Francesco Faccio
Francesco Faccio
The Swiss AI Lab IDSIA / USI & SUPSI, KAUST AI Initiative
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Cited by
Cited by
Policy optimization via importance sampling
AM Metelli, M Papini, F Faccio, M Restelli
Advances in Neural Information Processing Systems 31, 2018
Mindstorms in Natural Language-Based Societies of Mind
M Zhuge*, H Liu*, F Faccio*, DR Ashley*, R Csordás, A Gopalakrishnan, ...
NeurIPSW (Best Paper Award), 2023
Parameter-based value functions
F Faccio, L Kirsch, J Schmidhuber
ICLR 2021, 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
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
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
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
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
Learning to identify critical states for reinforcement learning from videos
H Liu, M Zhuge, B Li, Y Wang, F Faccio, B Ghanem, J Schmidhuber
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
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
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
Learning useful representations of recurrent neural network weight matrices
V Herrmann, F Faccio, J Schmidhuber
ICML 2024, 2024
Cross-attention makes inference cumbersome in text-to-image diffusion models
W Zhang, H Liu, J Xie, F Faccio, MZ Shou, J Schmidhuber
arXiv preprint arXiv:2404.02747, 2024
Highway reinforcement learning
Y Wang, H Liu, M Strupl, F Faccio, Q Wu, X Tan, J Schmidhuber
Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms
M Alhakami, DR Ashley, J Dunham, F Faccio, E Feron, J Schmidhuber
arXiv preprint arXiv:2404.08093, 2024
Language Agents as Optimizable Graphs
M Zhuge, W Wang, L Kirsch, F Faccio, D Khizbullin, J Schmidhuber
ICML 2024, 2024
Reinforcement learning with general evaluators and generators of policies
F Faccio
PhD Thesis, 2024
Efficient Value Propagation with the Compositional Optimality Equation
P Piekos, A Ramesh, F Faccio, J Schmidhuber
GCRL Workshop, OpenReview. net, 2023
Continually Adapting Optimizers Improve Meta-Generalization
W Wang, L Kirsch, F Faccio, M Zhuge, J Schmidhuber
NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation …, 2023
A study of importance sampling techniques for policy optimization
Politecnico di Milano, 2017
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