786 citations · 1.5k across the 17 of their papers we have counts for
17 papers
NECA: Network-Embedded Deep Representation Learning for Categorical Data
Xiaonan Gao, Sen Wu, Wenjun Zhou
We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA dee…
Metadata Shaping: Natural Language Annotations for the Tail
Simran Arora, Sen Wu, Enci Liu +1
Language models (LMs) have made remarkable progress, but still struggle to generalize beyond the training data to rare linguistic patterns. Since rare entities and facts are preval…
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text
Maya Varma, Laurel Orr, Sen Wu +3
Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare ent…
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
Laurel Orr, Megan Leszczynski, Simran Arora +4
A challenge for named entity disambiguation (NED), the task of mapping textual mentions to entities in a knowledge base, is how to disambiguate entities that appear rarely in the t…
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
Fan Yang, Hongyang R. Zhang, Sen Wu +2
The problem of learning one task using samples from another task is central to transfer learning. In this paper, we focus on answering the following question: when does combining t…
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings
Mayee F. Chen, Daniel Y. Fu, Frederic Sala +5
Our goal is to enable machine learning systems to be trained interactively. This requires models that perform well and train quickly, without large amounts of hand-labeled data. We…
Understanding and Improving Information Transfer in Multi-Task Learning
Sen Wu, Hongyang R. Zhang, Christopher Ré
We investigate multi-task learning approaches that use a shared feature representation for all tasks. To better understand the transfer of task information, we study an architectur…
On the Generalization Effects of Linear Transformations in Data Augmentation
Sen Wu, Hongyang R. Zhang, Gregory Valiant +1
Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and…
Ivy: Instrumental Variable Synthesis for Causal Inference
Zhaobin Kuang, Frederic Sala, Nimit Sohoni +5
A popular way to estimate the causal effect of a variable x on y from observational data is to use an instrumental variable (IV): a third variable z that affects y only through x.…
Understanding the Downstream Instability of Word Embeddings
Megan Leszczynski, Avner May, Jian Zhang +3
Many industrial machine learning (ML) systems require frequent retraining to keep up-to-date with constantly changing data. This retraining exacerbates a large challenge facing ML…
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
Vincent S. Chen, Sen Wu, Zhenzhen Weng +2
In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving tas…
Snorkel: Rapid Training Data Creation with Weak Supervision
Alexander Ratner, Stephen H. Bach, Henry Ehrenberg +3
Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train stat…
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data
Jason Fries, Sen Wu, Alex Ratner +1
We present SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources lik…
Fonduer: Knowledge Base Construction from Richly Formatted Data
Sen Wu, Luke Hsiao, Xiao Cheng +4
We focus on knowledge base construction (KBC) from richly formatted data. In contrast to KBC from text or tabular data, KBC from richly formatted data aims to extract relations con…
Data Programming: Creating Large Training Sets, Quickly
Alexander Ratner, Christopher De Sa, Sen Wu +2
Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labele…
Incremental Knowledge Base Construction Using DeepDive
Jaeho Shin, Sen Wu, Feiran Wang +3
Populating a database with unstructured information is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. Recent n…
Feature Engineering for Knowledge Base Construction
Christopher Ré, Amir Abbas Sadeghian, Zifei Shan +4
Knowledge base construction (KBC) is the process of populating a knowledge base, i.e., a relational database together with inference rules, with information extracted from document…