Framework for Exploring and Understanding Multivariate Correlations. - Louis Kirsch; Niklas Riekenbrauck; Daniel Thevessen; Marcus Pappik; Axel Stebner; Julius Kunze; Alexander Meissner; Arvind Kumar Shekar; Emmanuel Müller (ECML/PKDD (3), 2017)

Transfer Learning for Speech Recognition on a Budget. - Julius Kunze; Louis Kirsch; Ilia Kurenkov; Andreas Krug; Jens Johannsmeier; Sebastian Stober (Rep4NLP@ACL, 2017)

Modular Networks: Learning to Decompose Neural Computation. - Louis Kirsch; Julius Kunze; David Barber (NeurIPS, 2018)

Gaussian Mean Field Regularizes by Limiting Learned Information. - Julius Kunze; Louis Kirsch; Hippolyt Ritter; David Barber (Entropy, 2019)

Improving Generalization in Meta Reinforcement Learning using Learned Objectives. - Louis Kirsch; Sjoerd van Steenkiste; Jürgen Schmidhuber (ICLR, 2020)

Parameter-Based Value Functions. - Francesco Faccio; Louis Kirsch; Jürgen Schmidhuber (ICLR, 2021)

Meta Learning Backpropagation And Improving It. - Louis Kirsch; Jürgen Schmidhuber (NeurIPS, 2021)

Introducing Symmetries to Black Box Meta Reinforcement Learning. - Louis Kirsch; Sebastian Flennerhag; Hado van Hasselt; Abram L. Friesen; Junhyuk Oh; Yutian Chen 001 (AAAI, 2022)

Exploring through Random Curiosity with General Value Functions. - Aditya A. Ramesh; Louis Kirsch; Sjoerd van Steenkiste; Jürgen Schmidhuber (NeurIPS, 2022)

General-Purpose In-Context Learning by Meta-Learning Transformers. - Louis Kirsch; James Harrison; Jascha Sohl-Dickstein; Luke Metz (CoRR, 2022)

Eliminating Meta Optimization Through Self-Referential Meta Learning. - Louis Kirsch; Jürgen Schmidhuber (CoRR, 2022)

Goal-Conditioned Generators of Deep Policies. - Francesco Faccio; Vincent Herrmann; Aditya A. Ramesh; Louis Kirsch; Jürgen Schmidhuber (AAAI, 2023)

The Benefits of Model-Based Generalization in Reinforcement Learning. - Kenny John Young; Aditya A. Ramesh; Louis Kirsch; Jürgen Schmidhuber (ICML, 2023)

Learning One Abstract Bit at a Time Through Self-invented Experiments Encoded as Neural Networks. - Vincent Herrmann; Louis Kirsch; Jürgen Schmidhuber (IWAI, 2023)

Brain-inspired learning in artificial neural networks: a review. - Samuel Schmidgall; Jascha Achterberg; Thomas Miconi; Louis Kirsch; Rojin Ziaei; S. Pardis Hajiseyedrazi; Jason Eshraghian (CoRR, 2023)

The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute. - Aleksandar Stanic; Dylan R. Ashley; Oleg Serikov; Louis Kirsch; Francesco Faccio; Jürgen Schmidhuber; Thomas Hofmann 001; Imanol Schlag (CoRR, 2023)

Discovering Temporally-Aware Reinforcement Learning Algorithms. - Matthew Thomas Jackson; Chris Lu 001; Louis Kirsch; Robert Tjarko Lange; Shimon Whiteson; Jakob Nicolaus Foerster (ICLR, 2024)

Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning. - Aditya A. Ramesh; Kenny John Young; Louis Kirsch; Jürgen Schmidhuber (ICML, 2024)

GPTSwarm: Language Agents as Optimizable Graphs. - Mingchen Zhuge; Wenyi Wang; Louis Kirsch; Francesco Faccio; Dmitrii Khizbullin; Jürgen Schmidhuber (ICML, 2024)

Language Agents as Optimizable Graphs. - Mingchen Zhuge; Wenyi Wang; Louis Kirsch; Francesco Faccio; Dmitrii Khizbullin; Jürgen Schmidhuber (CoRR, 2024)

Adversarial Robustness of In-Context Learning in Transformers for Linear Regression. - Usman Anwar; Johannes von Oswald; Louis Kirsch; David Krueger 001; Spencer Frei (CoRR, 2024)

Automating the Search for Artificial Life with Foundation Models. - Akarsh Kumar; Chris Lu 001; Louis Kirsch; Yujin Tang; Kenneth O. Stanley; Phillip Isola; David Ha (CoRR, 2024)

Mindstorms in Natural Language-Based Societies of Mind. - Mingchen Zhuge; Haozhe Liu; Francesco Faccio; Dylan R. Ashley; Róbert Csordás; Anand Gopalakrishnan; Abdullah Hamdi; Hasan Abed Al Kader Hammoud; Vincent Herrmann; Kazuki Irie; Louis Kirsch; Bing Li 024; et al. (Comput. Vis. Media, 2025)

Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens. - Usman Anwar; Johannes von Oswald; Louis Kirsch; David Krueger 001; Spencer Frei (Trans. Mach. Learn. Res., 2025)