SKADA-Bench : Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities, has been published published in TMLR today 🚀. It was a huge team effort to design (and publish) an open source fully reproducible DA benchmark 🧵1/n. openreview.net/forum?id=k9F...

🚀 We are happy to organize the BERT²S workshop @neuripsconf.bsky.social 2025 on Recent Advances in Time Series Foundation Models.
🌐 berts-workshop.github.io
📜Submit by August 22
🎓Speakers and panelists: Chenghao Liu, Mingsheng Long, Zoe Piran, Danielle C. Maddix, Ameet Talwalkar, Qingsong Wen

Here is the recording with the slides for those interested!
🎤 youtu.be/UONvP1TL0-g?...
📊 drive.google.com/file/d/14ZIo...
📑 arxiv.org/pdf/2410.02724

@cohere.com @cohereforai.bsky.social

🚀 Very happy to be presenting Large Language Models as Markov Chains at Cohere Labs on June 19th at 6 pm CET (Paris time)!!

Huge thanks to Andrej Jovanović @cohere.com @cohereforai.bsky.social for the invitation 🤗

Paper: arxiv.org/pdf/2410.02724
Learn more: cohere.com/events/Coher...

Skada Sprint Alert: Contribute to Domain Adaptation in Python

📖 Machine learning models often fail when the data distribution changes between training and testing. That’s where Domain Adaptation comes in — helping models stay reliable across domains.

🤗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!

🤗 Very happy to have (humbly) contributed to this work!

This is a collab with the usual open-source suspects from Inria, @polytechniqueparis.bsky.social and @univparissaclay.bsky.social.

Check it out if you are interested in open-source reproducible research 😇

🚀 Policy gradient methods like DeepSeek’s GRPO are great for finetuning LLMs via RLHF.

But what happens when we swap autoregressive generation for discrete diffusion, a rising architecture promising faster & more controllable LLMs?

Introducing SEPO !

📑 arxiv.org/pdf/2502.01384

🧵👇

🚀Proud to share our work on the training dynamics in Transformers with Wassim Bouaziz & @viviencabannes.bsky.social @Inria @MetaAI

📝Easing Optimization Paths arxiv.org/pdf/2501.02362 (accepted @ICASSP 2025 🥳)

📝Clustering Heads 🔥https://arxiv.org/pdf/2410.24050

🖥️ github.com/facebookrese...

1/🧵

Happy to see Disentangled In-Context Learning accepted at ICLR 2025 🥳

Make zero-shot reinforcement learning with LLMs go brrr 🚀

🖥️ github.com/abenechehab/...

📜 arxiv.org/pdf/2410.11711

Congrats Abdelhakim (abenechehab.github.io) for leading it, always fun working with nice and strong people 🤗

🎤Presenting our work on Unsupervised Accuracy Estimation at #NeurIPS2024 this week!

✋🏾Poster Session 4 West - on Thu. at 4:30 pm

📍 Poster #4310 - East Exhibit Hall A-C

DM me if you'd like to chat :)

Checkout the new version of this awesome domain adaptation library! So nice to work with such good people 🤗

🚨So, you want to predict your model's performance at test time?🚨

💡Our NeurIPS 2024 paper proposes 𝐌𝐚𝐍𝐨, a training-free and SOTA approach!

📑 arxiv.org/pdf/2405.18979
🖥️https://github.com/Renchunzi-Xie/MaNo

1/🧵(A surprise at the end!)

Anne Gagneux, Ségolène Martin, @quentinbertrand.bsky.social Remi Emonet and I wrote a tutorial blog post on flow matching: dl.heeere.com/conditional-... with lots of illustrations and intuition!

We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423

Check this out, a low-hanging fruit of our recent work « Large Language Models as Markov Chains » arxiv.org/pdf/2410.02724