Alex Hägele
@haeggee.bsky.social
PhD Student in Machine Learning @ICepfl MLO, MSc/BSc from @ETH_en.
haeggee.github.io
haeggee.github.io
Reposted by Alex Hägele
I am excited to announce that I will join the University of Zurich as an assistant professor in August this year! I am looking for PhD students and postdocs starting from the fall.
My research interests include optimization, federated learning, machine learning, privacy, and unlearning.
My research interests include optimization, federated learning, machine learning, privacy, and unlearning.
March 6, 2025 at 2:17 AM
I am excited to announce that I will join the University of Zurich as an assistant professor in August this year! I am looking for PhD students and postdocs starting from the fall.
My research interests include optimization, federated learning, machine learning, privacy, and unlearning.
My research interests include optimization, federated learning, machine learning, privacy, and unlearning.
Reposted by Alex Hägele
🤗Thanks a lot @haeggee.bsky.social and @mjaggi.bsky.social for having me in the MLO group at EPFL @icepfl.bsky.social to present "Large Language Models as Markov Chains".
Slides are available on my website (link in thread).
🎉 New experiments with Llama and Gemma models in the updated paper!
Slides are available on my website (link in thread).
🎉 New experiments with Llama and Gemma models in the updated paper!
February 28, 2025 at 1:03 PM
🤗Thanks a lot @haeggee.bsky.social and @mjaggi.bsky.social for having me in the MLO group at EPFL @icepfl.bsky.social to present "Large Language Models as Markov Chains".
Slides are available on my website (link in thread).
🎉 New experiments with Llama and Gemma models in the updated paper!
Slides are available on my website (link in thread).
🎉 New experiments with Llama and Gemma models in the updated paper!
Reposted by Alex Hägele
Learning rate schedules seem mysterious? Why is the loss going down so fast during cooldown?
Turns out that this behaviour can be described with a bound from *convex, nonsmooth* optimization.
A short thread on our latest paper 🚞
arxiv.org/abs/2501.18965
Turns out that this behaviour can be described with a bound from *convex, nonsmooth* optimization.
A short thread on our latest paper 🚞
arxiv.org/abs/2501.18965
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training
We show that learning-rate schedules for large model training behave surprisingly similar to a performance bound from non-smooth convex optimization theory. We provide a bound for the constant schedul...
arxiv.org
February 5, 2025 at 10:13 AM
Learning rate schedules seem mysterious? Why is the loss going down so fast during cooldown?
Turns out that this behaviour can be described with a bound from *convex, nonsmooth* optimization.
A short thread on our latest paper 🚞
arxiv.org/abs/2501.18965
Turns out that this behaviour can be described with a bound from *convex, nonsmooth* optimization.
A short thread on our latest paper 🚞
arxiv.org/abs/2501.18965
Reposted by Alex Hägele
Hi there 👋 Happy to join Bluesky!
We are the EPFL AI Center - EPFL's hub for artificial intelligence, shaping a future where AI works for everyone through cutting-edge research, education, and collaborations the private and public sector.
ai.epfl.ch
We are the EPFL AI Center - EPFL's hub for artificial intelligence, shaping a future where AI works for everyone through cutting-edge research, education, and collaborations the private and public sector.
ai.epfl.ch
November 22, 2024 at 9:13 AM
Hi there 👋 Happy to join Bluesky!
We are the EPFL AI Center - EPFL's hub for artificial intelligence, shaping a future where AI works for everyone through cutting-edge research, education, and collaborations the private and public sector.
ai.epfl.ch
We are the EPFL AI Center - EPFL's hub for artificial intelligence, shaping a future where AI works for everyone through cutting-edge research, education, and collaborations the private and public sector.
ai.epfl.ch