Statistical Mechanics of Learning : Generalization

By Manfred Opper. 2002

Subfields: Scaling & Learning Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Scaling & Learning Theory

1. Tristan Yang
2. Percy Liang
3. Boaz Barak
4. Ilya Sutskever
5. Preetum Nakkiran
6. Alia Abbara
7. Shiori Sagawa
8. Lenka Zdeborov'a

Citations in the corpus

• Deep double descent: where bigger models and more data hurt (2019, positive): “Similar behavior had been observed in Opper (1995; 2001 Mei & Montanari (2019).”
• Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning (2019, background): “The main interest of such settings is to use the analysis of typical case problems with synthetic data created from simple generative models as means of getting additional insight on real world applications where data are not worst case ([5, 6, 7, 8, 14]).”
• An Investigation of Why Overparameterization Exacerbates Spurious Correlations (2020, methodology): “This behavior has been observed empirically (Belkin et al., 2019; Opper, 1995; Advani & Saxe, 2017; Nakkiran et al., 2019) and shown analytically in highdimensional regression (Hastie et al., 2019; Bartlett et al., 2019; Mei & Montanari, 2019).”