conference paper
Learning theory for conditional risk minimization
PMLR
published
yes
Alexander
Zimin
author 37099E9C-F248-11E8-B48F-1D18A9856A87
Christoph
Lampert
author 40C20FD2-F248-11E8-B48F-1D18A9856A870000-0001-8622-7887
ChLa
department
AISTATS: Artificial Intelligence and Statistics
Lifelong Learning of Visual Scene Understanding
project
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.
ML Research Press2017Fort Lauderdale, FL, United States
eng
000509368500024
54213 - 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,” 54:213–22. ML Research Press, 2017.
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: ML Research Press.
A. Zimin, C. Lampert, in:, ML Research Press, 2017, pp. 213–222.
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. Learning theory for conditional risk minimization. In: Vol 54. ML Research Press; 2017:213-222.
Zimin, Alexander, and Christoph Lampert. <i>Learning Theory for Conditional Risk Minimization</i>. Vol. 54, ML Research Press, 2017, pp. 213–22.
11082018-12-11T11:50:11Z2023-10-17T10:01:12Z