🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

CMU's Engineering & Public Policy Dept has a tenure-track opening in AI & public policy!! EPP is interested in both *policy for AI* and *AI for policy* If you're in CS, it's a SUPER exciting opportunity to use ML methods for real-world impact and learn from a dept that's done so

At NeurIPS? Check out the 2nd workshop on Attributing Model Behavior at Scale (ATTRIB)! Meeting Rm 205-207, starting @ 9am - amazing talks from Surbhi Goel Sanmi Koyejo Baharan Mirzasoleiman, Robert Geirhos, and Seong Joon Oh + exciting contributed talks! More info: attrib-workshop.cc

Giving a talk tomorrow at #NeurIPS2024!

Want state-of-the-art data curation, data poisoning & more? Just do gradient descent! w/ Andrew Ilyas Ben Chen Axel Feldmann Billy Moses Aleksander Madry: we show how to optimize final model loss wrt any continuous variable. Key idea: Metagradients (grads through model training)

Was really fun working on this project, and not just because of the results: huge shoutout to Logan Engstrom (who is graduating soon 👀) and our amazing **undergraduate student** Ben Chen!

Building AI systems is now a fragmented process spanning multiple organizations & entities. In new work (w/ Aspen Sarah Cen Andrew Ilyas Isabella Struckman Luis Videgaray Caso), we study the implications of these emerging networks → what we call *AI supply chains* 🧵

This work has been a long time coming and I'm so grateful to my collaborators for helping make this work possible. Main takeaway: AI supply chains matter! We've seen them emerge (rapidly) in the past few years and they will have implications on *all* of us, inside and outside

Ever wondered which data from large datasets (like OXE) actually helps when training/tuning a policy for specific tasks? We present DataMIL, a framework for measuring how each training sample influences policy performance, hence enabling effective data selection 🧵

📢Sign up to attend and present a poster/spotlight talk at the Summer Workshop on Collaborative Learning and Data Sharing! 🌟 Hear from speakers with experience spanning ML, theory, databases, law, policy, and more, including Yiling Chen, Raul Castro Fernandez, Niva Elkin-Koren,

(1/2) Successfully defended my thesis today! I was incredibly fortunate to work with my amazing supervisors Yaoliang Yu, Sun Sun, and Gautam Kamath. Huge thanks also to my committee members Elliot Creager, Hongyang Zhang, and Sébastien Gambs.