AutoML.org
@AutoML_org
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research groups on Automated Machine Learning
Freiburg & Hannover & Tübingen
Joined August 2016
AutoML in the weights of a neural network.
This might be the first time after 10 years that boosted trees are not the best default choice when working with data in tables. Instead a pre-trained neural network is, the new TabPFN, as we just published in Nature 🎉
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RT @FrankRHutter: The data science revolution is getting closer. TabPFN v2 is published in Nature: On tabular class….
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RT @SathyaKamesh98: We are elated to introduce our most recent work on time-series foundation models - Mamba4Cast: Efficient Zero-Shot Time….
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RT @gklambauer: Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues. Forget my earlier post, this is the cool one! :). Ana….
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Excited to share our work on this simple yet powerful method for linear RNNs like Mamba or DeltaNet to track states without increasing computational complexity. From @PontilGroup and @FrankRHutter's group.
LLMs can now track states, finally matching this cat!.And we prove it. But how? 🧵👇. 1/ Paper: with @julien_siems @jkhfranke @ZelaArber @FrankRHutter @MPontil
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This work was done jointly by @JakeMRobertson under the supervision of the wonderful @NoorAwad and @FrankRHutter. Link to paper: Blog post: #AI #FairnessInAI #MachineLearning #EthicsInAI #ACM #ManyFairHPO.
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ManyFairHPO helps practitioners navigate fairness metric trade-offs, assess conflicts, and make context-aware model selections amidst nuanced fairness dilemmas. Join us for an engaging discussion on October 22nd at the AAAI/ACM AI Ethics and Society Conference in San Jose!.
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When it comes to fairness in AI, "fair" can mean different things to different people. We saw this challenge as an opportunity to innovate!. 🌟 Introducing ManyFairHPO: a human-in-the-loop optimization framework!.
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RT @SamuelMullr: Transformers perform remarkable generalizations in the in-context learning setting. E.g. when trained only on step functio….
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RT @CarolaDoerr19: What a great pleasure it was to host the @automl_conf here in Paris this week. Big shoutout to all co-organizers and in….
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RT @BBischl: We have another opening for a PhD in AutoML @LMU_Muenchen. Apply now at #PhD #MachineLearning #Statist….
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RT @automl_conf: In less than a month, the AutoML Conference 2024 will be in Paris. I don't think that we can quite compete with the #Olymp….
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Let's make ML fairer for everyone! 🤖⚖️.Link to paper: @JakeMRobertson @noahholl @__NoorAwad__ @FrankRHutter. #AI #Fairness #CausalInference #Transformers #MLResearch.
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Our experiments show some promising results, paving the way transformers in causal and counterfactual fairness with exciting applications to law, healthcare, and finance, as well as extensions to fairness pre-processing, path-specific effects, and multi-objective optimization.
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Drawing from recent advances in in-context-learning (ICL) and prior-fitted networks (PFNs), FairPFN removes the causal effects of protected attributes directly from observational data.
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🌟 Excited to share our latest work on counterfactual fairness in #MachineLearning at the ICML Workshop on Next Gen. AI Safety in Vienna 🌟. We introduce FairPFN, a transformer trained on synthetic data to remove gender, age, and racial bias in -sensitive real-world ML problems.
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RT @neeratyoy: In #Vienna for #ICML2024 to present our latest work!.With @_herilalaina_, @StAdriaensen, @karibbov, @Edberg_Wardman, and @Fr….
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RT @__mfeurer__: Joint work with @LindauerMarius, Florian Karl, Julia Moosbauer, @ATornede, @amuellerml, @FrankRHutter and @BBischl.
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RT @__mfeurer__: 📝In the meantime you can also read a short summary blog post at or watch a video summary at https:….
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RT @__mfeurer__: Wondering how humans should be involved in designing #AutoML solutions 🤔? Check out our #ICML2024 paper: "Position: A Call….
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