Jason D. Lee

Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods

2019 · arXiv (Cornell University)

Recent applications that arise in machine learning have surged significant interest in solving min-max saddle point games. This problem has been extensively studied in the convex-concave regime for which a global equilibrium solution can be …

$\ell_1$-regularized Neural Networks are Improperly Learnable in Polynomial Time

2015 · arXiv (Cornell University)

We study the improper learning of multi-layer neural networks. Suppose that the neural network to be learned has $k$ hidden layers and that the $\ell_1$-norm of the incoming weights of any neuron is bounded by …

Optimal transport mapping via input convex neural networks

2019 · arXiv (Cornell University)

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 the optimal Kantorovich potential which induces the …

Fully Character-Level Neural Machine Translation without Explicit Segmentation

2017 · Transactions of the Association for Computational Linguistics

Most existing machine translation systems operate at the level of words, relying on explicit segmentation to extract tokens. We introduce a neural machine translation (NMT) model that maps a source character sequence to a target …