Search Results for author:
Found 9 papers, 2 papers with code
Extreme Multi-label Classification from Aggregated Labels
Current XMC approaches are not built for such multi-instance multi-label (MIML) training data, and MIML approaches do not scale to XMC sizes.
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space
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
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
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
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
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
High Dimensional Robust Sparse Regression
Our algorithm recovers the true sparse parameters with sub-linear sample complexity, in the presence of a constant fraction of arbitrary corruptions.
Deep Active Learning for Named Entity Recognition
In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning.
Normalized Spectral Map Synchronization
The algorithmic advancement of synchronizing maps is important in order to solve a wide range of practice problems with possible large-scale dataset.
