256 citations · 487 across the 10 of their papers we have counts for
10 papers
Provable Representation Learning for Imitation Learning via Bi-level Optimization
Sanjeev Arora, Simon S. Du, Sham Kakade +2
A common strategy in modern learning systems is to learn a representation that is useful for many tasks, a.k.a. representation learning. We study this strategy in the imitation lea…
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
Yi Zhang, Orestis Plevrakis, Simon S. Du +3
Adversarial training is a popular method to give neural nets robustness against adversarial perturbations. In practice adversarial training leads to low robust training loss. Howev…
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity
Simon S. Du, Jason D. Lee, Gaurav Mahajan +1
The current paper studies the problem of agnostic -learning with function approximation in deterministic systems where the optimal -function is approximable by a function in…
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Yining Wang, Ruosong Wang, Simon S. Du +1
We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity a…
Enhanced Convolutional Neural Tangent Kernels
Zhiyuan Li, Ruosong Wang, Dingli Yu +4
Recent research shows that for training with loss, convolutional neural networks (CNNs) whose width (number of channels in convolutional layers) goes to infinity correspon…
Towards Understanding the Importance of Shortcut Connections in Residual Networks
Tianyi Liu, Minshuo Chen, Mo Zhou +3
Residual Network (ResNet) is undoubtedly a milestone in deep learning. ResNet is equipped with shortcut connections between layers, and exhibits efficient training using simple fir…