Search Results for author: Yanyao Shen

Found 9 papers, 2 papers with code

Extreme Multi-label Classification from Aggregated Labels

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.

Classification Extreme Multi-Label Classification +1

Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space

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.


Iterative Least Trimmed Squares for Mixed Linear Regression

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.


Learning with Bad Training Data via Iterative Trimmed Loss Minimization

no code implementations28 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.

Iteratively Learning from the Best

no code implementations27 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.

Dense Information Flow for Neural Machine Translation

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.

Decoder Machine Translation +2

High Dimensional Robust Sparse Regression

no code implementations29 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.

regression Vocal Bursts Intensity Prediction

Deep Active Learning for Named Entity Recognition

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.

Active Learning Decoder +4

Normalized Spectral Map Synchronization

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.

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