37 citations · 65 across the 6 of their papers we have counts for
7 papers
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Peng Shi, Patrick Ng, Zhiguo Wang +5
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural langua…
Decomposed Adversarial Learned Inference
Alexander Hanbo Li, Yaqing Wang, Changyou Chen +1
Effective inference for a generative adversarial model remains an important and challenging problem. We propose a novel approach, Decomposed Adversarial Learned Inference (DALI), w…
Censored Quantile Regression Forest
Alexander Hanbo Li, Jelena Bradic
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhi…
Semi-Supervised Learning for Text Classification by Layer Partitioning
Alexander Hanbo Li, Abhinav Sethy
Most recent neural semi-supervised learning algorithms rely on adding small perturbation to either the input vectors or their representations. These methods have been successful on…
Knowledge Enhanced Attention for Robust Natural Language Inference
Alexander Hanbo Li, Abhinav Sethy
Neural network models have been very successful at achieving high accuracy on natural language inference (NLI) tasks. However, as demonstrated in recent literature, when tested on…
Censored Quantile Regression Forests
Alexander Hanbo Li, Jelena Bradic
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhi…
Boosting in the presence of outliers: adaptive classification with non-convex loss functions
Alexander Hanbo Li, Jelena Bradic
This paper examines the role and efficiency of the non-convex loss functions for binary classification problems. In particular, we investigate how to design a simple and effective…