TOMORROW: Thesis Defense - July 12, 2023 - Sebastian Caldas - Collaborative learning by leveraging siloed data

[Artur Dubrawski]

On Tue, Jun 27, 2023 at 6:16 PM Artur Dubrawski <awd at cs.cmu.edu> wrote:

> This will be fun!
>
> Please mark your calendars and join to cheer for Sebastian while enjoying
> a presentation of exceptional quality.
>
> Cheers
> Artur
>
> ---------- Forwarded message ---------
> From: Diane Stidle <stidle at andrew.cmu.edu>
> Date: Tue, Jun 27, 2023 at 4:24 PM
> Subject: Thesis Defense - July 12, 2023 - Sebastian Caldas - Collaborative
> learning by leveraging siloed data
> To: ml-seminar at cs.cmu.edu <ML-SEMINAR at cs.cmu.edu>, <cler at pitt.edu>, <
> martin.jaggi at epfl.ch>
>
>
> *Thesis Defense*
>
> Date: July 12, 2023
> Time: 4:00pm (EDT)
> Place: GHC 8102 & Remote
> PhD Candidate: Sebastian Caldas
>
> *Title: **Collaborative learning by leveraging siloed data*
> Abstract:
> Regulations can often limit stakeholders’ modeling capabilities by
> preventing data sharing. For example, in order to protect patient privacy,
> clinical centers may be unable to share their data and thus lack
> representative records to learn about a rare condition. To address this
> challenge, previous work in machine learning has shown that these
> stakeholders benefit from training models in a collaborative fashion,
> improving their predictive performance. However, as we start training these
> collaborative models in real-world settings, and in order to be truly
> useful, they need to provide utility along dimensions beyond predictive
> performance. In this thesis, we propose methods and algorithms to improve
> collaborative models that leverage siloed data along three dimensions. In
> the first part, we propose methods to reduce the communication footprint of
> models learned by mobile devices cooperating over edge networks, allowing
> for higher capacity models to be trained. Then, in the second part, we
> introduce an algorithm that provides explanations about predictions of
> models trained across clinical centers, thus improving their clinical
> utility. Finally, in the third part, we address the need to encode expert
> supervision into collaborative models trained using on-device data,
> increasing the class of problems we can tackle in these scenarios.
>
> *Thesis Committee:*
> Artur Dubrawski (Chair)
> Virginia Smith
> Gilles Clermont (University of Pittsburgh) cler at pitt.edu
> Martin Jaggi (EPFL) martin.jaggi at epfl.ch
>
> *Link to the draft document: *
> https://drive.google.com/file/d/1xTPUERARdnQnMSNCnu2VXrWk1YFKQOqp/view?usp=sharing
>
>
> *Zoom meeting link:*
> https://cmu.zoom.us/j/92902855666?pwd=a3l4V3ppQlp1K252K2doSjNTRlhPdz09
> <https://www.google.com/url?q=https://cmu.zoom.us/j/92902855666?pwd%3Da3l4V3ppQlp1K252K2doSjNTRlhPdz09&sa=D&source=calendar&ust=1688311717182551&usg=AOvVaw0fNQndryXEeSLoOO-ZhNfp>
>
> --
> Diane Stidle
> PhD Program Manager
> Machine Learning Department
> Carnegie Mellon Universitystidle at andrew.cmu.edu
>
>
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