Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity

9 March 2020

Papers citing "Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity"

8 / 8 papers shown

Title
The Power of Random Features and the Limits of Distribution-Free Gradient Descent Ari Karchmer Eran Malach 28 0 0 15 May 2025
Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck Benjamin L. Edelman Surbhi Goel Sham Kakade Eran Malach Cyril Zhang 50 8 0 07 Sep 2023
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit Boaz Barak Benjamin L. Edelman Surbhi Goel Sham Kakade Eran Malach Cyril Zhang 44 124 0 18 Jul 2022
Random Feature Amplification: Feature Learning and Generalization in Neural Networks Spencer Frei Niladri S. Chatterji Peter L. Bartlett MLT 35 29 0 15 Feb 2022
Quantum machine learning beyond kernel methods Sofiene Jerbi Lukas J. Fiderer Hendrik Poulsen Nautrup Jonas M. Kubler Hans J. Briegel Vedran Dunjko 19 162 0 25 Oct 2021
Reconstruction on Trees and Low-Degree Polynomials Frederic Koehler Elchanan Mossel 35 9 0 14 Sep 2021
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks Eran Malach Gilad Yehudai Shai Shalev-Shwartz Ohad Shamir 26 20 0 31 Jan 2021
A case where a spindly two-layer linear network whips any neural network with a fully connected input layer Manfred K. Warmuth W. Kotłowski Ehsan Amid MLT 36 1 0 16 Oct 2020