von Oswald, Johannes

19 publications

ICLR 2025 Learning Randomized Algorithms with Transformers Johannes von Oswald, Seijin Kobayashi, Yassir Akram, Angelika Steger

ICLR 2025 Multi-Agent Cooperation Through Learning-Aware Policy Gradients Alexander Meulemans, Seijin Kobayashi, Johannes von Oswald, Nino Scherrer, Eric Elmoznino, Blake Aaron Richards, Guillaume Lajoie, Blaise Aguera y Arcas, Joao Sacramento

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 Modular Solutions That Generalize Compositionally Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wolczyk, Alexandra Maria Proca, Johannes von Oswald, Razvan Pascanu, Joao Sacramento, Angelika Steger

ICMLW 2024 Efficient Linear System Solver with Transformers Max Vladymyrov, Johannes von Oswald, Nolan Andrew Miller, Mark Sandler

NeurIPS 2024 Linear Transformers Are Versatile In-Context Learners Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge

ICMLW 2024 Linear Transformers Are Versatile In-Context Learners Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge

NeurIPS 2024 Weight Decay Induces Low-Rank Attention Layers Seijin Kobayashi, Yassir Akram, Johannes von Oswald

ICML 2023 Transformers Learn In-Context by Gradient Descent Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, Joao Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov

NeurIPS 2022 A Contrastive Rule for Meta-Learning Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, João Sacramento

NeurIPS 2022 Disentangling the Predictive Variance of Deep Ensembles Through the Neural Tangent Kernel Seijin Kobayashi, Pau Vilimelis Aceituno, Johannes von Oswald

NeurIPSW 2022 Random Initialisations Performing Above Chance and How to Find Them Frederik Benzing, Simon Schug, Robert Meier, Johannes von Oswald, Yassir Akram, Nicolas Zucchet, Laurence Aitchison, Angelika Steger

NeurIPS 2022 The Least-Control Principle for Local Learning at Equilibrium Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento

ICLR 2021 Continual Learning in Recurrent Neural Networks Benjamin Ehret, Christian Henning, Maria Cervera, Alexander Meulemans, Johannes von Oswald, Benjamin F Grewe

ICLRW 2021 Learning Where to Learn Dominic Zhao, Nicolas Zucchet, Joao Sacramento, Johannes von Oswald

NeurIPS 2021 Learning Where to Learn: Gradient Sparsity in Meta and Continual Learning Johannes von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento

ICLR 2021 Neural Networks with Late-Phase Weights Johannes von Oswald, Seijin Kobayashi, Joao Sacramento, Alexander Meulemans, Christian Henning, Benjamin F Grewe

NeurIPS 2021 Posterior Meta-Replay for Continual Learning Christian Henning, Maria Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F. Grewe, João Sacramento

ICLR 2020 Continual Learning with Hypernetworks Johannes von Oswald, Christian Henning, João Sacramento, Benjamin F. Grewe