Kiarash Mohammadi

MSc student @Mila_Quebec/@UMontreal, prev @MPI_IS, @FU_Mashhad

[Kiarash Mohammadi @kiarashinho]

@tetraduzione @gfarnadi Grazie mille Antonio :D

[Kiarash Mohammadi @kiarashinho]

@gfarnadi Thank you for the kind words Golnoosh! Eagerly looking forward to the new adventure!

[Kiarash Mohammadi @kiarashinho]

Video of the talk: https://t.co/GSTl0rXcoD

[Kiarash Mohammadi @kiarashinho]

Interested in classic #AI solutions to modern #AI/#ML problems? See our work on “Scaling Guarantees for Nearest Counterfactual Explanations” @AIESConf! My first paper as the lead author!

The poster and info about the oral presentation available at:

https://t.co/nLcznfJV7W

[Kiarash Mohammadi @kiarashinho]

Immense thanks to the wonderful supervisors: @IValeraM, @heikalkhan, and Gilles Barthe ❤️

pdf: https://t.co/KZdQTp79hu
abs: https://t.co/SToG5zxR7Y

[Kiarash Mohammadi @kiarashinho]

@javaloyML @mcontisc @NikRuggeri Oh man, oh man...

[Kiarash Mohammadi @kiarashinho]

@UlrichJunker @AIESConf Thanks for watching!

[Kiarash Mohammadi @kiarashinho]

@heikalkhan @IValeraM Indeed ❤️

[Kiarash Mohammadi @kiarashinho]

@martin_trapp @AIESConf Thank you, Martin!

[Kiarash Mohammadi @kiarashinho]

@heikalkhan @AIESConf Oh Amir, thank you! Result of amazing mentorship/friendship ❤️

[Kiarash Mohammadi @kiarashinho]

@heghbalz @AIESConf Thanks a lot Hamid! Much credit goes to @heikalkhan!

[Kiarash Mohammadi @kiarashinho]

@heghbalz @cpjku @gather_town @prim_paul @kkoutini @shreyan_ch @lsmartak I see years of bomberman skills backing up these moves :D

[Kiarash Mohammadi @kiarashinho]

@heghbalz Special thanks to the wonderful supervisors!

[Kiarash Mohammadi @kiarashinho]

@heghbalz Thank you Hamid 🙏 wouldn't have been possible without the amazing collaborators.

[Kiarash Mohammadi @kiarashinho]

See how computing common objectives on MCs and MDPs becomes fixed-parameter #tractable under bounded #treewidth:

https://t.co/PmWPA2t0fh

More in the paper! (including linear-time solutions to systems of linear equations given small treewidth for the primal graph!)