🔥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

Michael and Jie did an amazing job on their first PhD project, by finding and fixing common pitfalls in empirical ML privacy evaluations. It turns out, if you evaluate things properly, DP-SGD is also the best *heuristic* defense when you instantiate it with large epsilon values.

Very nice empirical work by Francesco Pinto @ Neurips 2024 Yaxi Hu Amartya Sanyal while they were in our group: a simple method that leverages public unlabeled data well to make semi-private learning more effective in practice

If you're AISTATS Conference and interested in causal inference, feel free to talk to my wonderful students Piersilvio De Bartolomeis & Javier Abad Martinez at the poster session today!

📣 Postdoctoral fellowships, up to 24 months to conduct cutting-edge research in Europe. If you would like the Rational Intelligence Lab life to host you, reach out to me directly. 🤝 🗓️ Deadline: 11 September 2024 🙏 RT please 🥺 …sklodowska-curie-actions.ec.europa.eu/news/msca-open…

Very happy that this paper was accepted to #COLT2024 on the separation between private and non-private online learnability. Joint work with Daniil Dmitriev and Kristof Szabo. There are still few open problems that remain to fully settle this question :)

Excited to be at #ICML2024 to present on Thurs morning (11:30am, Hall C4-9, Poster 2217) our work on FRAPPÉ, a generic procedure for turning any multi-task objective into a modular bilevel optimization problem, with implications for fairness, alignment, or multi-domain learning.

📣Announcing the 2024 NeurIPS Workshop on Statistical Frontiers in LLMs and Foundation Models 📣 Submissions open now, deadline September 15th sites.google.com/berkeley.edu/b… If your work intersects with statistics and black-box models, please submit! This includes: ✅ Bias ✅

If you're working in the area of statistics for machine learning, consider submitting to this #neurips workshop until September 13!

If you're interested to work on mathematical foundations for trustworthy machine learning, consider applying to this fellowship ETH AI Center!

They even made a dramatic poster to go with it 🤣

Watch this special debate live tomorrow at 10:30 a.m. PT — part of our workshop on Unknown Futures of Generalization. Register to attend in person, access the livestream, or view the recording before it's captioned for publication: simons.berkeley.edu/web-registrati…

Join us at LeT-All's NeurIPS Social on Thursday 7:30pm and chat with awesome mentors like Amin Karbasi , Cristóbal Guzmàn, Gavin Brown , Vasilis Kontonis , Ankit Pensia (Ankit Pensia), Ramya Vinayak , Maryam Aliakbarpour , Erin Grant, Josh Alman (Josh Alman), Surbhi Goel , Vatsal Sharan

What's the cost of representing small subgroups when the outlier proportion may be larger? Find out from Daniil Dmitriev at the poster session Wed, 4:30 pm PT, #5710 #NeurIPS2024. Joint with @rares_buhai S. Tiegel, Alexander Wolters, G. Novikov, Amartya Sanyal, D. Steurer

How robustly can we predict when multiple environments are not heterogeneous enough? Invariant-based methods in the setting of "partially identifiable" robust risks rank very differently - more details from Julia Kostin @ 11am PST, East Exhibit Hall A-C #4606 #Neurips2024

Eager to hear feedback from anyone who applies causal inference about this recent work with this amazing group of people Piersilvio De Bartolomeis Javier Abad Martinez Guanbo Wang Konstantin Donhauser Ray Duch and Issa Dahabreh.

Interested in theory for high-dimensional statistical problems? The workshop "Youth in High Dimensions" in #Trieste July 7-9 has an amazing speaker lineup and offers great networking opportunities indico.ictp.it/event/10849 - co-organized w/ Marco Mondelli Sebastian Goldt JeanBarbier

x.com/AerniMichael/s… I'm also excited to present this paper about LLMs inadvertently leaking training data on Thursday afternoon (tomorrow!)

For those at #ICLR working on memorization/privacy/copyright check out Javier Abad Martinez 's oral and poster presentations today on a simple method to prevent LLMs from completing copyrighted work.

If you're at #ICLR in Singapore and interested in treatment effect estimation and causal inference, try to catch Piersilvio De Bartolomeis from our group today who has published a series of interesting work exploring how to leverage multiple data sources to enhance causal inference

Register now (first-come first-served) for the "Math of Trustworthy ML workshop" at #LagoMaggiore, Switzerland, Oct 12-16 this year, with a great speaker lineup and the opportunity to present your work as a poster session or contributed talk. Details @ mmlworkshop.ethz.ch