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Masha Vladimirova Profile
Masha Vladimirova

@iamvladimirova

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300
Following
430
Statuses
53

Senior Researcher @ Criteo | Fairness, Causality, Deep learning theory

Joined August 2019
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@iamvladimirova
Masha Vladimirova
3 days
#AIActionSummit events have started with scientific days! 🥐🥐🥐
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@iamvladimirova
Masha Vladimirova
16 days
RT @criteo: We had full house at our Paris offices this week for our #TrustworthyAI Symposium, organized by @CriteoAILab in anticipation a…
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@iamvladimirova
Masha Vladimirova
25 days
🌟 Join us for the Trustworthy AI Symposium! 🌟 📅 Jan 21, 2025 📍 Criteo HQ, Paris 🌐 Dive into #ethicalAI, #fairness, & #transparency with top experts. Don’t miss this #AIActionSummit event! Co-organizers @jy_franceschi @NathalieLaneret @CriteoAILab
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@iamvladimirova
Masha Vladimirova
2 months
Still jet lagged after Vancouver but coming to the office was worth it! Closing the year with one more achievement: receiving a community award for my work on fairness! Thank you @CriteoEng @CriteoAILab
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@iamvladimirova
Masha Vladimirova
2 months
Dive into our #NeurIPS paper and join us in advancing fairness in advertising! 🔗 I am in Vancouver in person this week 🍁 If you attend the conference, more information on the poster at
@CriteoAILab
Criteo AI Lab
2 months
If you are attending #NeurIPS2024 today, don't miss the posters presentations by our Criteos 👇 📍West Ballroom A-D ⏰11am - 2pm PST @iamvladimirova @hr1ch3rd ⏰ 4:30pm to 7:30pm PST @jlnzhou @RahierThibaud @NeurIPSConf Let's take a look at the papers 🧵👇
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@iamvladimirova
Masha Vladimirova
2 months
This is so far one of my proudest moments: being showcasted at the Élysée palace in front of a hundred of leaders and experts 🤩 The AI field has a huge gender imbalance problem, it is important to support inclusion so that everybody feels belonging #NoAIWithoutWomen ❤️
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@iamvladimirova
Masha Vladimirova
10 months
RT @MrsCaroline_C: Only smilles tonight @WiMLDS_Paris ! Thank you to the speakers, the audience and @CriteoEng @CriteoAILab 📸 @erwanaliasr
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@iamvladimirova
Masha Vladimirova
1 year
We demonstrated the effectiveness of the approach on Criteo data, where treatments💉are bidding strategies🔨 and patients🤒 are users🙋(instead of opportunities). Beyond the industrial motivation, one may think of any allocation problem, where it is important to be risk-averse.
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@iamvladimirova
Masha Vladimirova
2 years
@CLeaR_2022 Hi! Thanks for organizing! Could you please specify what the virtual components are?
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@iamvladimirova
Masha Vladimirova
2 years
RT @ICBINBWorkshop: Excited to host @iamvladimirova at the ICBINB monthly seminar series! Title: Heavy tails in Bayesian neural networks:…
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@iamvladimirova
Masha Vladimirova
3 years
Many thanks to the speakers for presenting at our #BDL session! It was great and a lot of fun @vincefort @andrewgwils #ISBA2022
@JulyanArbel
Julyan Arbel
3 years
#ISBA2022 📢 Come and join our session on priors, PAC-Bayes, and Myths & Reality in Bayesian Deep Learning today at 1.30. Feat. stellar line-up: @vincefort Kostas Pitas @andrewgwils cc @iamvladimirova
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@iamvladimirova
Masha Vladimirova
3 years
Thank you @j_ISBA for organization and for the poster award! 🎉
@j_ISBA
j-ISBA
3 years
These 2 days of #BaYSM2022 have been intense but very exciting! Thanks to all the participants! Look forward to #ISBA2022@ISBA_events
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@iamvladimirova
Masha Vladimirova
3 years
RT @dabystrova: Survey on causal discovery from time series
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@iamvladimirova
Masha Vladimirova
3 years
RT @BayesDuality: We just had our first informal get together with some members of @BayesDuality project! Very excited to hear from so many…
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@iamvladimirova
Masha Vladimirova
3 years
Looking forward to giving a talk about BDL in presence!
@ISBA_events
ISBA
3 years
The 1st Bayesian Nonparametrics Networking workshop is planned to be held on 25-29 April 2022 in Nicosia (Cyprus), in presence. Abstract submission deadline: 28 January 2022 Early registration deadline: 10 March 2022 Link:
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@iamvladimirova
Masha Vladimirova
3 years
🥳We prove that the dependence expressed via ∆(z1, z2) is positive or negative wrt signs of z1 and z2. ✨Check out our paper to learn more about dependence and how it changes after changing the width and depth of the neural network! 3/3
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