94 citations · 160 across the 18 of their papers we have counts for
6 papers · 1 filter
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction
Yiyuan She, Jiahui Shen, Chao Zhang
Modern high-dimensional methods often adopt the "bet on sparsity" principle, while in supervised multivariate learning statisticians may face "dense" problems with a large number o…
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language Models
Yue Yu, Lingkai Kong, Jieyu Zhang +2
While pre-trained language model (PLM) fine-tuning has achieved strong performance in many NLP tasks, the fine-tuning stage can be still demanding in labeled data. Recent works hav…
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities an…
Self-Training with Differentiable Teacher
Simiao Zuo, Yue Yu, Chen Liang +5
Self-training achieves enormous success in various semi-supervised and weakly-supervised learning tasks. The method can be interpreted as a teacher-student framework, where the tea…
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
Yinghao Li, Pranav Shetty, Lucas Liu +2
We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak sup…
PanGu-: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
Wei Zeng, Xiaozhe Ren, Teng Su +35
Large-scale Pretrained Language Models (PLMs) have become the new paradigm for Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as GPT-3 have demon…