Breaking the Activation Function Bottleneck through Adaptive Parameterization. - Sebastian Flennerhag; Hujun Yin; John A. Keane; Mark J. Elliot (NeurIPS, 2018)

Transferring Knowledge across Learning Processes. - Sebastian Flennerhag; Pablo Garcia Moreno; Neil D. Lawrence; Andreas C. Damianou (ICLR, 2019)

Augmenting correlation structures in spatial data using deep generative models. - Konstantin Klemmer; Adriano S. Koshiyama; Sebastian Flennerhag (CoRR, 2019)

Meta-Learning with Warped Gradient Descent. - Sebastian Flennerhag; Andrei A. Rusu; Razvan Pascanu; Hujun Yin; Raia Hadsell (CoRR, 2019)

Meta-Learning with Warped Gradient Descent. - Sebastian Flennerhag; Andrei A. Rusu; Razvan Pascanu; Francesco Visin; Hujun Yin; Raia Hadsell (ICLR, 2020)

QuantNet: Transferring Learning Across Systematic Trading Strategies. - Adriano S. Koshiyama; Sebastian Flennerhag; Stefano B. Blumberg; Nick Firoozye; Philip C. Treleaven (CoRR, 2020)

Temporal Difference Uncertainties as a Signal for Exploration. - Sebastian Flennerhag; Jane X. Wang; Pablo Sprechmann; Francesco Visin; Alexandre Galashov; Steven Kapturowski; Diana L. Borsa; Nicolas Heess; André Barreto 001; Razvan Pascanu (CoRR, 2020)

Discovering Diverse Nearly Optimal Policies withSuccessor Features. - Tom Zahavy; Brendan O'Donoghue; André Barreto 001; Volodymyr Mnih; Sebastian Flennerhag; Satinder Singh 001 (CoRR, 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)

Meta-Gradients in Non-Stationary Environments. - Jelena Luketina; Sebastian Flennerhag; Yannick Schroecker; David Abel; Tom Zahavy; Satinder Singh 001 (CoLLAs, 2022)

Probing Transfer in Deep Reinforcement Learning without Task Engineering. - Andrei Alex Rusu; Sebastian Flennerhag; Dushyant Rao; Razvan Pascanu; Raia Hadsell (CoLLAs, 2022)

Bootstrapped Meta-Learning. - Sebastian Flennerhag; Yannick Schroecker; Tom Zahavy; Hado van Hasselt; David Silver; Satinder Singh 001 (ICLR, 2022)

Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization. - Robert Tjarko Lange; Tom Schaul; Yutian Chen 001; Chris Lu 001; Tom Zahavy; Valentin Dalibard; Sebastian Flennerhag (GECCO, 2023)

Discovering Evolution Strategies via Meta-Black-Box Optimization. - Robert Tjarko Lange; Tom Schaul; Yutian Chen 001; Tom Zahavy; Valentin Dalibard; Chris Lu 001; Satinder Singh 001; Sebastian Flennerhag (GECCO Companion, 2023)

Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality. - Tom Zahavy; Yannick Schroecker; Feryal M. P. Behbahani; Kate Baumli; Sebastian Flennerhag; Shaobo Hou; Satinder Singh 001 (ICLR, 2023)

ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. - Ted Moskovitz; Brendan O'Donoghue; Vivek Veeriah; Sebastian Flennerhag; Satinder Singh 001; Tom Zahavy (ICML, 2023)

Optimistic Meta-Gradients. - Sebastian Flennerhag; Tom Zahavy; Brendan O'Donoghue; Hado Philip van Hasselt; András György 001; Satinder Singh 001 (NeurIPS, 2023)

Optimism and Adaptivity in Policy Optimization. - Veronica Chelu; Tom Zahavy; Arthur Guez; Doina Precup; Sebastian Flennerhag (CoRR, 2023)

Vision-Language Models as a Source of Rewards. - Kate Baumli; Satinder Baveja; Feryal M. P. Behbahani; Harris Chan; Gheorghe Comanici; Sebastian Flennerhag; Maxime Gazeau; Kristian Holsheimer; Dan Horgan; Michael Laskin; Clare Lyle; Hussain Masoom; et al. (CoRR, 2023)