Storkey, Amos

27 publications

NeurIPS 2025 HyperMARL: Adaptive Hypernetworks for Multi-Agent RL Kale-ab Tessera, Arrasy Rahman, Amos Storkey, Stefano V. Albrecht

AISTATS 2024 Approximate Bayesian Class-Conditional Models Under Continuous Representation Shift Thomas L. Lee, Amos Storkey

CoLLAs 2024 Chunking: Continual Learning Is Not Just About Distribution Shift Thomas L Lee, Amos Storkey

ICLR 2024 DAM: Towards a Foundation Model for Forecasting Luke Nicholas Darlow, Qiwen Deng, Ahmed Hassan, Martin Asenov, Rajkarn Singh, Artjom Joosen, Adam Barker, Amos Storkey

NeurIPS 2024 Diffusion for World Modeling: Visual Details Matter in Atari Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce, François Fleuret

ICMLW 2024 Efficient Offline Reinforcement Learning: The Critic Is Critical Adam Jelley, Trevor McInroe, Sam Devlin, Amos Storkey

NeurIPS 2024 Einspace: Searching for Neural Architectures from Fundamental Operations Linus Ericsson, Miguel Espinosa, Chenhongyi Yang, Antreas Antoniou, Amos Storkey, Shay B. Cohen, Steven McDonagh, Elliot J. Crowley

ICML 2023 ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna Wardlaw, Grant Mair, Emanuele Trucco, Amos Storkey

AISTATS 2023 Adversarial Robustness of VAEs Through the Lens of Local Geometry Asif Khan, Amos Storkey

ICLR 2023 Contrastive Meta-Learning for Partially Observable Few-Shot Learning Adam Jelley, Amos Storkey, Antreas Antoniou, Sam Devlin

ICMLW 2023 Label Noise: Correcting a Correction Loss William Toner, Amos Storkey

NeurIPSW 2023 Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning Lukas Schäfer, Filippos Christianos, Amos Storkey, Stefano Albrecht

NeurIPSW 2022 Deep Class-Conditional Gaussians for Continual Learning Thomas L Lee, Amos Storkey

ECCV 2022 Prediction-Guided Distillation for Dense Object Detection Chenhongyi Yang, Mateusz Ochal, Amos Storkey, Elliot J. Crowley

ICML 2021 Better Training Using Weight-Constrained Stochastic Dynamics Benedict Leimkuhler, Tiffany J Vlaar, Timothée Pouchon, Amos Storkey

ICLRW 2021 How Sensitive Are Meta-Learners to Dataset Imbalance? Mateusz Ochal, Massimiliano Patacchiola, Jose Manuel Vazquez Diosdado, Amos Storkey, Sen Wang

ICML 2021 Neural Architecture Search Without Training Joe Mellor, Jack Turner, Amos Storkey, Elliot J Crowley

ICLR 2020 BlockSwap: Fisher-Guided Block Substitution for Network Compression on a Budget Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos Storkey, Gavin Gray

ICLRW 2020 Comparing Recurrent and Convolutional Neural Networks for Predicting Wave Propagation Stathi Fotiadis, Eduardo Pignatelli, Mario Lino Valencia, Chris Cantwell, Amos Storkey, Anil A. Bharath

ICLR 2019 Exploration by Random Network Distillation Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov

ICLR 2019 How to Train Your MAML Antreas Antoniou, Harrison Edwards, Amos Storkey

ICLR 2019 Large-Scale Study of Curiosity-Driven Learning Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, Alexei A. Efros

ICLR 2019 On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey

ICML 2015 Training Deep Convolutional Neural Networks to Play Go Christopher Clark, Amos Storkey

ICML 2014 Multi-Period Trading Prediction Markets with Connections to Machine Learning Jinli Hu, Amos Storkey

AISTATS 2012 Discriminative Mixtures of Sparse Latent Fields for Risk Management Felix Agakov, Peter Orchard, Amos Storkey

AISTATS 2011 Machine Learning Markets Amos Storkey