112 citations · 288 across the 11 of their papers we have counts for
9 papers · 1 filter
When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima?
Maziar Sanjabi, Sina Baharlouei, Meisam Razaviyayn +1
We study the optimization problem for decomposing dimensional fourth-order Tensors with non-orthogonal components. We derive \textit{deterministic} conditions under which s…
SGD Learns One-Layer Networks in WGANs
Qi Lei, Jason D. Lee, Alexandros G. Dimakis +1
Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require so…
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks
Yu Bai, Jason D. Lee
Recent theoretical work has established connections between over-parametrized neural networks and linearized models governed by he Neural Tangent Kernels (NTKs). NTK theory leads t…
Optimal transport mapping via input convex neural networks
Ashok Vardhan Makkuva, Amirhossein Taghvaei, Sewoong Oh +1
In this paper, we present a novel and principled approach to learn the optimal transport between two distributions, from samples. Guided by the optimal transport theory, we learn t…
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
Alekh Agarwal, Sham M. Kakade, Jason D. Lee +1
Policy gradient methods are among the most effective methods in challenging reinforcement learning problems with large state and/or action spaces. However, little is known about ev…
Convergence of Adversarial Training in Overparametrized Neural Networks
Ruiqi Gao, Tianle Cai, Haochuan Li +3
Neural networks are vulnerable to adversarial examples, i.e. inputs that are imperceptibly perturbed from natural data and yet incorrectly classified by the network. Adversarial tr…