1 citations · 2 across the 4 of their papers we have counts for
3 papers · 1 filter
AI4SLT: Empirical Processes in Lean 4 for Formal Statistical Learning Theory
Yuanhe Zhang, Jason D. Lee, Fanghui Liu
We present the first comprehensive Lean 4 formalization of statistical learning theory (SLT) grounded in empirical process theory. Our en-to-end formal infrastructure implement the…
Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining
Yunwei Ren, Yatin Dandi, Florent Krzakala +1
The empirical success of deep learning is often attributed to deep networks' ability to exploit hierarchical structure in data, constructing increasingly complex features across la…
Scaling Laws in Linear Regression: Compute, Parameters, and Data
Licong Lin, Jingfeng Wu, Sham M. Kakade +2
Empirically, large-scale deep learning models often satisfy a neural scaling law: the test error of the trained model improves polynomially as the model size and data size grow. Ho…