Recent searches

No recent searches

Search options

Only available when logged in.
mastodon.world is one of the many independent Mastodon servers you can use to participate in the fediverse.
Generic Mastodon server for anyone to use.

Administered by:

Server stats:

8.4K
active users

mastodon.world: AboutStatusProfiles directoryPrivacy policy

Mastodon: AboutGet the appKeyboard shortcutsView source codev4.4.0

#conformalprediction

0 posts0 participants0 posts today
Dr Mircea Zloteanu ☀️ 🌊🌴<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statstab</span></a> #357 Uncertainty Estimation with Conformal Prediction</p><p>Thoughts: Haven't parsed this properly but maybe be an interesting discussion point. How best to quantify uncertainty?</p><p><a href="https://mastodon.social/tags/conformalprediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>conformalprediction</span></a> <a href="https://mastodon.social/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> <a href="https://mastodon.social/tags/confidenceintervals" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>confidenceintervals</span></a> <a href="https://mastodon.social/tags/uncertainty" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>uncertainty</span></a></p><p><a href="https://m-clark.github.io/posts/2025-06-01-conformal/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">m-clark.github.io/posts/2025-0</span><span class="invisible">6-01-conformal/</span></a></p>
Dr Mircea Zloteanu ☀️ 🌊🌴<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statstab</span></a> #223 Conformal predictions w/ {marginaleffects}</p><p>Thoughts: Sometimes you need a range of likely future values. To get an assumption-free Prediction Interval, use conformal methods.</p><p><a href="https://mastodon.social/tags/r" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>r</span></a> <a href="https://mastodon.social/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://mastodon.social/tags/marginaleffects" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>marginaleffects</span></a> <a href="https://mastodon.social/tags/prediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>prediction</span></a> <a href="https://mastodon.social/tags/conformalprediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>conformalprediction</span></a></p><p><a href="https://marginaleffects.com/bonus/conformal.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">marginaleffects.com/bonus/conf</span><span class="invisible">ormal.html</span></a></p>
robjhyndman<p>Some thoughts on <a href="https://aus.social/tags/conformalprediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>conformalprediction</span></a><br> for <a href="https://aus.social/tags/timeseries" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>timeseries</span></a> with Xiaoqian Wang <a href="https://robjhyndman.com/publications/cpts.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">robjhyndman.com/publications/c</span><span class="invisible">pts.html</span></a></p>
draxus<p>In the last couple of weeks I've been learning about <a href="https://mastodon.social/tags/ConformalPrediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ConformalPrediction</span></a>, a family of algorithms to measure the uncertainty of predictions made by <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> models.</p><p>Here are a few links to get you started:<br>- CP course by <span class="h-card" translate="no"><a href="https://sigmoid.social/@ChristophMolnar" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>ChristophMolnar</span></a></span> <a href="https://mindfulmodeler.substack.com/p/week-1-getting-started-with-conformal" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">mindfulmodeler.substack.com/p/</span><span class="invisible">week-1-getting-started-with-conformal</span></a><br>- Multi-class notebook (in Spanish) <a href="https://nbviewer.org/github/MMdeCastro/Uncertainty_Quantification_XAI/blob/main/UQ_multiclass.ipynb" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">nbviewer.org/github/MMdeCastro</span><span class="invisible">/Uncertainty_Quantification_XAI/blob/main/UQ_multiclass.ipynb</span></a><br>- MAPIE library: <a href="https://mapie.readthedocs.io/en/latest/index.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">mapie.readthedocs.io/en/latest</span><span class="invisible">/index.html</span></a><br>- TorchCP library: <a href="https://github.com/ml-stat-Sustech/TorchCP" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/ml-stat-Sustech/Tor</span><span class="invisible">chCP</span></a></p>
Kyle Taylor<p>Making the rounds again... </p><p>...Blackbox <a href="https://hostux.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures... <a href="https://hostux.social/tags/ConformalPrediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ConformalPrediction</span></a> is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models... </p><p>[1] <a href="https://arxiv.org/abs/2107.07511" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2107.07511</span><span class="invisible"></span></a><br>[2] <a href="https://arxiv.org/abs/2106.06137" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2106.06137</span><span class="invisible"></span></a></p>
PyData Madrid<p>Nos vemos *hoy* en nuestra reunión de marzo: ⏩ Analítica acelerada con Shapelets y conformal prediction, este mes en The Bridge</p><p><a href="https://www.meetup.com/pydata-madrid/events/299749589/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">meetup.com/pydata-madrid/event</span><span class="invisible">s/299749589/</span></a></p><p>¡Te esperamos a las 19:00! Y después, networking 🗣️</p><p><a href="https://masto.ai/tags/PyDataMadrid" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyDataMadrid</span></a> <a href="https://masto.ai/tags/PyData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyData</span></a> <a href="https://masto.ai/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://masto.ai/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://masto.ai/tags/ConformalPrediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ConformalPrediction</span></a> <a href="https://masto.ai/tags/shapelets" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>shapelets</span></a></p>
Cheng Soon Ong<p>Why perform&nbsp;cross validation (CV) in <a href="https://masto.ai/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a>? To estimate the generalization error of a trained predictor. This paper uses the idea of a <a href="https://masto.ai/tags/ProperLoss" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ProperLoss</span></a> (called Q-class). Then it covers CV, bootstrap, and Mallow's covariance penalties. It also covers <a href="https://masto.ai/tags/ConformalPrediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ConformalPrediction</span></a>, which is newly popular because of Emanuel Candes' keynote at <a href="https://masto.ai/tags/NeurIPS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeurIPS</span></a> 2022<br><a href="https://doi.org/10.3390/stats4040063" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.3390/stats4040063</span><span class="invisible"></span></a><br>The paper is also a good advertisement for Efron and Hastie's recent book.</p>
Cedric Archambeau<p>Today, we open sourced Fortuna (<a href="https://github.com/awslabs/fortuna" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">github.com/awslabs/fortuna</span><span class="invisible"></span></a>) a library for uncertainty quantification.<br>Deep neural networks are often overconfident and do not know what they don’t know. Quantifying the uncertainty in the predictions they make will help deploy deep learning more responsibly and more safely.<br><a href="https://sigmoid.social/tags/responsibleAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>responsibleAI</span></a> <a href="https://sigmoid.social/tags/ConformalPrediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ConformalPrediction</span></a> <a href="https://sigmoid.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://sigmoid.social/tags/UncertaintyQuantification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>UncertaintyQuantification</span></a> <a href="https://sigmoid.social/tags/deeplearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>deeplearning</span></a> <a href="https://sigmoid.social/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a></p>
TrendingLive feeds
About