #appliedml
-- Jonathan Franklin #appliedml
However you slice the data, you will always find that accuracy changes, says Kyunghyun Cho, presenting a compelling yet starkly critical view on the idea of generalised prediction models in hospital triage: an area where we can demonstrably save lives and costs to society https://www.nature.com/articles/s41586-023-06160-y #AppliedML #NYUtron
Great kickoff with Prof. Urbanke. Thanks #EPFL for having us here, and keeping the #AppliedML 🇨🇭🤗 events open and affordable!
Glad to be following #AppliedML again today: I will cover the discussion about Generative AI in a blog post later. Check my notes from previous conferences at https://blog.datalets.ch/tag/appliedml/
JP de VooghtMind the predict/fit switcheroo #appliedml #onlinelearning https://maxhalford.github.io/blog/predict-fit-switcheroo/
The online machine learning predict/fit switcheroo • Max Halford
Why I’m writing this Fact: designing open source software is hard. It’s difficult to make design decisions which don’t make any compromises. I like to fall back on Dieter Rams’ 10 principles for good design. I feel like they apply rather well to software design. Especially when said software is open source, due to the many users and the plethora of use cases. I had to make a significant design decision for River.
Roban Hultman KramerAnyway, I keep meaning to write up a blog post on “falsehoods I have believed about measuring model performance” touching on #AppliedML issues related to #modelEvaluation, #metrics, #monitoring, #observability, and #experiments (#RCTs). The cool kids would call this #AIAlignment in their VC pitch decks, but even us #NormCore ML engineers have to wrestle with how to measure and optimize the real-world impact of our models.
You have a problem: you currently pick thresholds for model-based actions using some arbitrary heuristic.
Your solution: pick the threshold that maximizes expected utility (e.g. revenue, profit, ROI, …) instead. That’s the definition of the rational decision, right?
Hmm, for some reason you now seem to have several more problems.
#DecisionTheory #Optimization #rationality #AppliedML
Thies LindenthalUncertainty about the inner workings of #machinelearning models holds back the application of ML-enabled systems in real estate markets. How do ML models arrive at their estimates?
How can practitioners guarantee that ML systems do not run afoul of the law?
Wayne Wan and I show how ML systems can be externally tested with dedicated system tests (as commonly done in software development).
#newpaperalert #appliedML #realestate #proptech #explainableML
https://onlinelibrary.wiley.com/doi/abs/10.1111/1540-6229.12416