Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks. - Patrik Reizinger; Bálint Gyires-Tóth (LOD, 2019)

Attention-Based Curiosity-Driven Exploration in Deep Reinforcement Learning. - Patrik Reizinger; Márton Szemenyei (ICASSP, 2020)

Handling Realistic Noise in Multi-Agent Systems with Self-Supervised Learning and Curiosity. - Márton Szemenyei; Patrik Reizinger (J. Artif. Intell. Soft Comput. Res., 2021)

Embrace the Gap: VAEs Perform Independent Mechanism Analysis. - Patrik Reizinger; Luigi Gresele; Jack Brady; Julius von Kügelgen; Dominik Zietlow; Bernhard Schölkopf; Georg Martius; Wieland Brendel; Michel Besserve (NeurIPS, 2022)

ima-vae. - Patrik Reizinger; Luigi Gresele; Jack Brady; Dominik Zietlow; Julius von Kügelgen; Michel Besserve; Georg Martius; Wieland Brendel; Bernhard Schölkopf (Zenodo, 2022)

nl-causal-representations. - Patrik Reizinger; Yash Sharma 001; Matthias Bethge; Bernhard Schölkopf; Ferenc Huszár; Wieland Brendel (Zenodo, 2022)

Jacobian-based Causal Discovery with Nonlinear ICA. - Patrik Reizinger; Yash Sharma 001; Matthias Bethge; Bernhard Schölkopf; Ferenc Huszár; Wieland Brendel (Trans. Mach. Learn. Res., 2023)

nl-causal-representations. - Patrik Reizinger; Yash Sharma 001; Matthias Bethge; Bernhard Schölkopf; Ferenc Huszár; Wieland Brendel (Zenodo, 2023)

An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. - Goutham Rajendran; Patrik Reizinger; Wieland Brendel; Pradeep Kumar Ravikumar (CLeaR, 2024)

Position: Understanding LLMs Requires More Than Statistical Generalization. - Patrik Reizinger; Szilvia Ujváry; Anna Mészáros; Anna Kerekes; Wieland Brendel; Ferenc Huszár (ICML, 2024)

Rule Extrapolation in Language Modeling: A Study of Compositional Generalization on OOD Prompts. - Anna Mészáros; Szilvia Ujváry; Wieland Brendel; Patrik Reizinger; Ferenc Huszar (NeurIPS, 2024)

Understanding LLMs Requires More Than Statistical Generalization. - Patrik Reizinger; Szilvia Ujváry; Anna Mészáros; Anna Kerekes; Wieland Brendel; Ferenc Huszár (CoRR, 2024)

Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts. - Anna Mészáros; Szilvia Ujváry; Wieland Brendel; Patrik Reizinger; Ferenc Huszár (CoRR, 2024)

InfoNCE: Identifying the Gap Between Theory and Practice. - Evgenia Rusak; Patrik Reizinger; Attila Juhos; Oliver Bringmann 001; Roland S. Zimmermann; Wieland Brendel (AISTATS, 2025)

Cross-Entropy Is All You Need To Invert the Data Generating Process. - Patrik Reizinger; Alice Bizeul; Attila Juhos; Julia E. Vogt; Randall Balestriero; Wieland Brendel; David A. Klindt (ICLR, 2025)

Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning. - Patrik Reizinger; Siyuan Guo; Ferenc Huszár; Bernhard Schölkopf; Wieland Brendel (ICLR, 2025)

From superposition to sparse codes: interpretable representations in neural networks. - David A. Klindt; Charles O'Neill; Patrik Reizinger; Harald Maurer; Nina Miolane (CoRR, 2025)

An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research. - Patrik Reizinger; Randall Balestriero; David A. Klindt; Wieland Brendel (CoRR, 2025)

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning. - Patrik Reizinger; Bálint Mucsányi; Siyuan Guo; Benjamin Eysenbach; Bernhard Schölkopf; Wieland Brendel (CoRR, 2025)

Estimating Treatment Effects with Independent Component Analysis. - Patrik Reizinger; Lester Mackey; Wieland Brendel; Rahul Krishnan (CoRR, 2025)