4.3k citations · 8.6k across the 159 of their papers we have counts for
6 papers · 1 filter
Differentiable MPC for End-to-end Planning and Control
Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks +2
We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces. This provides one…
A Continuous-Time View of Early Stopping for Least Squares
Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
We study the statistical properties of the iterates generated by gradient descent, applied to the fundamental problem of least squares regression. We take a continuous-time view, i…
Trellis Networks for Sequence Modeling
Shaojie Bai, J. Zico Kolter, Vladlen Koltun
We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized…
What game are we playing? End-to-end learning in normal and extensive form games
Chun Kai Ling, Fei Fang, J. Zico Kolter
Although recent work in AI has made great progress in solving large, zero-sum, extensive-form games, the underlying assumption in most past work is that the parameters of the game…
Scaling provable adversarial defenses
Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen +1
Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only…
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Shaojie Bai, J. Zico Kolter, Vladlen Koltun
For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent…