activity
20142022
most citedSnorkel: Rapid Training Data Creation with Weak Supervision

786 citations · 1.5k across the 17 of their papers we have counts for

collaborators
Showing 2020Show all

7 papers · 1 filter

cs.CL2020★ 43 cited

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…

stat.ML2020★ 1 cited

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…

stat.ML2020★ 4 cited

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…

cs.LG2020★ 46 cited

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…

cs.LG2020★ 12 cited

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…

cs.LG2020★ 11 cited

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.…