On my way to Montreal for COLM. Let me know if you’re also coming! I’d be very happy to catch up!

We present our poster at #1013 in the Wednesday morning session.

Joint work with the amazing Ryo Bertolissi, @idoh.bsky.social, @arkrause.bsky.social.

Paper: arxiv.org/pdf/2410.05026

Joint work with the amazing @marbaga.bsky.social, @gmartius.bsky.social, @arkrause.bsky.social

We propose an algorithm that does this by actively maximizing expected information gain of the demonstrations, with a couple of tricks to estimate this quantity and mitigate forgetting.
Interestingly, this solution is viable even without any information about pre-training!

In our ICML paper, we study fine-tuning a generalist policy for multiple tasks. We ask, provided a pre-trained policy, how can we maximize multi-task performance with a minimal number of additional demonstrations?

📌 We are presenting a possible solution on Wed, 11am to 1.30pm at B2-B3 W-609!

Our method significantly improves accuracy (measured as perplexity) for large language models and achieves a new state-of-the-art on the Pile benchmark.

If you're interested in test-time training or active learning, come chat with me at our poster session!

We introduce SIFT, a novel data selection algorithm for test-time training of language models. Unlike traditional nearest neighbor methods, SIFT uses uncertainty estimates to select maximally informative data, balancing relevance & diversity.

Paper: arxiv.org/pdf/2410.08020

✨ Very excited to share that our work "Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs" will be presented at ICLR! ✨

🗓️ Wednesday, April 23rd, 7:00–9:30 p.m. PDT
📍 Hall 3 + Hall 2B #257

Joint work with my fantastic collaborators Sascha Bongni,
@idoh.bsky.social, @arkrause.bsky.social

We've released our lecture notes for the course Probabilistic AI at ETH Zurich, covering uncertainty in ML and its importance for sequential decision making. Thanks a lot to @jonhue.bsky.social for his amazing effort and to everyone who contributed! We hope this resource is useful to you!

Unfortunately not as of now. We may also release Jupyter notebooks in the future, but this may take some time.

I'm glad you find this resource useful Maximilian!

Noted. Thanks for the suggestion!

Very glad to hear that they’ve been useful to you! :)

table of contents:

Huge thanks to the countless people that helped in the process of bringing this resource together!

I'm very excited to share notes on Probabilistic AI that I have been writing with @arkrause.bsky.social 🥳

arxiv.org/pdf/2502.05244

These notes aim to give a graduate-level introduction to probabilistic ML + sequential decision-making.
I'm super glad to be able to share them with all of you now!

Overfitting, as it is colloquially described in data science and machine learning, doesn’t exist. www.argmin.net/p/thou-shalt...

The slides for my lectures on (Bayesian) Active Learning, Information Theory, and Uncertainty are online now 🥳 They cover quite a bit from basic information theory to some recent papers:

blackhc.github.io/balitu/

and I'll try to add proper course notes over time 🤗

Preprint: arxiv.org/pdf/2410.08020

Tomorrow I’ll be presenting our recent work on improving LLMs via local transductive learning in the FITML workshop at NeurIPS.
Join us for our ✨oral✨ at 10:30am in east exhibition hall A.

Joint work with my fantastic collaborators Sascha Bongni, @idoh.bsky.social, @arkrause.bsky.social

Paper: arxiv.org/pdf/2402.158...
Virtual poster: neurips.cc/virtual/2024...

We’re presenting our work “Transductive Active Learning: Theory and Applications” now at NeurIPS. Come join us in East at poster #4924!

Joint work with my fantastic collaborators Bhavya Sukhija, Lenart Treven, Yarden As, @arkrause.bsky.social

Assume that the nodes of a social network can choose between two alternative technologies: B and X.
A node using B receives a benefit with respect to X, but there is a benefit to using the same tech as the majority of your neighbors.
Assume everyone uses X at time t=0. Will they switch to B?