cognitive control (1)
compositionality (1)
continual learning (3)
curriculum learning (4)
data imbalance (2)
epidemic mitigation (1)
fairness (2)
fatigue (1)
landscape (6)
lottery ticket hypothesis (1)
momentum (1)
optimal control (1)
optimisation (7)
reinforcement learning (1)
review (1)
spurious correlations (1)
transfer learning (2)
cognitive control (1)
A meta-learning framework for rationalizing cognitive fatigue in neural systems
Yujun Li, Rodrigo Carrasco-Davis, Younes Strittmatter, Stefano Sarao Mannelli, Sebastian Musslick
CogSci 2024 (Oral)
compositionality (1)
Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning
Jin Hwa Lee, Stefano Sarao Mannelli, Andrew Saxe
ICML 2024
continual learning (3)
Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation
Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath, Sebastian Goldt, Andrew Saxe
ICML 2022
A meta-learning framework for rationalizing cognitive fatigue in neural systems
Yujun Li, Rodrigo Carrasco-Davis, Younes Strittmatter, Stefano Sarao Mannelli, Sebastian Musslick
CogSci 2024 (Oral)
Optimal Protocols for Continual Learning via Statistical Physics and Control Theory
Francesco Mori, Stefano Sarao Mannelli, Francesca Mignacco
curriculum learning (4)
An Analytical Theory of Curriculum Learning in Teacher-Student Networks
Luca Saglietti*, Stefano Sarao Mannelli*, Andrew Saxe
NeurIPS 2022
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions
Nishil Patel, Sebastian Lee, Stefano Sarao Mannelli, Sebastian Goldt, Andrew Saxe
Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning
Jin Hwa Lee, Stefano Sarao Mannelli, Andrew Saxe
ICML 2024
Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks
Stefano Sarao Mannelli, Yaraslau Ivashinka, Andrew Saxe, Luca Saglietti
ICML 2024
data imbalance (2)
Bias-inducing geometries: an exactly solvable data model with fairness implications
Stefano Sarao Mannelli, Federica Gerace, Negar Rostamzadeh, Luca Saglietti
Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training
Anchit Jain, Rozhin Nobahari, Aristide Baratin, Stefano Sarao Mannelli
Accepted to NeurIPS 2024
epidemic mitigation (1)
Epidemic mitigation by statistical inference from contact tracing data
Antoine Baker, Indaco Biazzo, Alfredo Braunstein, Giovanni Catania, Luca Dall’Asta, Alessandro Ingrosso, Florent Krzakala, Fabio Mazza, Marc Mezard, Anna Paola Muntoni, Maria Refinetti, Stefano Sarao Mannelli, Lenka Zdeborova
Proceedings of the National Academy of Sciences
fairness (2)
Bias-inducing geometries: an exactly solvable data model with fairness implications
Stefano Sarao Mannelli, Federica Gerace, Negar Rostamzadeh, Luca Saglietti
Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training
Anchit Jain, Rozhin Nobahari, Aristide Baratin, Stefano Sarao Mannelli
Accepted to NeurIPS 2024
fatigue (1)
A meta-learning framework for rationalizing cognitive fatigue in neural systems
Yujun Li, Rodrigo Carrasco-Davis, Younes Strittmatter, Stefano Sarao Mannelli, Sebastian Musslick
CogSci 2024 (Oral)
landscape (6)
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborova
ICML 2019
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Lenka Zdeborova
NeurIPS 2019 (Spotlight)
Marvels and pitfalls of the Langevin algorithm in noisy high-dimensional inference
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborova
Physical Review X
Thresholds of descending algorithms in inference problems
Stefano Sarao Mannelli, Lenka Zdeborova
Journal of Statistical Mechanics: Theory and Experiment
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborova
NeurIPS 2020
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborova
NeurIPS 2020
lottery ticket hypothesis (1)
Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks
Stefano Sarao Mannelli, Yaraslau Ivashinka, Andrew Saxe, Luca Saglietti
ICML 2024
momentum (1)
Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems
Stefano Sarao Mannelli, Pierfrancesco Urbani
NeurIPS 2021
optimal control (1)
Optimal Protocols for Continual Learning via Statistical Physics and Control Theory
Francesco Mori, Stefano Sarao Mannelli, Francesca Mignacco
optimisation (7)
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborova
ICML 2019
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Lenka Zdeborova
NeurIPS 2019 (Spotlight)
Marvels and pitfalls of the Langevin algorithm in noisy high-dimensional inference
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborova
Physical Review X
Thresholds of descending algorithms in inference problems
Stefano Sarao Mannelli, Lenka Zdeborova
Journal of Statistical Mechanics: Theory and Experiment
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborova
NeurIPS 2020
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborova
NeurIPS 2020
Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems
Stefano Sarao Mannelli, Pierfrancesco Urbani
NeurIPS 2021
reinforcement learning (1)
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions
Nishil Patel, Sebastian Lee, Stefano Sarao Mannelli, Sebastian Goldt, Andrew Saxe
review (1)
Thresholds of descending algorithms in inference problems
Stefano Sarao Mannelli, Lenka Zdeborova
Journal of Statistical Mechanics: Theory and Experiment
spurious correlations (1)
Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training
Anchit Jain, Rozhin Nobahari, Aristide Baratin, Stefano Sarao Mannelli
Accepted to NeurIPS 2024
transfer learning (2)
Probing transfer learning with a model of synthetic correlated datasets
Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli, Andrew Saxe, Lenka Zdeborová
Machine Learning: Science and Technology
Optimal transfer protocol by incremental layer defrosting
Federica Gerace, Diego Doimo, Stefano Sarao Mannelli, Luca Saglietti, Alessandro Laio