Search Results for author:
Found 5 papers, 2 papers with code
Learning-Based Efficient Approximation of Data-enabled Predictive Control
However, the size of the optimization problem in DeePC grows linearly with respect to the data size, which prohibits its application due to high computational costs.
MPC-Inspired Reinforcement Learning for Verifiable Model-Free Control
In this paper, we introduce a new class of parameterized controllers, drawing inspiration from Model Predictive Control (MPC).
A Competitive Algorithm for Agnostic Active Learning
For some hypothesis classes and input distributions, active agnostic learning needs exponentially fewer samples than passive learning; for other classes and distributions, it offers little to no improvement.
Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm
We show that bound- and summation-constrained steepest descent in the L1-norm guarantees more progress per iteration than previous rules and can be computed in only $O(n \log n)$ time.
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
We extend the known regret bounds for classical OCO algorithms to the relative setting.
