The following pages link to A theory of the learnable (Q3714486):
Displaying 17 items.
- Learning stability of partially observed switched linear systems (Q6550242) (← links)
- Compositional sparsity of learnable functions (Q6554713) (← links)
- Geometric problems in machine learning (Q6560225) (← links)
- A tighter generalization bound for reservoir computing (Q6561720) (← links)
- Analyzing robustness of Angluin's \(L^*\) algorithm in presence of noise (Q6563046) (← links)
- Realizable learning is all you need (Q6566462) (← links)
- Boosting simple learners (Q6566593) (← links)
- Reprint of: Some thoughts about transfer learning. What role for the source domain? (Q6577670) (← links)
- A statistical approach to learning constraints (Q6577673) (← links)
- Synergies between machine learning and reasoning -- an introduction by the Kay R. Amel group (Q6577680) (← links)
- Statistical computational learning (Q6602226) (← links)
- A direct PRF construction from Kolmogorov complexity (Q6637531) (← links)
- Computable PAC learning of continuous features (Q6649436) (← links)
- Structural lower bounds on black-box constructions of pseudorandom functions (Q6652977) (← links)
- Is ML-based cryptanalysis inherently limited? Simulating cryptographic adversaries via gradient-based methods (Q6652981) (← links)
- An algorithm for learning representations of models with scarce data (Q6660916) (← links)
- PACMAN: PAC-style bounds accounting for the mismatch between accuracy and negative log-loss (Q6663349) (← links)
