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.

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.

✨ 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

table of contents:

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!