Kirsch, Louis

29 publications

TMLR 2025 Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens Usman Anwar, Johannes von Oswald, Louis Kirsch, David Krueger, Spencer Frei

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

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

ICMLW 2024 Learning In-Context Decision Making with Synthetic MDPs Akarsh Kumar, Chris Lu, Louis Kirsch, Phillip Isola

ICML 2024 Sequence Compression Speeds up Credit Assignment in Reinforcement Learning Aditya Ramesh, Kenny John Young, Louis Kirsch, Jürgen Schmidhuber

NeurIPSW 2023 Continually Adapting Optimizers Improve Meta-Generalization Wenyi Wang, Louis Kirsch, Francesco Faccio, Mingchen Zhuge, Jürgen Schmidhuber

NeurIPSW 2023 Continually Adapting Optimizers Improve Meta-Generalization Wenyi Wang, Louis Kirsch, Francesco Faccio, Mingchen Zhuge, Jürgen Schmidhuber

NeurIPSW 2023 Discovering Temporally-Aware Reinforcement Learning Algorithms Matthew Thomas Jackson, Chris Lu, Louis Kirsch, Robert Tjarko Lange, Shimon Whiteson, Jakob Nicolaus Foerster

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

NeurIPSW 2023 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, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piękos, Aditya Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanić, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber

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

NeurIPSW 2023 Towards General-Purpose In-Context Learning Agents Louis Kirsch, James Harrison, C. Daniel Freeman, Jascha Sohl-Dickstein, Jürgen Schmidhuber

NeurIPSW 2023 Towards General-Purpose In-Context Learning Agents Louis Kirsch, James Harrison, C. Daniel Freeman, Jascha Sohl-Dickstein, Jürgen Schmidhuber

NeurIPSW 2023 Towards General-Purpose In-Context Learning Agents Louis Kirsch, James Harrison, C. Freeman, Jascha Sohl-Dickstein, Jürgen Schmidhuber

NeurIPSW 2023 Towards General-Purpose In-Context Learning Agents Louis Kirsch, James Harrison, C. Freeman, Jascha Sohl-Dickstein, Jürgen Schmidhuber

NeurIPS 2022 Exploring Through Random Curiosity with General Value Functions Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber

NeurIPSW 2022 General-Purpose In-Context Learning by Meta-Learning Transformers Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, Luke Metz

ICMLW 2022 Goal-Conditioned Generators of Deep Policies Francesco Faccio, Vincent Herrmann, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber

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

ICMLW 2022 Self-Referential Meta Learning Louis Kirsch, Jürgen Schmidhuber

NeurIPSW 2022 The Benefits of Model-Based Generalization in Reinforcement Learning Kenny John Young, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber

NeurIPSW 2021 Exploring Through Random Curiosity with General Value Functions Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber

NeurIPSW 2021 Introducing Symmetries to Black Box Meta Reinforcement Learning Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen

NeurIPSW 2021 Introducing Symmetries to Black Box Meta Reinforcement Learning Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen

NeurIPS 2021 Meta Learning Backpropagation and Improving It Louis Kirsch, Jürgen Schmidhuber

ICLR 2021 Parameter-Based Value Functions Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber

ICLR 2020 Improving Generalization in Meta Reinforcement Learning Using Learned Objectives Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber

NeurIPS 2018 Modular Networks: Learning to Decompose Neural Computation Louis Kirsch, Julius Kunze, David Barber

ECML-PKDD 2017 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