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

Ambroise Odonnat - Large Language Models as Markov Chains

YouTube video by Cohere

youtu.be

Ambroise Odonnat @ambroiseodt.bsky.social · last yr.

🚀 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...

🚀 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 😇

Théo Gnassounou@tgnassou.bsky.social · 2y ago

🚀 I’m pleased to announce a new preprint! "SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities" 📢 Check it out & contribute! 📜 Paper: arxiv.org/abs/2407.11676 💻 Code: github.com/scikit-adapt...

🚀 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 🤗

GitHub - abenechehab/dicl: Official implementation of DICL (Disentangled In-Context Learning), featured in the paper Zero-shot Model-based Reinforcement Learning using Large Language Models.

Official implementation of DICL (Disentangled In-Context Learning), featured in the paper Zero-shot Model-based Reinforcement Learning using Large Language Models. - abenechehab/dicl

github.com

🎤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 :)

Ambroise Odonnat @ambroiseodt.bsky.social · 2y ago

🚨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!)

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

Théo Gnassounou@tgnassou.bsky.social · 2y ago

🚀 Skada v0.4.0 is out! Skada is an open-source Python library built for domain adaptation (DA), helping machine learning models to adapt to distribution shifts. Github: github.com/scikit-adapt... Doc: scikit-adaptation.github.io DOI: doi.org/10.5281/zeno... Installation: `pip install skada`

🚨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

Oussama Zekri@ozekri.bsky.social · 2y ago

🚀 Did you know you can use the in-context learning abilities of an LLM to estimate the transition probabilities of a Markov chains? The results are pretty exciting ! 😄