Silvia Chiappa

@csilviavr

Machine Learning Researcher at DeepMind.

Vrijeme pridruživanja: travanj 2017.

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  1. 26. sij

    The effort that ACM FAT* is making for advancing work on fairness and related topics is great, but I find its name somewhat incompatible with fairness. I wish the organizers would change the name: Many would feel more comfortable in presenting their work there .

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  2. 24. sij

    Reach out to our sponsorship chair if you are interested in sponsoring AISTATS 2020.

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  3. proslijedio/la je Tweet
    22. sij

    Just a reminder - if you're planning to attend AISTATS and have childcare needs - or simply have a relevant opinion to share - please fill out this form. Thanks for those already providing details!

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  4. 16. sij

    I am very proud of this initiative, and I hope we will be able inspire other ML conferences to follow the same path!

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    9. sij

    We are organizing a workshop on Causal learning for Decision Making at along with , Jovana Mitrovic, , Stefan and . Consider submitting your work!

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  6. 10. sij

    Totally agree! We stress the importance of DAGs and (path-specific) counterfactuals in reasoning about and dealing with fairness in

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  7. 10. stu 2019.

    We believe that it is preferable to have a clear conference policy on parallel work. How to treat non-peer reviewed or recent publications is something that would need more discussion.

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  8. 27. lis 2019.

    Appello - Sign the Petition! via

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    22. lis 2019.

    We had a record-breaking number of submissions to AISTATS this year! Congratulations to all authors! The reviewing phase will start in a few hours.

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    19. lis 2019.

    I and 50601 others have signed the open letter demanding a on Brexit - add your name to it here: ✍️

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  11. 13. lis 2019.

    I really enjoyed participating: a lot of interesting research to learn about and many interesting discussions. Thanks Jes and the other organizers for inviting me!

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  12. 5. lis 2019.

    We use CBNs to highlight the necessity of understanding the data underlying the ML system for fairness, and to introduce techniques to deal with complex fairness scenarios. More details in the papers:

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    30. ruj 2019.

    It starts when follow-up work by well-known researchers doesn't admit that it's following your line. Next wave of papers propose "new" ideas, without realizing they are instances. Then work comes out where you're written out of the history. Scholarship matters.

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    27. ruj 2019.

    The field continues to suffer from a severe lack of ethnic, gender, & cultural diversity. To explore these pervasive challenges, today gifted £250K to the Partnership on AI to support the establishment of a fellowship focused on this task:

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    25. ruj 2019.

    This new book: Handbook of Graphical Models captures well how the field has grown since its inception in the 1980's. Written by top statisticians, it can serve as a good introduction to causal modeling for traditionally-trained statisticians.

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    21. ruj 2019.

    Beware of an important distinction between "counterfactual explanations" in ML, eg. , and in science. The former explains when "system output" would change. The latter explains when "world outcome" would change.

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    8. ruj 2019.

    The Chatbot has descended to a whole new level of incompetence, with . Classic symptoms in a FEMALE, results in a diagnosis of or . The Chatbot ONLY suggests the possibility of a in MEN!

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  18. 11. ruj 2019.

    Very happy that we have a new amazing centre for ML research in London! Awesome achievement by and UCL.

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    9. ruj 2019.

    OPT 2019 happening on 14th Dec; 5 min walking distance from – visit for the CfP. The space is limited to 50 people and we will provide breakfast and coffee (no registration fees). DM me in case you have questions. All participants welcome.

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    6. ruj 2019.

    Thrilled to be able to share what I've been working on for the last year - solving the fundamental equations of quantum mechanics with deep learning!

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