Learning theory for conditional risk minimization

A. Zimin, C. Lampert, in:, JMLR, Inc. and Microtome Publishing, 2017, pp. 213–222.

Conference Paper | Published | English
Department
Series Title
PMLR
Abstract
In this work we study the learnability of stochastic processes with respect to the conditional risk, i.e. the existence of a learning algorithm that improves its next-step performance with the amount of observed data. We introduce a notion of pairwise discrepancy between conditional distributions at different times steps and show how certain properties of these discrepancies can be used to construct a successful learning algorithm. Our main results are two theorems that establish criteria for learnability for many classes of stochastic processes, including all special cases studied previously in the literature.
Publishing Year
Date Published
2017-04-01
Volume
54
Page
213 - 222
Conference
AISTATS: Artificial Intelligence and Statistics
Conference Location
Fort Lauderdale, FL, United States
Conference Date
2017-04-20 – 2017-04-22
IST-REx-ID

Cite this

Zimin A, Lampert C. Learning theory for conditional risk minimization. In: Vol 54. JMLR, Inc. and Microtome Publishing; 2017:213-222.
Zimin, A., & Lampert, C. (2017). Learning theory for conditional risk minimization (Vol. 54, pp. 213–222). Presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States: JMLR, Inc. and Microtome Publishing.
Zimin, Alexander, and Christoph Lampert. “Learning Theory for Conditional Risk Minimization,” 54:213–22. JMLR, Inc. and Microtome Publishing, 2017.
A. Zimin and C. Lampert, “Learning theory for conditional risk minimization,” presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States, 2017, vol. 54, pp. 213–222.
Zimin A, Lampert C. 2017. Learning theory for conditional risk minimization. AISTATS: Artificial Intelligence and Statistics, PMLR, vol. 54. 213–222.
Zimin, Alexander, and Christoph Lampert. Learning Theory for Conditional Risk Minimization. Vol. 54, JMLR, Inc. and Microtome Publishing, 2017, pp. 213–22.

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