384 citations · 1.1k across the 22 of their papers we have counts for
11 papers · 1 filter
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…
Continuous Control with Contexts, Provably
Simon S. Du, Ruosong Wang, Mengdi Wang +1
A fundamental challenge in artificial intelligence is to build an agent that generalizes and adapts to unseen environments. A common strategy is to build a decoder that takes the c…
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Sanjeev Arora, Simon S. Du, Zhiyuan Li +3
Recent research shows that the following two models are equivalent: (a) infinitely wide neural networks (NNs) trained under l2 loss by gradient descent with infinitesimally small l…
Efficient Symmetric Norm Regression via Linear Sketching
Zhao Song, Ruosong Wang, Lin F. Yang +2
We provide efficient algorithms for overconstrained linear regression problems with size when the loss function is a symmetric norm (a norm invariant under sign-flips…
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
Simon S. Du, Sham M. Kakade, Ruosong Wang +1
Modern deep learning methods provide effective means to learn good representations. However, is a good representation itself sufficient for sample efficient reinforcement learning?…
The Communication Complexity of Optimization
Santosh S. Vempala, Ruosong Wang, David P. Woodruff
We consider the communication complexity of a number of distributed optimization problems. We start with the problem of solving a linear system. Suppose there is a coordinator toge…
Dimensionality Reduction for Tukey Regression
Kenneth L. Clarkson, Ruosong Wang, David P. Woodruff
We give the first dimensionality reduction methods for the overconstrained Tukey regression problem. The Tukey loss function has …
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
Simon S. Du, Kangcheng Hou, Barnabás Póczos +3
While graph kernels (GKs) are easy to train and enjoy provable theoretical guarantees, their practical performances are limited by their expressive power, as the kernel function of…
On Exact Computation with an Infinitely Wide Neural Net
Sanjeev Arora, Simon S. Du, Wei Hu +3
How well does a classic deep net architecture like AlexNet or VGG19 classify on a standard dataset such as CIFAR-10 when its width --- namely, number of channels in convolutional l…
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Sanjeev Arora, Simon S. Du, Wei Hu +2
Recent works have cast some light on the mystery of why deep nets fit any data and generalize despite being very overparametrized. This paper analyzes training and generalization f…