Doubly Stochastic Primal-Dual Coordinate Method for Empirical Risk Minimization
51:00
Microsoft
Doubly Stochastic Primal-Dual Coordinate Method for Empirical Risk Minimization
We proposed a doubly stochastic primal-dual coordinate optimization algorithm for regularized empirical risk minimization that can be formulated as a saddle-point problem using convex conjugate functions. Different from the existing coordinate methods, the proposed method randomly samples both primal and dual coordinates to update solutions ...
Mar 30, 2016
Empirical Risk Minimization Examples
Closed-loop Weighted Empirical Risk Minimization (CW-ERM) addresses a long-standing problem in imitation learning. It is simple to implement and significantly reduces self-driving collisions by a large margin on real-world datasets. Below we are comparing the same scene using ERM (left) and CW-ERM (right) and can see that CW-ERM avoids a front collision. Find out more about this project here 👇 https://wp-research-uk.github.io/cw-erm/ #WovenPlanet | Woven by Toyota
0:08
Closed-loop Weighted Empirical Risk Minimization (CW-ERM) addresses a long-standing problem in imitation learning. It is simple to implement and significantly reduces self-driving collisions by a large margin on real-world datasets. Below we are comparing the same scene using ERM (left) and CW-ERM (right) and can see that CW-ERM avoids a front collision. Find out more about this project here 👇 https://wp-research-uk.github.io/cw-erm/ #WovenPlanet | Woven by Toyota
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