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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The data science revolution is getting closer. TabPFN v2 is published in Nature: https://t.co/Ybb15pnZ5P On tabular classification with up to 10k data points & 500 features, in 2.8s TabPFN on average outperforms all other methods, even when tuning them for up to 4 hours🧵1/19
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We are elated to introduce our most recent work on time-series foundation models - Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models. Authors: @o_swelam, Sathya Kamesh, @julien_siems, David Salinas, @FrankRHutter Link: https://t.co/1JkVRqm7VS
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Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues Forget my earlier post, this is the cool one! :) Analysis of STATE TRACKING capabilities of "linear RNNs" (GLA, MAMBA, mLSTM). P: https://t.co/xdcOnUrKjE
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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: https://t.co/aKvrqYtkWh 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: https://t.co/VFZwPQGdYs Blog post: https://t.co/7SmTwse0Jq
#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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Transformers perform remarkable generalizations in the in-context learning setting. E.g. when trained only on step functions, the model generalizes to smooth predictions when given a smooth input. (1/n, a paper thread)
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What a great pleasure it was to host the @automl_conf here in Paris this week. Big shoutout to all co-organizers and in particular to the amazing @ElenaRaponi_ @anjajankovic Simon Provost and to the online chairs Gabi Kadlecová and @AndreBiedenkapp See you in NYC next year 😃
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We have another opening for a PhD in AutoML @LMU_Muenchen. Apply now at https://t.co/3YJrnAAlil
#PhD #MachineLearning #Statistics #DataScience #AutoML
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In less than a month, the AutoML Conference 2024 will be in Paris. I don't think that we can quite compete with the #Olympics, but we will give our best ;-)
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Let's make ML fairer for everyone! 🤖⚖️ Link to paper: https://t.co/5xRM00P3bN
@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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In #Vienna for #ICML2024 to present our latest work! With @_herilalaina_, @StAdriaensen, @karibbov, @Edberg_Wardman, and @FrankRHutter, we introduce, ifBO: In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization.
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Joint work with @LindauerMarius, Florian Karl, Julia Moosbauer, @ATornede, @amuellerml, @FrankRHutter and @BBischl
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📝In the meantime you can also read a short summary blog post at https://t.co/KJfFpYBQMF or watch a video summary at https://t.co/qXkyPPcql2 🎥 2/3
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Wondering how humans should be involved in designing #AutoML solutions 🤔? Check out our #ICML2024 paper: "Position: A Call to Action for a Human-Centered AutoML Paradigm"! 📄✨ https://t.co/TmB1p7HIhw Drop by at our poster on Thu, Jul 25 at 11:30 AM in Hall C 4-9 #2003 📅 1/3
proceedings.mlr.press
Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) workflows...
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