Our work on how LLMs store relations selected as NeurIPS Spotlight paper

Our paper The Structure of Relation Decoding Linear Operators in Large Language Models by Miranda Anna Christ, Adrián Csiszárik, Gergely Becsó, and Dániel Varga was accepted at the NeurIPS 2025 conference as a Spotlight paper (~3% of submissions).

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Healthcare

Prevention & Prediction

Patient Pathways

Patient Pathway Mission

Leveraging Hungary’s healthcare data assets for prevention, prediction, and decision support.

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The table

All Blog posts Publications Events Grants Announcements

Piercing the chessboard

Publication

July 13, 2023

publications

We consider the minimum number of lines $h_n$ and $p_n$ needed to intersect or pierce, respectively, all the cells of the $n \times n$ chessboard. Determining these values can also be interpreted as a strengthening of the classical plank problem for integer points. Using the symmetric plank theorem of K. Ball, we prove that $h = \lceil \frac{n}{2} \rceil$ for each $n \leq 1$. Studying the piercing problem, we show that $0.

July 13, 2023

On higher order Fourier analysis in characteristic p

Publication

January 27, 2023

publications

In this paper, the nilspace approach to higher-order Fourier analysis is developed in the setting of vector spaces over a prime field $\mathbb{F}_p$, with applications mainly in ergodic theory. A key requisite for this development is to identify a class of nilspaces adequate for this setting. We introduce such a class, whose members we call $p$-homogeneous nilspaces. One of our main results characterizes these objects in terms of a simple algebraic property.

January 27, 2023

Regularity and inverse theorems for uniformity norms on compact abelian groups and nilmanifolds

Publication

August 1, 2022

publications

We prove a general form of the regularity theorem for uniformity norms, and deduce an inverse theorem for these norms which holds for a class of compact nilspaces including all compact abelian groups, and also nilmanifolds; in particular we thus obtain the first non-abelian versions of such theorems. We derive these results from a general structure theorem for cubic couplings, thereby unifying these results with the Host–Kra Ergodic Structure Theorem. A unification of this kind had been propounded as a conceptual prospect by Host and Kra.

August 1, 2022

Negative Sampling in Variational Autoencoders

Publication

May 16, 2022

publications

Modern deep artificial neural networks have achieved great success in the domain of computer vision and beyond. However, their application to many real-world tasks is undermined by certain limitations, such as overconfident uncertainty estimates on out-of-distribution data or performance deterioration under data distribution shifts. Several types of deep learning models used for density estimation through probabilistic generative modeling have been shown to fail to detect out-of-distribution samples by assigning higher likelihoods to anomalous data.

May 16, 2022

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The Team

The AI group at the institute brings together experts with backgrounds in both industry and academia. We place equal emphasis on theoretical foundations, thorough experimentation, and practical applications. Our close collaboration ensures a continuous exchange of knowledge between scientific research and applied projects.

All Theory Applications Former members

Balázs Szegedy

Mathematical Theory

Attila Börcs, PhD

NLP, Modeling, MLOps

Adrián Csiszárik

Representation Learning, Foundations

Győző Csóka

NLP, MLOps

Domonkos Czifra

NLP, Foundations

Botond Forrai

Modeling

Péter Kőrösi-Szabó

Modeling

Gábor Kovács

NLP, Modeling

Judit Laki, MD PhD

Healthcare

Márton Muntag

Time Series, NLP, Modeling

Dávid Terjék

Generalization, Mathematical Theory

Dániel Varga

Foundations, Computer aided proofs

Pál Zsámboki

Reinforcement Learning, Geometric Deep Learning

Zsolt Zombori

Formal Reasoning

Péter Ágoston

Combinatory, Geometry

Beatrix Mária Benkő

Representation Learning

Jakab Buda

NLP

Diego González Sánchez

Generalization, Mathematical Theory

Melinda F. Kiss

Representation Learning

Ákos Matszangosz

Topology, Foundations

Alex Olár

Foundations

Gergely Papp

Modeling

Open Positions

The Rényi AI group is actively recruiting both theorists and practitioners.

Machine Learning Engineer

Announcement: December 1, 2023

Deadline: rolling

applications

Rényi Institute is seeking Machine Learning Engineers to join our AI Research & Development team. Preferred Qualifications: • MLOps experience (especially in cloud environments) • Industry experience working on ML solutions

Research Scientist

Announcement: December 1, 2023

Deadline: rolling

theory, applications

Rényi Institute is seeking Research Scientists to join our AI Research & Development team. You will have the privilege to work at a renowned academic institute and do what you love: do research and publish in the field of machine learning / deep learning.

All Positions

Rényi AI - Building bridges between mathematics and artificial intelligence.