no code implementations • ICML 2020 • Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit Dhillon
Current XMC approaches are not built for such multi-instance multi-label (MIML) training data, and MIML approaches do not scale to XMC sizes.
no code implementations • NeurIPS 2019 • Shuo Yang, Yanyao Shen, Sujay Sanghavi
In this paper, we provide a new algorithm - Interaction Hard Thresholding (IntHT) which is the first one to provably accurately solve this problem in sub-quadratic time and space.
no code implementations • NeurIPS 2019 • Yanyao Shen, Sujay Sanghavi
We then evaluate it for the widely studied setting of isotropic Gaussian features, and establish that we match or better existing results in terms of sample complexity.
no code implementations • 28 Oct 2018 • Yanyao Shen, Sujay Sanghavi
In this paper, we study a simple and generic framework to tackle the problem of learning model parameters when a fraction of the training samples are corrupted.
no code implementations • 27 Sep 2018 • Yanyao Shen, Sujay Sanghavi
We study a simple generic framework to address the issue of bad training data; both bad labels in supervised problems, and bad samples in unsupervised ones.
1 code implementation • NAACL 2018 • Yanyao Shen, Xu Tan, Di He, Tao Qin, Tie-Yan Liu
Recently, neural machine translation has achieved remarkable progress by introducing well-designed deep neural networks into its encoder-decoder framework.
Ranked #66 on Machine Translation on WMT2014 English-German
no code implementations • 29 May 2018 • Liu Liu, Yanyao Shen, Tianyang Li, Constantine Caramanis
Our algorithm recovers the true sparse parameters with sub-linear sample complexity, in the presence of a constant fraction of arbitrary corruptions.
2 code implementations • WS 2017 • Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar
In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning.
no code implementations • NeurIPS 2016 • Yanyao Shen, Qi-Xing Huang, Nati Srebro, Sujay Sanghavi
The algorithmic advancement of synchronizing maps is important in order to solve a wide range of practice problems with possible large-scale dataset.