Arno Solin

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Statement from #AISTATS2026 organizers regarding the @openreview.bsky.social API Security Incident

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. 🙏✨
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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.
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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
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YouTube video by Aalto University Making sense of learning machines – Arno Solin

I recently gave my installation talk after being tenured. The video of the talk is now available on the university's YouTube channel: youtu.be/R1UQoflPTDg 1/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]

📣 Please share: We invite submissions to the 29th International Conference on Artificial Intelligence and Statistics (#AISTATS 2026) and welcome paper submissions at the intersection of AI, machine learning, statistics, and related areas. [1/3]

BitVI on 1D Gaussian mixture models.

Remember that computers use bitstrings to represent numbers? We exploit this in our recent @auai.org paper and introduce #BitVI.

#BitVI directly learns an approximation in the space of bitstring representations, thus, capturing complex distributions under varying numerical precision regimes.

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]

Real-world #3DGS scenes are messy—occluders, moving objects, and clutter often ruin reconstruction. This #CVPR2025 paper presents DeSplat, which separates static scene content from distractors, all without requiring external semantic models. [1/n]

I’m visiting the Isaac Newton Institute for Mathematical Sciences in Cambridge this week.

I’m giving an invited talk in the ”Calibrating prediction uncertainty : statistics and machine learning perspectives” workshop on Thursday.

Are Your Continuous Approximations Really Continuous? Reimagining... Efficiently performing probabilistic inference in large models is a significant challenge due to the high computational demands and continuous nature of the model parameters. At the same time, the...

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]

Have you thought that in computer memory model weights are given in terms of discrete values in any case. Thus, why not do probabilistic inference on the discrete (quantized) parameters. @trappmartin.bsky.social is presenting our work at #AABI2025 today. [1/3]

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...
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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).
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Excited to share "Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation", presented at the #ICLR2025 "Workshop on Reasoning and Planning for LLMs" on Monday! 🚀
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Our TMLR-to-ICLR poster "Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices" (Frida Viset, Anton Kullberg, Frederiek Wesel, Arno Solin)
🗓️ Hall 3 + Hall 2B #416, Fri 25 Apr 10 a.m. +08 — 12:30 p.m. +08
📄 Preprint: arxiv.org/abs/2408.02346

Our #ICLR2025 poster "Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models" (Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg)
🗓️ Hall 3 + Hall 2B #194, Fri 25 Apr 3 p.m. +08 — 5:30 p.m. +08
📄 Preprint: arxiv.org/abs/2405.17656

Our #ICLR2025 poster "Streamlining Prediction in Bayesian Deep Learning" (Rui Li · Marcus Klasson, Arno Solin, Martin Trapp)
🗓️ Hall 3 + Hall 2B #413, Fri 25 Apr 10 a.m. +08 — 12:30 p.m. +08
📄 Preprint: arxiv.org/abs/2411.18425

Our #ICLR2025 poster "Discrete Codebook World Models for Continuous Control" (Aidan Scannell, Mohammadreza Nakhaeinezhadfard, Kalle Kujanpää, Yi Zhao, Kevin Luck, Arno Solin, Joni Pajarinen)
🗓️ Hall 3 + Hall 2B #415, Thu 24 Apr 10 a.m. +08 — 12:30 p.m. +08
📄 Preprint: arxiv.org/abs/2503.00653

Our #ICLR2025 poster "Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs" (Severi Rissanen, Markus Heinonen, Arno Solin)
🗓️ Hall 3 + Hall 2B #140, Thu 24 Apr 3 p.m. +08 — 5:30 p.m. +08
📄 Preprint: arxiv.org/abs/2410.11149

Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices The Hilbert-space Gaussian Process (HGP) approach offers a hyperparameter-independent basis function approximation for speeding up Gaussian Process (GP) inference by projecting the GP onto M basis fun...

Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices
Frida Viset · Anton Kullberg · Frederiek Wesel · Arno Solin
Hall 3 + Hall 2B #416
🗓️ Fri 25 Apr 10 a.m. +08 — 12:30 p.m. +08
📄 arxiv.org/abs/2408.02346

Alignment is Key for Applying Diffusion Models to Retrosynthesis Retrosynthesis, the task of identifying precursors for a given molecule, can be naturally framed as a conditional graph generation task. Diffusion models are a particularly promising modelling approac...

Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
Najwa Laabid · Severi Rissanen · Markus Heinonen · Arno Solin · Vikas Garg
Hall 3 + Hall 2B #194
🗓️ Fri 25 Apr 3 p.m. +08 — 5:30 p.m. +08
📄 arxiv.org/abs/2405.17656