With this announcement out, it's time for an overdue career update! I’ll be joining Harvard SEAS in 2023. I’m beyond thrilled to be part of this superb cohort of incoming faculty, and to join such an exciting, forward-looking and interdisciplinary academic unit as @hseas. [1/n]
New paper (w/ @nfusi) #ICML2021: “Dataset Dynamics via Gradient Flows in Probability Space”
TL; DR: we cast transfer learning, dataset ‘shaping’ and model re-purposing as *labeled* dataset optimization using gradient flows in the "space of datasets"
Longer version: a🧵
Exactly 10 years ago this day, I arrived in the US with my entire life packed into two suitcases for what was supposed to be just a masters’ degree “and then I’ll head back home” 😅
Happening today! Join us in room 220-222 for a day packed with Optimal Transport, from theory to applications, w/ talks by Felix Otto, Laetitia Chapel @RFlamary @brandonamos@ArnaudDoucet1 Florentina Bunea, Smita Krishnaswamy, Sinho Chewi
among others! #FeelTheMonge#NeurIPS2023
🚀 #OTML is back for this year’s NeurIPS! We'll have a superb set of speakers (TBA soon) spanning theory, stats, and comp aspects of OT. If you're looking for a home for your work on OT or related topics, we'd love to see it (link to CFP below). Deadline: Sep 29th. #Monge
It’s official! 👩🏻🎓Dr Amores it is. Extremely proud of @jdthamores for a spectacular PhD defense. A fitting testament to a remarkably interdisciplinary researcher, her thesis seamlessly weaves together science, art and design. She never ceases to amaze me, and did so again today 🎉
Come work with us, now *in person* again! We have projects spanning various areas of ML and Stats, and an awesome team of researchers to work with. Apply below 👇
If you're a PhD student interested in interning with me or one of my amazing colleagues at MSR New England this summer, please apply here
careers.microsoft.com/us/en/job/1483…
This has been a few months in the making, so very happy to finally see it go live! 🎆
The blog gives an overview of our paper (w/ @nfusi) on OT dataset distances (+ fun interactive visualizations).
The context: we're interested in a notion of distance between datasets... (1/n)
Microsoft researchers propose Optimal Transport Dataset Distance, a method for computing similarity b/w labeled datasets—regardless of whether their label sets are directly comparable. Learn how it can predict task transferability & help data augmentation: aka.ms/AA9p00v
🚀 #OTML is back for this year’s NeurIPS! We'll have a superb set of speakers (TBA soon) spanning theory, stats, and comp aspects of OT. If you're looking for a home for your work on OT or related topics, we'd love to see it (link to CFP below). Deadline: Sep 29th. #Monge
✨Another edition of the Optimal Transport and Machine Learning workshop is taking place at #NeurIPS2023.
🚀Contribute your work on OT and theory, computation, deep learning, or applications until Sept 29 (otmlworkshop.github.io/call)! Details on speakers and schedule coming soon.
Some things sound too good to be true. This is *not* one of them. Been here only a few weeks, but can safely say it’s as great as this listing makes it sound - awesome collaborators, exciting environment, freedom in shaping research agenda, i.e., postdoc paradise. Apply!
𝐶𝑎𝑛 𝑦𝑜𝑢 𝑡𝑒𝑎𝑐ℎ 𝑎𝑛 “𝑜𝑙𝑑 𝐿𝐿𝑀” 𝑛𝑒𝑤 𝑡𝑟𝑖𝑐𝑘𝑠? 🐕🦺
You *can* if you ⟨𝕋𝔸𝔾⟩ your commands!
In this work led by superstar @JunhongShen1 (w/ @nfusi@ntenenz Jimmy Hall) we introduce a simple-but-effective method to ‘repurpose’ LLMs to new domains and tasks
Microsoft presents Tag-LLM
Repurposing General-Purpose LLMs for Specialized Domains
paper page: huggingface.co/papers/2402.05…
Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language. However, their capabilities wane in
