๐ข New Paper Update @aistats_conf ๐ข
TLDR: We present causally consistent method, LOGIC, for missing-data imputation that unifies causal discovery and can explicitly say: โInsufficient info, cannot impute .โ ๐
#AISTATS2026
Joint work with @JensKleesiek & @MichaelKamp7
@jack_merullo_ @RajuSrihita @_MichaelPearce@ElanaPearl This is some great work! Looking only at the representation makes a lot of sense from a learning-theoretic perspective (we showed that at NeurIPS 2021). I'm working with a brilliant PhD student and we see the similar phenomena for adversarial examples (https://t.co/3kfbWHThnt).
Being part of #ELLIS was one of the best experiences in my PhD path. I hope to continue productive collaboration with the network further as a more senior researcher!
Meet Linara Adilova, Postdoc at @TU_Dortmund ๐ฉ๐ช, ELLIS Member, & ELLIS PhD Grad ๐ at RU Bochum & @EPFL
Her research explores the theory of #DeepLearning from loss surfaces to info-theoretic training analysis.
Check out her research goals in AI/ML ๐
#WomenInELLIS
@WilliamLMnz@flwrlabs I love flower! We implemented FedBN in it and recently a student implemented Federated Daisy-Chaining. Thanks a lot to the flower-team for supporting my student, btw. You guys are awesome!
PhD Position in Trustworthy AI at the Lamarr Institute (TU Dortmund, Germany)!
Iโm looking for outstanding PhD student working on:
โ Federated & Multi-Agent Learning
โ Theory of Deep Learning
โ Causality and Causal Representations
(1/7)
@AsmaImt72364308 I'm in computer science, so I have no idea about the admission process there. In general, you want to apply for a PhD position. As soon as you have an offer, you can get a visa (check if you might be eligible for a blue card).
9/
If youโre serious about science, and about doing it sustainably, Germany might be the place.
Get in touch.
Ask questions.
Apply.
Letโs build something worth building.
(End ๐งต)
(13/13)
8/
We donโt promise Google salaries.
But we offer something different:
Scientific freedom.
Stability.
Time to think deeply.
And colleagues who care more about ideas than metrics.
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