#conformalprediction — Public Fediverse posts

Internship / Ph.D. proposal (w. J-B Fermanian):

"Exploring Conformal Prediction in Long-Tail Scenarios"

Come and work on @plantnet.bsky.social data with us!

http://josephsalmon.eu/perso/internship_proposal2026.pdf

#Machinelearning
#ConformalPrediction
#CitizenScience

Internship / Ph.D. proposal (w. J-B Fermanian):

"Exploring Conformal Prediction in Long-Tail Scenarios"

Come and work on @plantnet.bsky.social data with us!

http://josephsalmon.eu/perso/internship_proposal2026.pdf

#Machinelearning
#ConformalPrediction
#CitizenScience

Internship / Ph.D. proposal (w. J-B Fermanian):

"Exploring Conformal Prediction in Long-Tail Scenarios"

Come and work on @plantnet.bsky.social data with us!

http://josephsalmon.eu/perso/internship_proposal2026.pdf

#Machinelearning
#ConformalPrediction
#CitizenScience

🚨 New blog post 🚨

"**Optimal prediction sets for plant identification: an interactive guide**"

https://josephsalmon.eu/blog/long-tail/

Joint work with Tiffany Ding and Jean-Baptiste Fermanian.

#longtail
#PlantNet
#AppliedConformalPrediction
#ConformalPrediction

🚨 New blog post 🚨

"**Optimal prediction sets for plant identification: an interactive guide**"

https://josephsalmon.eu/blog/long-tail/

Joint work with Tiffany Ding and Jean-Baptiste Fermanian.

#longtail
#PlantNet
#AppliedConformalPrediction
#ConformalPrediction

🚨 New blog post 🚨

"**Optimal prediction sets for plant identification: an interactive guide**"

https://josephsalmon.eu/blog/long-tail/

Joint work with Tiffany Ding and Jean-Baptiste Fermanian.

#longtail
#PlantNet
#AppliedConformalPrediction
#ConformalPrediction

🚨 New blog post 🚨

"**Optimal prediction sets for plant identification: an interactive guide**"

https://josephsalmon.eu/blog/long-tail/

Joint work with Tiffany Ding and Jean-Baptiste Fermanian.

#longtail
#PlantNet
#AppliedConformalPrediction
#ConformalPrediction

🚨 New blog post 🚨

"**Optimal prediction sets for plant identification: an interactive guide**"

https://josephsalmon.eu/blog/long-tail/

Joint work with Tiffany Ding and Jean-Baptiste Fermanian.

#longtail
#PlantNet
#AppliedConformalPrediction
#ConformalPrediction

Some thoughts on #conformalprediction
for #timeseries with Xiaoqian Wang https://robjhyndman.com/publications/cpts.html

Some thoughts on #conformalprediction
for #timeseries with Xiaoqian Wang https://robjhyndman.com/publications/cpts.html

Some thoughts on #conformalprediction
for #timeseries with Xiaoqian Wang https://robjhyndman.com/publications/cpts.html

Some thoughts on #conformalprediction
for #timeseries with Xiaoqian Wang https://robjhyndman.com/publications/cpts.html

Some thoughts on #conformalprediction
for #timeseries with Xiaoqian Wang https://robjhyndman.com/publications/cpts.html

In the last couple of weeks I've been learning about #ConformalPrediction, a family of algorithms to measure the uncertainty of predictions made by #MachineLearning models.

Here are a few links to get you started:
- CP course by @ChristophMolnar https://mindfulmodeler.substack.com/p/week-1-getting-started-with-conformal
- Multi-class notebook (in Spanish) https://nbviewer.org/github/MMdeCastro/Uncertainty_Quantification_XAI/blob/main/UQ_multiclass.ipynb
- MAPIE library: https://mapie.readthedocs.io/en/latest/index.html
- TorchCP library: https://github.com/ml-stat-Sustech/TorchCP

In the last couple of weeks I've been learning about #ConformalPrediction, a family of algorithms to measure the uncertainty of predictions made by #MachineLearning models.

Here are a few links to get you started:
- CP course by @ChristophMolnar https://mindfulmodeler.substack.com/p/week-1-getting-started-with-conformal
- Multi-class notebook (in Spanish) https://nbviewer.org/github/MMdeCastro/Uncertainty_Quantification_XAI/blob/main/UQ_multiclass.ipynb
- MAPIE library: https://mapie.readthedocs.io/en/latest/index.html
- TorchCP library: https://github.com/ml-stat-Sustech/TorchCP

In the last couple of weeks I've been learning about #ConformalPrediction, a family of algorithms to measure the uncertainty of predictions made by #MachineLearning models.

Here are a few links to get you started:
- CP course by @ChristophMolnar https://mindfulmodeler.substack.com/p/week-1-getting-started-with-conformal
- Multi-class notebook (in Spanish) https://nbviewer.org/github/MMdeCastro/Uncertainty_Quantification_XAI/blob/main/UQ_multiclass.ipynb
- MAPIE library: https://mapie.readthedocs.io/en/latest/index.html
- TorchCP library: https://github.com/ml-stat-Sustech/TorchCP

In the last couple of weeks I've been learning about #ConformalPrediction, a family of algorithms to measure the uncertainty of predictions made by #MachineLearning models.

Here are a few links to get you started:
- CP course by @ChristophMolnar https://mindfulmodeler.substack.com/p/week-1-getting-started-with-conformal
- Multi-class notebook (in Spanish) https://nbviewer.org/github/MMdeCastro/Uncertainty_Quantification_XAI/blob/main/UQ_multiclass.ipynb
- MAPIE library: https://mapie.readthedocs.io/en/latest/index.html
- TorchCP library: https://github.com/ml-stat-Sustech/TorchCP

In the last couple of weeks I've been learning about #ConformalPrediction, a family of algorithms to measure the uncertainty of predictions made by #MachineLearning models.

Here are a few links to get you started:
- CP course by @ChristophMolnar https://mindfulmodeler.substack.com/p/week-1-getting-started-with-conformal
- Multi-class notebook (in Spanish) https://nbviewer.org/github/MMdeCastro/Uncertainty_Quantification_XAI/blob/main/UQ_multiclass.ipynb
- MAPIE library: https://mapie.readthedocs.io/en/latest/index.html
- TorchCP library: https://github.com/ml-stat-Sustech/TorchCP

⏩ Analítica acelerada con Shapelets y conformal prediction

https://fediverse.tv/w/ik5pf8Ew5WkGzMz8eckomn

Nos vemos *hoy* en nuestra reunión de marzo: ⏩ Analítica acelerada con Shapelets y conformal prediction, este mes en The Bridge

https://www.meetup.com/pydata-madrid/events/299749589/

¡Te esperamos a las 19:00! Y después, networking 🗣️

#PyDataMadrid #PyData #python #MachineLearning #ConformalPrediction #shapelets

Nos vemos *hoy* en nuestra reunión de marzo: ⏩ Analítica acelerada con Shapelets y conformal prediction, este mes en The Bridge

https://www.meetup.com/pydata-madrid/events/299749589/

¡Te esperamos a las 19:00! Y después, networking 🗣️

#PyDataMadrid #PyData #python #MachineLearning #ConformalPrediction #shapelets

Nos vemos *hoy* en nuestra reunión de marzo: ⏩ Analítica acelerada con Shapelets y conformal prediction, este mes en The Bridge

https://www.meetup.com/pydata-madrid/events/299749589/

¡Te esperamos a las 19:00! Y después, networking 🗣️

#PyDataMadrid #PyData #python #MachineLearning #ConformalPrediction #shapelets

Nos vemos *hoy* en nuestra reunión de marzo: ⏩ Analítica acelerada con Shapelets y conformal prediction, este mes en The Bridge

https://www.meetup.com/pydata-madrid/events/299749589/

¡Te esperamos a las 19:00! Y después, networking 🗣️

#PyDataMadrid #PyData #python #MachineLearning #ConformalPrediction #shapelets

Nos vemos *hoy* en nuestra reunión de marzo: ⏩ Analítica acelerada con Shapelets y conformal prediction, este mes en The Bridge

https://www.meetup.com/pydata-madrid/events/299749589/

¡Te esperamos a las 19:00! Y después, networking 🗣️

#PyDataMadrid #PyData #python #MachineLearning #ConformalPrediction #shapelets

Deepmind erforscht die unsicheren Wahrheiten der Künstlichen Intelligenz

#KI,#Grundwahrheit,#Unsicherheit,#DeepMind,#Google,#Annotationen,#StatistischeModelle,#ConformalPrediction,#MonteCarloCP,#Hautzustandsklassifizierung

https://kinews24.de/deepmind-erforscht-die-unsicheren-wahrheiten/

TIL of #conformalprediction, a way to assess the uncertainty of a prediction (from any algorithm, including from #machineleaning). It is used in research to make #autonomousdriving safer by predicting other agent's movements: https://www.youtube.com/watch?v=QvIJH4cZy3E

It does not require an expert model, but in turn it needs a statistically representative dataset.

TIL of #conformalprediction, a way to assess the uncertainty of a prediction (from any algorithm, including from #machineleaning). It is used in research to make #autonomousdriving safer by predicting other agent's movements: https://www.youtube.com/watch?v=QvIJH4cZy3E

It does not require an expert model, but in turn it needs a statistically representative dataset.

TIL of #conformalprediction, a way to assess the uncertainty of a prediction (from any algorithm, including from #machineleaning). It is used in research to make #autonomousdriving safer by predicting other agent's movements: https://www.youtube.com/watch?v=QvIJH4cZy3E

It does not require an expert model, but in turn it needs a statistically representative dataset.

Why perform cross validation (CV) in #MachineLearning? To estimate the generalization error of a trained predictor. This paper uses the idea of a #ProperLoss (called Q-class). Then it covers CV, bootstrap, and Mallow's covariance penalties. It also covers #ConformalPrediction, which is newly popular because of Emanuel Candes' keynote at #NeurIPS 2022
https://doi.org/10.3390/stats4040063
The paper is also a good advertisement for Efron and Hastie's recent book.

Why perform cross validation (CV) in #MachineLearning? To estimate the generalization error of a trained predictor. This paper uses the idea of a #ProperLoss (called Q-class). Then it covers CV, bootstrap, and Mallow's covariance penalties. It also covers #ConformalPrediction, which is newly popular because of Emanuel Candes' keynote at #NeurIPS 2022
https://doi.org/10.3390/stats4040063
The paper is also a good advertisement for Efron and Hastie's recent book.

Why perform cross validation (CV) in #MachineLearning? To estimate the generalization error of a trained predictor. This paper uses the idea of a #ProperLoss (called Q-class). Then it covers CV, bootstrap, and Mallow's covariance penalties. It also covers #ConformalPrediction, which is newly popular because of Emanuel Candes' keynote at #NeurIPS 2022
https://doi.org/10.3390/stats4040063
The paper is also a good advertisement for Efron and Hastie's recent book.

Why perform cross validation (CV) in #MachineLearning? To estimate the generalization error of a trained predictor. This paper uses the idea of a #ProperLoss (called Q-class). Then it covers CV, bootstrap, and Mallow's covariance penalties. It also covers #ConformalPrediction, which is newly popular because of Emanuel Candes' keynote at #NeurIPS 2022
https://doi.org/10.3390/stats4040063
The paper is also a good advertisement for Efron and Hastie's recent book.

Why perform cross validation (CV) in #MachineLearning? To estimate the generalization error of a trained predictor. This paper uses the idea of a #ProperLoss (called Q-class). Then it covers CV, bootstrap, and Mallow's covariance penalties. It also covers #ConformalPrediction, which is newly popular because of Emanuel Candes' keynote at #NeurIPS 2022
https://doi.org/10.3390/stats4040063
The paper is also a good advertisement for Efron and Hastie's recent book.

🚀 #AWS Fortuna is skyrocketing! 🚀 Just a few days, and so many GitHub stars and forks! ⭐️

Fortuna supports #ConformalPrediction, #BayesianInference and other methods for #UncertaintyQuantification in #DeepLearning.

Try it out and let us know!
https://github.com/awslabs/fortuna

In collaboration with @cedapprox @andrewgwils and team.

#uncertainty #neuralnetworks #bayesian #conformal #calibration #jax #flax #python #opensource #library #machinelearning #ai

🚀 #AWS Fortuna is skyrocketing! 🚀 Just a few days, and so many GitHub stars and forks! ⭐️

Fortuna supports #ConformalPrediction, #BayesianInference and other methods for #UncertaintyQuantification in #DeepLearning.

Try it out and let us know!
https://github.com/awslabs/fortuna

In collaboration with @cedapprox, @andrewgwils and team.

#uncertainty #neuralnetworks #bayesian #conformal #calibration #jax #flax #python #opensource #library #machinelearning #ai

🚀 #AWS Fortuna is skyrocketing! 🚀 Just a few days, and so many GitHub stars and forks! ⭐️

Fortuna supports #ConformalPrediction, #BayesianInference and other methods for #UncertaintyQuantification in #DeepLearning.

Try it out and let us know!
https://github.com/awslabs/fortuna

In collaboration with @cedapprox, @andrewgwils and team.

#uncertainty #neuralnetworks #bayesian #conformal #calibration #jax #flax #python #opensource #library #machinelearning #ai

🚀 #AWS Fortuna is skyrocketing! 🚀 Just a few days, and so many GitHub stars and forks! ⭐️

Fortuna supports #ConformalPrediction, #BayesianInference and other methods for #UncertaintyQuantification in #DeepLearning.

Try it out and let us know!
https://github.com/awslabs/fortuna

In collaboration with @cedapprox, @andrewgwils and team.

#uncertainty #neuralnetworks #bayesian #conformal #calibration #jax #flax #python #opensource #library #machinelearning #ai

🚀 #AWS Fortuna is skyrocketing! 🚀 Just a few days, and so many GitHub stars and forks! ⭐️

Fortuna supports #ConformalPrediction, #BayesianInference and other methods for #UncertaintyQuantification in #DeepLearning.

Try it out and let us know!
https://github.com/awslabs/fortuna

In collaboration with @cedapprox, @andrewgwils and team.

#uncertainty #neuralnetworks #bayesian #conformal #calibration #jax #flax #python #opensource #library #machinelearning #ai

🚀 #AWS Fortuna is skyrocketing! 🚀 Just a few days, and so many GitHub stars and forks! ⭐️

Fortuna supports #ConformalPrediction, #BayesianInference and other methods for #UncertaintyQuantification in #DeepLearning.

Try it out and let us know!
https://github.com/awslabs/fortuna

In collaboration with @cedapprox, @andrewgwils and team.

#uncertainty #neuralnetworks #bayesian #conformal #calibration #jax #flax #python #opensource #library #machinelearning #ai

Just shared a more hands-on guide for using the new package for #conformalprediction in #julia that I’ve been working on: https://github.com/pat-alt/ConformalPrediction.jl
“How to Coformalize a Deep Image Classifier” on TDS (https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) or my blog (https://www.paltmeyer.com/blog/posts/conformal-image-classifier/)

Thoughts and contributions welcome 🤗

Just shared a more hands-on guide for using the new package for #conformalprediction in #julia that I’ve been working on: https://github.com/pat-alt/ConformalPrediction.jl
“How to Coformalize a Deep Image Classifier” on TDS (https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) or my blog (https://www.paltmeyer.com/blog/posts/conformal-image-classifier/)

Thoughts and contributions welcome 🤗

Just shared a more hands-on guide for using the new package for #conformalprediction in #julia that I’ve been working on: https://github.com/pat-alt/ConformalPrediction.jl
“How to Coformalize a Deep Image Classifier” on TDS (https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) or my blog (https://www.paltmeyer.com/blog/posts/conformal-image-classifier/)

Thoughts and contributions welcome 🤗

Just shared a more hands-on guide for using the new package for #conformalprediction in #julia that I’ve been working on: https://github.com/pat-alt/ConformalPrediction.jl
“How to Coformalize a Deep Image Classifier” on TDS (https://towardsdatascience.com/how-to-conformalize-a-deep-image-classifier-14ead4e1a5a0) or my blog (https://www.paltmeyer.com/blog/posts/conformal-image-classifier/)

Thoughts and contributions welcome 🤗

One approach to do conformal prediction in regression is to use quantile regression (pinball loss). One annoying thing about quantile regression is that if you estimate multiple quantiles, they could cross (and they really shoudn't). This paper proposes a method that prevents crossing (there are other papers that do so too), in particular for conformal prediction.

https://arxiv.org/abs/2210.10161v1

#MachineLearning #ConformalPrediction #QuantileRegression

One approach to do conformal prediction in regression is to use quantile regression (pinball loss). One annoying thing about quantile regression is that if you estimate multiple quantiles, they could cross (and they really shoudn't). This paper proposes a method that prevents crossing (there are other papers that do so too), in particular for conformal prediction.

https://arxiv.org/abs/2210.10161v1

#MachineLearning #ConformalPrediction #QuantileRegression