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Found 6 papers, 1 papers with code
Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration
We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees such as stability and optimality at systems level.
Towards Expedited Impedance Tuning of a Robotic Prosthesis for Personalized Gait Assistance by Reinforcement Learning Control
Personalizing medical devices such as lower limb wearable robots is challenging.
Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation
In this paper, a strategy is proposed that allows the use of inexact solutions of these subproblems, which, as proved in the paper, can be incorporated without the loss of theoretical convergence guarantees.
Optimization and Control
Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers
In this work, we show that in the case of linear predictors, the expected error and the expected ranking loss can be effectively approximated by smooth functions whose closed form expressions and those of their first (and second) order derivatives depend on the first and second moments of the data distribution, which can be precomputed.
Active Metric Learning for Supervised Classification
We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature.
Optimal Generalized Decision Trees via Integer Programming
Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features.
