no code implementations • 25 May 2022 • Alessio Mazzetto, Cristina Menghini, Andrew Yuan, Eli Upfal, Stephen H. Bach
We develop the first non-trivial lower bound on the worst-case error of the best map from attributes to classes for this setting, even with perfect attribute detectors.
no code implementations • 15 May 2022 • Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen H. Bach, Himabindu Lakkaraju
We then leverage these properties to propose a novel evaluation framework which can quantitatively measure disparities in the quality of explanations output by state-of-the-art methods.
no code implementations • 4 May 2022 • Ryan Smith, Jason A. Fries, Braden Hancock, Stephen H. Bach
Our experimental evaluation shows that prompting large language models within a weak supervision framework can provide significant gains in accuracy.
1 code implementation • 7 Apr 2022 • Nihal V. Nayak, Peilin Yu, Stephen H. Bach
Further, we show that CSP improves generalization to higher-order attribute-attribute-object compositions and combinations of pretrained attributes and fine-tuned objects.
1 code implementation • ACL 2022 • Stephen H. Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged S. Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir Radev, Mike Tian-Jian Jiang, Alexander M. Rush
PromptSource is a system for creating, sharing, and using natural language prompts.
2 code implementations • 8 Nov 2021 • Wasu Piriyakulkij, Cristina Menghini, Ross Briden, Nihal V. Nayak, Jeffrey Zhu, Elaheh Raisi, Stephen H. Bach
Machine learning practitioners often have access to a spectrum of data: labeled data for the target task (which is often limited), unlabeled data, and auxiliary data, the many available labeled datasets for other tasks.
3 code implementations • ICLR 2022 • Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Tali Bers, Stella Biderman, Leo Gao, Thomas Wolf, Alexander M. Rush
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
no code implementations • 24 Jun 2021 • Jessica Dai, Sohini Upadhyay, Stephen H. Bach, Himabindu Lakkaraju
In situations where explanations of black-box models may be useful, the fairness of the black-box is also often a relevant concern.
1 code implementation • 8 Jun 2021 • Peilin Yu, Tiffany Ding, Stephen H. Bach
We evaluate our framework on three text classification and six object classification tasks.
2 code implementations • 13 Dec 2020 • Reza Esfandiarpoor, Amy Pu, Mohsen Hajabdollahi, Stephen H. Bach
In many practical few-shot learning problems, even though labeled examples are scarce, there are abundant auxiliary datasets that potentially contain useful information.
2 code implementations • 18 Jun 2020 • Nihal V. Nayak, Stephen H. Bach
Zero-shot learning relies on semantic class representations such as hand-engineered attributes or learned embeddings to predict classes without any labeled examples.
Ranked #1 on
Generalized Zero-Shot Learning
on OntoNotes
no code implementations • 2 Dec 2018 • Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alexander Ratner, Braden Hancock, Houman Alborzi, Rahul Kuchhal, Christopher Ré, Rob Malkin
Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications.
2 code implementations • 28 Nov 2017 • Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, Christopher Ré
In a user study, subject matter experts build models 2. 8x faster and increase predictive performance an average 45. 5% versus seven hours of hand labeling.
no code implementations • ICML 2017 • Stephen H. Bach, Bryan He, Alexander Ratner, Christopher Ré
Curating labeled training data has become the primary bottleneck in machine learning.
no code implementations • 17 May 2015 • Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor
In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data.