Ming Li

Statistical Learning Theory · with Kearns · 1987–1988

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Statistical Learning Theory — 6 citations into it

Whom Ming Li trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Leslie G. Valiant
2. Vladimir Naumovich Vapnik
3. Dana Angluin
4. Anselm Blumer
5. Andrzej Ehrenfeucht
6. Nick Littlestone

Who trusts Ming Li overall

1. Michael Kearns
2. Blaine Nelson
3. Vitaly Feldman
4. R. Rivest
5. Robert E. Schapire
6. Amit Daniely
7. Avrim Blum
8. Pavel Laskov
9. Shai Ben-David
10. Saeed Mahloujifar

Papers from Ming Li's perspective

Ordered by how strongly the researchers Ming Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. Learning Regular Sets from Queries and Counterexamples
2. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities
3. A theory of the learnable
4. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm
5. Occam's Razor
6. The Strength of Weak Learnability
7. A theory of the learnable
8. On the learnability of Boolean formulae
9. Convergence of stochastic processes
10. Pattern classification and scene analysis

Their papers

• Learning in the presence of malicious errors (1988)
• On the learnability of Boolean formulae (1987)