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Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #393 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements [actual post]Thoughts: #392 has the comments, but this is where the magic happens.<a href="https://mastodon.social/tags/modelselection" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelselection</a> <a href="https://mastodon.social/tags/modelcomparison" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelcomparison</a> <a href="https://mastodon.social/tags/variance" class="mention hashtag" rel="nofollow noopener" target="_blank">#variance</a> <a href="https://mastodon.social/tags/effectsize" class="mention hashtag" rel="nofollow noopener" target="_blank">#effectsize</a> <a href="https://mastodon.social/tags/tutorial" class="mention hashtag" rel="nofollow noopener" target="_blank">#tutorial</a><a href="https://www.fharrell.com/post/addvalue/" rel="nofollow noopener" translate="no" target="_blank">https://www.fharrell.com/post/addvalue/</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #392 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements (forum thread)Thoughts: Forums can be great for asking the author for exact answers to complex questions<a href="https://mastodon.social/tags/modelselection" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelselection</a> <a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener" target="_blank">#causalinference</a> <a href="https://mastodon.social/tags/prediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#prediction</a> <a href="https://mastodon.social/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#bias</a> <a href="https://mastodon.social/tags/information" class="mention hashtag" rel="nofollow noopener" target="_blank">#information</a><a href="https://discourse.datamethods.org/t/statistically-efficient-ways-to-quantify-added-predictive-value-of-new-measurements/2013/1" rel="nofollow noopener" translate="no" target="_blank">https://discourse.datamethods.org/t/statistically-efficient-ways-to-quantify-added-predictive-value-of-new-measurements/2013/1</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #358 What are some of the problems with stepwise regression?Thoughts: Model selection is not an easy task, but maybe don't naively try step wise reg.<a href="https://mastodon.social/tags/stepwise" class="mention hashtag" rel="nofollow noopener" target="_blank">#stepwise</a> <a href="https://mastodon.social/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#regression</a> <a href="https://mastodon.social/tags/QRPs" class="mention hashtag" rel="nofollow noopener" target="_blank">#QRPs</a> <a href="https://mastodon.social/tags/issues" class="mention hashtag" rel="nofollow noopener" target="_blank">#issues</a> <a href="https://mastodon.social/tags/phacking" class="mention hashtag" rel="nofollow noopener" target="_blank">#phacking</a> <a href="https://mastodon.social/tags/modelselection" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelselection</a> <a href="https://mastodon.social/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#bias</a><a href="https://www.stata.com/support/faqs/statistics/stepwise-regression-problems/" rel="nofollow noopener" translate="no" target="_blank">https://www.stata.com/support/faqs/statistics/stepwise-regression-problems/</a>

SRF IRISIRIS Insights I Nico Formanek: Are hyperparameters vibes? April 24, 2025, 2:00 p.m. (CEST) Our second IRIS Insights talk will take place with Nico Formanek. 🟦 This talk will discuss the role of hyperparameters in optimization methods for model selection (currently often called ML) from a philosophy of science point of view. Special consideration is given to the question of whether there can be principled ways to fix hyperparameters in a maximally agnostic setting. 🟦 This is a WebEx talk to which everyone who is interested is cordially invited. It will take place in English. Our IRIS speaker, Jun.-Prof. Dr. Maria Wirzberger, will moderate it. Following Nico Formanek's presentation, there will be an opportunity to ask questions. We look forward to active participation. 🟦 Please join this Webex talk using the following link: <a href="https://lnkd.in/eJNiUQKV" rel="nofollow noopener" translate="no" target="_blank">https://lnkd.in/eJNiUQKV</a> 🟦 <a href="https://xn--baw-joa.social/tags/Hyperparameters" class="mention hashtag" rel="nofollow noopener" target="_blank">#Hyperparameters</a> <a href="https://xn--baw-joa.social/tags/ModelSelection" class="mention hashtag" rel="nofollow noopener" target="_blank">#ModelSelection</a> <a href="https://xn--baw-joa.social/tags/Optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#Optimization</a> <a href="https://xn--baw-joa.social/tags/MLMethods" class="mention hashtag" rel="nofollow noopener" target="_blank">#MLMethods</a> <a href="https://xn--baw-joa.social/tags/PhilosophyOfScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#PhilosophyOfScience</a> <a href="https://xn--baw-joa.social/tags/ScientificMethod" class="mention hashtag" rel="nofollow noopener" target="_blank">#ScientificMethod</a> <a href="https://xn--baw-joa.social/tags/AgnosticLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#AgnosticLearning</a> <a href="https://xn--baw-joa.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://xn--baw-joa.social/tags/InterdisciplinaryResearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#InterdisciplinaryResearch</a> <a href="https://xn--baw-joa.social/tags/AIandPhilosophy" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIandPhilosophy</a> <a href="https://xn--baw-joa.social/tags/EthicsInAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#EthicsInAI</a> <a href="https://xn--baw-joa.social/tags/ResponsibleAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#ResponsibleAI</a> <a href="https://xn--baw-joa.social/tags/AITheory" class="mention hashtag" rel="nofollow noopener" target="_blank">#AITheory</a> <a href="https://xn--baw-joa.social/tags/WebTalk" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebTalk</a> <a href="https://xn--baw-joa.social/tags/OnlineLecture" class="mention hashtag" rel="nofollow noopener" target="_blank">#OnlineLecture</a> <a href="https://xn--baw-joa.social/tags/ResearchTalk" class="mention hashtag" rel="nofollow noopener" target="_blank">#ResearchTalk</a> <a href="https://xn--baw-joa.social/tags/ScienceEvents" class="mention hashtag" rel="nofollow noopener" target="_blank">#ScienceEvents</a> <a href="https://xn--baw-joa.social/tags/OpenInvitation" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenInvitation</a> <a href="https://xn--baw-joa.social/tags/AICommunity" class="mention hashtag" rel="nofollow noopener" target="_blank">#AICommunity</a> <a href="https://xn--baw-joa.social/tags/LinkedInScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinkedInScience</a> <a href="https://xn--baw-joa.social/tags/TechPhilosophy" class="mention hashtag" rel="nofollow noopener" target="_blank">#TechPhilosophy</a> <a href="https://xn--baw-joa.social/tags/AIConversations" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIConversations</a>

Nicola RomanòCan anyone help with understanding how to best do <a href="https://qoto.org/tags/modelselection" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelselection</a> in the context of <a href="https://qoto.org/tags/neuralnetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#neuralnetworks</a> ? I'm trying to understand how to reduce <a href="https://qoto.org/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#bias</a> due to the selection of a particular test set.More details here<a href="https://stats.stackexchange.com/q/620547/582" rel="nofollow noopener" target="_blank">https://stats.stackexchange.com/q/620547/582</a>

katch wreckfrom the standpoint of model selection, parsimony often boils down to dimensionality reduction<a href="https://mastodon.social/tags/modelSelection" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelSelection</a> <a href="https://mastodon.social/tags/parsimony" class="mention hashtag" rel="nofollow noopener" target="_blank">#parsimony</a> <a href="https://mastodon.social/tags/OccamsRazor" class="mention hashtag" rel="nofollow noopener" target="_blank">#OccamsRazor</a> <a href="https://mastodon.social/tags/dimensionalityReduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#dimensionalityReduction</a> <a href="https://mastodon.social/tags/degreesOfFreedom" class="mention hashtag" rel="nofollow noopener" target="_blank">#degreesOfFreedom</a> <a href="https://mastodon.social/tags/complexity" class="mention hashtag" rel="nofollow noopener" target="_blank">#complexity</a> <a href="https://mastodon.social/tags/informationTheory" class="mention hashtag" rel="nofollow noopener" target="_blank">#informationTheory</a> <a href="https://mastodon.social/tags/biasVarianceTradeoff" class="mention hashtag" rel="nofollow noopener" target="_blank">#biasVarianceTradeoff</a> <a href="https://mastodon.social/tags/overfitting" class="mention hashtag" rel="nofollow noopener" target="_blank">#overfitting</a> <a href="https://mastodon.social/tags/underfitting" class="mention hashtag" rel="nofollow noopener" target="_blank">#underfitting</a> <a href="https://mastodon.social/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#optimization</a> <a href="https://mastodon.social/tags/parameterTuning" class="mention hashtag" rel="nofollow noopener" target="_blank">#parameterTuning</a> <a href="https://mastodon.social/tags/crossValidation" class="mention hashtag" rel="nofollow noopener" target="_blank">#crossValidation</a> <a href="https://mastodon.social/tags/inverseProblems" class="mention hashtag" rel="nofollow noopener" target="_blank">#inverseProblems</a> <a href="https://mastodon.social/tags/inference" class="mention hashtag" rel="nofollow noopener" target="_blank">#inference</a> <a href="https://mastodon.social/tags/statisticalLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#statisticalLearning</a> <a href="https://mastodon.social/tags/machineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#machineLearning</a> <a href="https://mastodon.social/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> <a href="https://mastodon.social/tags/dataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#dataScience</a> <a href="https://mastodon.social/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#modeling</a> <a href="https://mastodon.social/tags/decisionTheory" class="mention hashtag" rel="nofollow noopener" target="_blank">#decisionTheory</a> <a href="https://mastodon.social/tags/fitting" class="mention hashtag" rel="nofollow noopener" target="_blank">#fitting</a> <a href="https://mastodon.social/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#regression</a> <a href="https://mastodon.social/tags/classification" class="mention hashtag" rel="nofollow noopener" target="_blank">#classification</a> <a href="https://mastodon.social/tags/residualError" class="mention hashtag" rel="nofollow noopener" target="_blank">#residualError</a> <a href="https://mastodon.social/tags/costFunction" class="mention hashtag" rel="nofollow noopener" target="_blank">#costFunction</a> <a href="https://mastodon.social/tags/performanceLoss" class="mention hashtag" rel="nofollow noopener" target="_blank">#performanceLoss</a>

Blake Richards7/10) This finding led to our <a href="https://fediscience.org/tags/proposal" class="mention hashtag" rel="nofollow noopener" target="_blank">#proposal</a>: Can we use α for <a href="https://fediscience.org/tags/modelSelection" class="mention hashtag" rel="nofollow noopener" target="_blank">#modelSelection</a> in an <a href="https://fediscience.org/tags/SSL" class="mention hashtag" rel="nofollow noopener" target="_blank">#SSL</a> pipeline? Two key +s of α:1. α doesn’t require labels2. α is quick to <a href="https://fediscience.org/tags/compute" class="mention hashtag" rel="nofollow noopener" target="_blank">#compute</a> (compared to training a readout)We study hyperparam selection in <a href="https://fediscience.org/tags/BarlowTwins" class="mention hashtag" rel="nofollow noopener" target="_blank">#BarlowTwins</a> (Zbontar et al.) as a case study!<a href="https://fediscience.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://fediscience.org/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> <a href="https://fediscience.org/tags/deeplearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#deeplearning</a> <a href="https://fediscience.org/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#neuroscience</a>

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