5 papers
Data-Driven MPC with Adaptively Sampled Non-Expert Demonstrations: Performance Guarantees and Sample Complexity
Shijie Pan, Agustin Castellano, Enrique Mallada
We study data-driven policy construction for dynamical systems using trajectories generated by surrogate finite-horizon Model Predictive Control (MPC), with the goal of approximati…
Memory-Computation Tradeoffs in Semi Amortized Parametric Optimization
Shijie Pan, Agustin Castellano, Zeyu Shen +1
Learning-enabled decision systems often use offline data or computation to reduce online compute cost. Despite the empirical success of such approaches, there is limited general un…
Symplectic Inductive Bias for Data-Driven Target Reachability in Hamiltonian Systems
Zhuo Ouyang, Jixian Liu, Enrique Mallada
Inductive bias refers to restrictions on the hypothesis class that enable a learning method to generalize effectively from limited data. A canonical example in control is linearity…
A Local Polyak-Lojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models
Ziqing Xu, Hancheng Min, Salma Tarmoun +2
Most prior work on the convergence of gradient descent (GD) for overparameterized neural networks relies on strong assumptions on the step size (infinitesimal), the hidden-layer wi…
Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits
Ha Manh Bui, Enrique Mallada, Anqi Liu
By leveraging the representation power of deep neural networks, neural upper confidence bound (UCB) algorithms have shown success in contextual bandits. To further balance the expl…