3 papers
math.OC2026
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…
cs.LG2025
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…
cs.LG2024
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…