#DistributionShift - Twitter Hashtag

about 1 year ago

6/6 w/@LiuChenruo, @KenanTang, @YaoQin_UCSB, we hope this bridges two critical conversations—and inspires new ones. Looking forward to your thoughts. #AISafety #RobustML #TrustworthyAI #DistributionShift #MLTheory

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Jielin Qiu @_Jason_Q

over 2 years ago

🎊Extremely honored to share that our paper on multimodal model robustness has been accepted as the 1st paper for the Journal of Data-centric Machine Learning Research @DMLRJournal With @yizhu59 @sxjscience @flwenz @mli65 #Multimodal #Robustness #DistributionShift

Journal of Data-centric Machine Learning Research @DMLRJournal

over 2 years ago

'Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift' by Jielin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao, Bo Li, Mu Li Action Editor: Hongyang Zhang https://t.co/mXh5OCAt9T #Multimodal #Robustness #DistributionShift

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Journal of Data-centric Machine Learning Research @DMLRJournal

over 2 years ago

'Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift' by Jielin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao, Bo Li, Mu Li Action Editor: Hongyang Zhang https://t.co/mXh5OCAt9T #Multimodal #Robustness #DistributionShift

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Lakmal Meegahapola @lakmalbuddika

about 3 years ago

This work was done in the context of @WeNetProject. Stay tuned for the pre-print. @EPFL @Idiap_ch #generalization #datasetshift #mobilesensing #sensors #bias #distributionshift #ML #AI #machinelearning

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Navid Shervani-Tabar @Nshervt

about 3 years ago

I had a great time learning from @sirbayes on various topics, including prediction under #DistributionShift and online decision making, during his visit to @NotreDame.

Nshervt's tweet photo. I had a great time learning from @sirbayes on various topics, including prediction under #DistributionShift and online decision making, during his visit to @NotreDame. https://t.co/EuCmF9L2cj

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Kishan @kpb_in_acad

over 3 years ago

Yet another #NeurIPS22 post in your timeline! 📣📣 My first in-person conference! 💡💡 Need your #ReinforcementLearning algorithms to be ROBUST??? a.k.a #distributionshift Attend our poster in Hall J: Tue 29 Nov 4 p.m. CST https://t.co/R9dmzXuYc7

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Jonas M. Kübler @jonas_kubler

almost 4 years ago

We use #AutoGluon (@innixma @smolix ) which out of the box leads to great empirical results and can outperform recent MMD-based tests (@AntoninSchrab, @AlexFengLiu1). In particular, we run the #distributionShift benchmark of @steverab @zacharylipton @guenneman.

jonas_kubler's tweet photo. We use #AutoGluon (@innixma @smolix ) which out of the box leads to great empirical results and can outperform recent MMD-based tests (@AntoninSchrab, @AlexFengLiu1).
In particular, we run the #distributionShift benchmark of @steverab @zacharylipton @guenneman. https://t.co/aKM2FKPG8x

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Danilo Bzdok

@danilobzdok

about 4 years ago

Our paper on #distributionshift in human neuroscience now accepted at @PLOSBiology. Congrats to @oualid_benkarim for leading this quantitative analysis! #untracked #diversity #outofdistribution @enigmabrains @ABCD_ReproNim @BertrandThirion @phyzang

Danilo Bzdok

@danilobzdok

almost 5 years ago

New preprint, led by @oualid_benkarim, examining out-of-distribution prediction in #ABIDE + #HBN: Analytical tools are needed to get a handle on #untracked #diversity in multi-site cohorts https://t.co/17sVduBFeo

danilobzdok's tweet photo. New preprint, led by @oualid_benkarim, examining
out-of-distribution prediction in #ABIDE + #HBN:

Analytical tools are needed to get a handle on
#untracked #diversity in multi-site cohorts

https://t.co/17sVduBFeo https://t.co/7QW32po4lC

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Kevin Patrick Murphy

@sirbayes

over 4 years ago

I am delighted to announce that a draft of my latest book, “Probabilistic Machine Learning: Advanced Topics”, is now available online at https://t.co/dSlKkwYpLr. It covers #DeepGenerativeModels, #BayesianInference, #Causality, #ReinforcementLearning, #DistributionShift, etc.

sirbayes's tweet photo. I am delighted to announce that a draft of my latest book, “Probabilistic Machine Learning: Advanced Topics”, is now available online at https://t.co/dSlKkwYpLr. It covers #DeepGenerativeModels, #BayesianInference, #Causality, #ReinforcementLearning, #DistributionShift, etc. https://t.co/BbLFTNZSro

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KSE @semanticbeeng

over 5 years ago

@jreuben1 > #machinelearning pipeline as directed acyclic graph (#DAG) once for both training and inference (scoring) DAGs are not powerful enough for modeling #NonStationaryLearning (#distributionShift, #conceptDrift) + we need integration with other application level business processes

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KSE @semanticbeeng

over 5 years ago

"#ML systems which depend strongly on properties of their inputs (i.i.d. assumption) tend to fail silently; in practice; #ML pipelines rarely inspect incoming data for signs of #distributionShift" #NonStationaryLearning #ModelRisk #MachineLearningEngineering Beyond "#MLOps"

semanticbeeng's tweet photo. "#ML systems which depend strongly on properties of their inputs (i.i.d. assumption) tend to fail silently; in practice; #ML pipelines rarely inspect incoming data for signs of #distributionShift"

#NonStationaryLearning #ModelRisk
#MachineLearningEngineering
Beyond "#MLOps" https://t.co/OSRI5I4L2Y

stat.ML Machine Learning arXiv submissions @arXiv_stat_ML

over 7 years ago

https://t.co/lnLCn3rjZc S Rabanser et. al. Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

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