| Policy optimization via importance sampling AM Metelli, M Papini, F Faccio, M Restelli Advances in Neural Information Processing Systems 31, 2018 | 94 | 2018 |
| Parameter-based value functions F Faccio, L Kirsch, J Schmidhuber ICLR 2021, 2020 | 18 | 2020 |
| Goal-conditioned generators of deep policies F Faccio*, V Herrmann*, A Ramesh, L Kirsch, J Schmidhuber Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7503-7511, 2023 | 8 | 2023 |
| Bayesian brains and the Rényi divergence N Sajid*, F Faccio*, L Da Costa, T Parr, J Schmidhuber, K Friston Neural Computation 34 (4), 829-855, 2022 | 8 | 2022 |
| General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States F Faccio, A Ramesh, V Herrmann, J Harb, J Schmidhuber Decision Awareness in Reinforcement Learning Workshop at ICML 2022, 2022 | 6 | 2022 |
| Neural differential equations for learning to program neural nets through continuous learning rules K Irie, F Faccio, J Schmidhuber Advances in Neural Information Processing Systems 35, 38614-38628, 2022 | 5 | 2022 |
| Mindstorms in Natural Language-Based Societies of Mind M Zhuge*, H Liu*, F Faccio*, DR Ashley*, R Csordás, A Gopalakrishnan, ... arXiv preprint arXiv:2305.17066, 2023 | 3 | 2023 |
| Upside-down reinforcement learning can diverge in stochastic environments with episodic resets M Štrupl, F Faccio, DR Ashley, J Schmidhuber, RK Srivastava arXiv preprint arXiv:2205.06595, 2022 | 3 | 2022 |
| Highway Reinforcement Learning Y Wang, H Liu, M Strupl, F Faccio, Q Wu, X Tan, J Schmidhuber | 1 | 2022 |
| Reward-Weighted Regression Converges to a Global Optimum M Štrupl*, F Faccio*, DR Ashley, RK Srivastava, J Schmidhuber Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8361-8369, 2022 | 1 | 2022 |
| The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute A Stanić, D Ashley, O Serikov, L Kirsch, F Faccio, J Schmidhuber, ... arXiv preprint arXiv:2309.11197, 2023 | | 2023 |
| Learning to Identify Critical States for Reinforcement Learning from Videos H Liu, M Zhuge, B Li, Y Wang, F Faccio, B Ghanem, J Schmidhuber arXiv preprint arXiv:2308.07795, 2023 | | 2023 |
| IDSIA/GoGePo: Official repository for the paper" Goal-Conditioned Generators of Deep Policies" F Faccio, V Herrmann, A Ramesh, L Kirsch, J Schmidhuber Github, 2022 | | 2022 |
| IDSIA/neuraldiffeq-fwp: Official repository for the paper" Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules"(NeurIPS 2022) K Irie, F Faccio, J Schmidhuber Github, 2022 | | 2022 |
| A study of importance sampling techniques for policy optimization F FACCIO Italy, 2018 | | 2018 |
Juergen SchmidhuberKing Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of LuganoVerified email at kaust.edu.sa
