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ML in PL

ML in PL Association is a non-profit organization devoted to fostering the ML community in Poland and promoting a deep understanding of ML methods.

Even though ML in PL is based in Poland, it seeks to provide opportunities for international cooperation.

24/04/2026

Creating something new and putting it out into the world — three new recordings that approach that from very different angles.

🎓 Michał Gdak, Marianna Nezhurina & Marek Kozłowski — Panel: Open Models, Open Data
Not all openness is equal. This panel covers the full spectrum from released weights to fully open code and data, the license implications at each level, and what responsible public release actually requires: pre-release evaluation, documentation, and the standards that matter.

🎓 Mihaela van der Schaar — Unleashing Creativity using AI Agent Networks
Professor van der Schaar's lab builds ML models that interpret dynamical systems without traditional equations, and agent networks that can autonomously formulate and validate scientific hypotheses. The talk traces the evolution from individual AI agents toward networks that drive real scientific discovery.

🎓 Sander Dieleman — Diffusion Models for Image and Video Generation
A thorough walkthrough of every component needed to build a state-of-the-art image or video generation model based on latent diffusion. If you want to understand what's actually inside these systems, this is a good place to start.

Links in the comments 👇

23/04/2026

Reliability and safety remain central themes of MLSS R&S 2026, explored from multiple research perspectives. We’re pleased to introduce the final speaker contributing to this year’s programme.

🎤 Nikolay Malkin is a Chancellor's Fellow in Informatics at the University of Edinburgh and a fellow of CIFAR's Learning in Machines and Brains programme. Their research focuses on algorithms for probabilistic inference and Bayesian machine learning, with applications in generative modelling, neurosymbolic AI, and machine reasoning. Within machine learning, Nikolay's work explores modelling of Bayesian posteriors over high-dimensional and structured variables, induction and discovery of compositional structure in generative models, and neurosymbolic methods for uncertainty-aware reasoning in language and formal systems. Their work has found applications in pure and applied sciences, including inverse imaging, remote sensing, discovery of novel biological and chemical structures, and, most recently, robot control. Nikolay holds a PhD in mathematics from Yale University (2021) and was previously a postdoctoral researcher at Mila – Québec AI Institute in Montréal (2021 to 2024).

🎟 Late registration is open until May 11, with a limited number of spots remaining. Details in the first comment.

📍 Kraków, Poland
📅 June 29 – July 3, 2026

18/04/2026

Five days structured around lectures, discussions, and time to exchange ideas - with space for informal conversations, poster sessions, and social events in the evenings.

The programme focuses on reliability and safety of machine learning methods and systems.

Regular registration closes tomorrow (April 19, AoE). You can still apply!

📍 Kraków, Poland
📅 June 29 – July 3, 2026

Details in the first comment.

17/04/2026

Generative models are powerful. The harder questions are whether we understand what's happening inside them, whether we can control it, and whether we can trust them with sensitive data. These three recordings tackle exactly that.

🎓 Kamil Deja — SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
Most methods for removing unwanted concepts from diffusion models lack transparency. SAeUron uses sparse autoencoders trained on model activations to learn interpretable, concept-specific features — and intervenes precisely on those to block targeted content. It outperforms existing approaches on the UnlearnCanvas benchmark, handles multiple concepts with a single SAE, and holds up under adversarial attack.

🎓 Antoni Kowalczuk — Privacy Attacks on Image AutoRegressive Models
Image autoregressive models have quietly matched diffusion models on image quality while being faster to generate. The privacy risks, however, are significantly higher: Antoni's membership inference attack reaches 86.38% TPR at FPR=1%, compared to 6.38% for diffusion models. Dataset membership can be inferred from as few as 6 samples, and hundreds of training images can be directly extracted.

🎓 Łukasz Staniszewski — Controlling Generative Models through Parameter Localization
Less than 1% of a diffusion model's parameters govern its textual content in image generation. Building on an ICLR 2025 paper, Łukasz presents a unified framework for localizing and modulating these components across image, text, and audio models — enabling fine-grained editing, efficient fine-tuning, and control over musical attributes like tempo, instrumentation, and vocal style.

Links in the comments 👇

16/04/2026

As we approach the final days of registration, we’re introducing the last speaker completing the programme of MLSS on Reliability & Safety 2026.

🎤 Christian Schroeder de Witt is a Principal Investigator at the Oxford Witt Lab, Department of Engineering Science, University of Oxford. His research spans artificial intelligence, physics, and computer science, combining theoretical work with practical questions around the reliability and security of AI systems.

His recent work focuses on multi-agent security, a direction addressing key gaps in current AI safety research by studying worst-case guarantees in agentic systems. Within this area, he introduced the concept of undetectable threats, highlighting limitations of anomaly-detection-based approaches and motivating security-by-design. His recent papers—on secret collusion, illusory attacks, and unelicitable backdoors—have appeared at venues such as NeurIPS 2024 and ICLR 2024.

Earlier in his career, he contributed to deep multi-agent reinforcement learning and co-authored work solving a long-standing problem in information security (perfectly secure steganography).

🎟 Regular registration closes this Sunday (April 19 AOE) - these are the final days to join MLSS R&S 2026, with only a limited number of spots remaining. Details in the first comment.

📍 Kraków, Poland
📅 June 29 – July 3, 2026

15/04/2026

Meet the new Project Leaders of ML in PL Conference 2026: Ewelina Kędzior and Mikołaj Piórczyński.

Ewelina has been with ML in PL for three years, starting in Special Ops before moving into Visual Identity. She studied quantitative methods at SGH and computer science at SGGW, and now works as a Data Scientist at DS360, focusing on machine learning, econometric modeling, and optimization. Outside of work, she enjoys scouting and discovering new places.

Mikołaj has spent the past three years working on Call for Contributions, and now steps into the co-lead role. He’s currently finishing his Master’s in Data Science at Warsaw University of Technology while doing research at IDEAS Research Institute. These days, he’s deep in thesis mode. When the weather’s good, you’ll find him running or biking; when it’s not, he’s watching old movies under a blanket.

They’ve both grown within the organization, know it inside out, and are now taking responsibility for the conference as a whole.

14/04/2026

It's been a busy week for events and programs landing in Poland, but this one deserves a separate post.
SPRIND, the German federal agency for breakthrough innovation, is launching a €125M challenge to build three European Frontier AI Labs. The premise is worth reading twice: not to catch up on transformers, but to find the next S-curve entirely. SSMs, world models, neuro-symbolic approaches, new training regimes — or whatever architectural thesis you've been sitting on.
Up to 10 teams get selected. Funding is non-dilutive (zero equity taken). 24 months of milestone-based ex*****on. The three strongest teams get positioned to raise ~€1B each.
Applications open April 30th, deadline May 29th.
On April 22nd, the SPRIND team is in Warsaw for a working session — no keynotes, no pitch decks, just direct conversation with the people running the program. Organized together with IDEAS (Piotr Sankowski), PFR Ventures, and Startup Poland. If you're a researcher thinking about building your own lab, an engineer with a strong architectural bet, or looking for co-founders, this is a useful room to be in.
Full program details at next-frontier.ai
Warsaw registration at luma.com/nfai-warsaw

14/04/2026

A lot of research never leaves the lab, not because it isn't useful, but because nobody built the bridge. IDEASHACK 2026 is a hackathon about exactly that bridge.
Organised by the IDEAS Research Institute (ELLIS Unit Warsaw) under the Horizon Europe project ELIAS, it brings together teams of 3–5 from across Europe to design platforms that help companies discover and collaborate with researchers.
The format: online kick-off April 24, development phase through May, and a Demo Day & Grand Final in Warsaw on June 19 for the top 5 teams. Prizes range from €3,000 to €5,000.
Open to EEA, Swiss, and UK residents. Registration closes April 19.
If you know people who think about how science connects to industry (or should), send this their way.

13/04/2026

The quality of a research programme is shaped not only by speakers, but by the people in the room.
MLSS R&S 2026 is a five-day programme in Kraków focused on reliability and safety of machine learning methods and systems. The school brings together PhD students and researchers for invited lectures, discussions, and close interaction with speakers and participants.
As we approach the final week of registration, applications remain open until this Sunday (April 19, AoE).
Each edition is shaped not only by the programme, but by the group itself - often leading to in-depth discussions, new ideas, and ongoing collaborations.
If you know someone working in this area, this might be a good moment to share this with them.

📍 Kraków, Poland
📅 June 29 – July 3, 2026

Details in the first comment.

10/04/2026

We have a lot of empirical intuitions about how neural networks behave. These three talks ask what's actually underneath them — from the structure of the loss surface, to how networks fold space, to the geometry of representations. If any of that sounds familiar, the recordings are worth setting aside some time for.

🎓 Weronika Ormaniec — What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis
Why do Transformers almost always come with adaptive optimizers, layer norm, and learning rate warmup — while classical architectures don't? Weronika derives the full loss Hessian for a single self-attention layer and shows that most of these choices trace back to Transformers' highly non-linear, heterogeneous parameter dependencies. Presented at ICLR 2025.

🎓 Michał Lewandowski — On Space Folds by Neural Networks
Neural networks fold the input space during training — qualitatively, this has been observed before. Michał introduces an actual measure of this folding, links it to generalization capacity, and proposes a regularization scheme that encourages folding to happen earlier in training.

🎓 Nahid Torbati — Exploring Geometric Representational Alignment through Ollivier Ricci Curvature
How do you compare representations between humans and artificial neural networks when the standard Euclidean assumptions don't hold? Nahid brings in Ollivier-Ricci curvature from Riemannian geometry and applies it to face stimuli across VGG-Face, a human-aligned variant, and real human similarity judgments. The geometric approach reveals alignment differences that traditional methods miss.

Links in the comments 👇

09/04/2026

We’re excited to introduce another speaker at MLSS - and the first one who joins us in a double role as both a speaker and a member of the Scientific Committee

🎤 Bartosz Zieliński is an Associate Professor at the Jagiellonian University in Kraków and Director of the Jagiellonian Centre for Artificial Intelligence. He earned his Ph.D. from the Polish Academy of Sciences and his Habilitation from Wrocław University of Science and Technology, both in Computer Science. He is a member of ELLIS and a Research Team Leader at the Group of Machine Learning Research (GMUM). His work focuses on computer vision, interpretable machine learning, and embodied artificial intelligence.

We’re proud to have him contributing to MLSS in this dual role - both shaping the program and sharing his expertise with participants.

🎟 Regular registration closes on April 19 - don’t wait too long. Details in the first comment.

📍 Kraków, Poland
📅 June 29 – July 3, 2026

08/04/2026

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Everything happening inside the largest ML community in Poland, from meetups and job opportunities to honest insights from people in the field, straight to your inbox.

Link in the comments.

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