p.s. we pronounce SSME as "Sesame" but you're welcome to your favorite pronunciation :)

Thanks also to our wonderful set of co-authors - Manish Raghavan (@manishraghav.bsky.social) , John Guttag, Bonnie Berger, and Emma Pierson (@emmapierson.bsky.social)-- without whom this work would not be possible!

Last but not least, thanks to @shuvoms.bsky.social,
who co-led this work with me, and is an excellent thinking partner. Collaborate with him if you can!!

The paper includes much more, including theoretical connections to the literature on semi-supervised mixture models. Lots of exciting directions ahead – come chat with me and Shuvom at NeurIPS this December in San Diego!
📄 Paper: arxiv.org/abs/2501.11866
💻 Code: github.com/divyashan/SSME

Across 8 tasks, 4 metrics, and dozens of classifiers, SSME consistently outperforms prior work, reducing estimation error by 5.1× vs. using labeled data alone and 2.4× vs. the next-best method!

SSME starts with a set of classifiers, unlabeled data, and bit of labeled data, and estimates the joint distribution of classifier scores and ground truth labels using a mixture model. SSME benefits from three sources of info: multiple classifiers, unlabeled data, and classifier scores.

thank you, gabriel!! glad i've gotten to learn so much about maps from you :')

thank you, Erica 🥹 so glad we got to work together this year!

thank you, Emma!!! likewise, i'm so grateful for our collaborations over the years!

thank you, Kenny!!! that's so nice of you to say.

for an up-to-date review of recent and ongoing work, you can learn more at dmshanmugam.github.io :)

I'll be at INFORMS, ML4H, and NeurIPS later this year, presenting on recent work related to challenges of imperfect data & models in healthcare (and what we can do about them) -- more on these pieces of work soon!!