I'm feeling grateful to colleagues, students, collaborators, and everyone who joined the talk – and excited about the next steps in research on machines that learn, and maybe one day, truly make sense. 🙏✨
4/n

My own research, together with my group, focuses less on building the giant models and more on designing the building blocks behind them: model components, inductive biases, training principles, and inference methods that make AI systems more robust, data-efficient, and uncertainty-aware.
3/n

I talked about "Making Sense of Learning Machines":
• How modern machine learning has learned to cope with natural, “chaotic” data – images, text, sound
• Why the big breakthroughs of the last 10–15 years matter
• What we lack and what we would like to understand
2/n

Yes. The easiest way to find it will be on the website virtual.aistats.org We are in the process of adding material there and will add a link.

We will go public with it as soon as everything is set up with the venue.

I'm thrilled to be Program Chairing AISTATS 2026 together with Aaditya Ramdas. AISTATS has a special feel to it, and it has been described by many colleagues as their "favourite conference". We aim to preserve that spirit while introducing some fresh elements for 2026. [3/3]

Accepted papers will be presented in person in Morocco, May 2–5, 2026. The full Call for Papers is available here: virtual.aistats.org/Conferences/... [2/3]

Check our #CVPR paper and project page for more results, videos, and code!
📄 arxiv.org/abs/2411.19756
🎈 aaltoml.github.io/desplat/

Qualitative visualization of static distractor elements achieved by our model, DeSplat. [3/n]

Compared to Splatfacto we model and can ignore distractors to improve 3DGS reconstruction quality. [2/n]

Our method addresses the eminent question of probabilistic modelling in quantized large-scale ML models. See the workshop paper below. [3/3]

📄 Paper: openreview.net/forum?id=Sai...

We introduce BitVI, a novel approach for variational inference with discrete bitstring representations of continuous parameters. We use a deterministic probabilistic circuit structure to model the distribution over bitstrings, allowing for exact and efficient probabilistic inference. [2/3]

We show that externalising reasoning as a DAG at test time leads to more accurate, efficient multi-hop retrieval – and integrates seamlessly with RAG systems like Self-RAG.
📄 Paper: openreview.net/pdf?id=gi9aq...
3/3

This work was born out of Prakhar's internship with Microsoft Research (\w Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin, Nagarajan Natarajan, and Amit Sharma).
2/3