no code implementations • ACL 2022 • Ryan Steed, Swetasudha Panda, Ari Kobren, Michael Wick
A few large, homogenous, pre-trained models undergird many machine learning systems — and often, these models contain harmful stereotypes learned from the internet.
no code implementations • EMNLP (BlackboxNLP) 2020 • Naveen Jafer Nizar, Ari Kobren
We present a method for adversarial input generation against black box models for reading comprehension based question answering.
no code implementations • AKBC 2020 • Derek Tam, Nicholas Monath, Ari Kobren, Andrew McCallum
The hierarchical structure of research organizations plays a pivotal role in science of science research as well as in tools that track the research achievements and output.
1 code implementation • 31 Dec 2019 • Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum
We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets.
1 code implementation • NeurIPS 2019 • Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas
We present RobinHood, an ofﬂine contextual bandit algorithm designed to satisfy a broad family of fairness constraints.
1 code implementation • ACL 2019 • Derek Tam, Nicholas Monath, Ari Kobren, Aaron Traylor, Rajarshi Das, Andrew McCallum
We evaluate STANCE's ability to detect whether two strings can refer to the same entity--a task we term alias detection.
1 code implementation • 19 Jun 2019 • Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum
Thus we introduce an approach to supervised hierarchical clustering that smoothly interpolates between single, average, and complete linkage, and we give a training procedure that simultaneously learns a linkage function and a dissimilarity function.
3 code implementations • 28 May 2019 • Ari Kobren, Barna Saha, Andrew McCallum
Automatically matching reviewers to papers is a crucial step of the peer review process for venues receiving thousands of submissions.
Data Structures and Algorithms Digital Libraries
no code implementations • 28 May 2019 • Ari Kobren, Pablo Barrio, Oksana Yakhnenko, Johann Hibschman, Ian Langmore
In this work, we develop a method for constructing KBs with tunable precision--i. e., KBs that can be made to operate at a specific false positive rate, despite storing both difficult-to-evaluate subjective attributes and more traditional factual attributes.
no code implementations • NeurIPS 2018 • Craig Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew Mcgregor, Andrew McCallum
For many classic structured prediction problems, probability distributions over the dependent variables can be efficiently computed using widely-known algorithms and data structures (such as forward-backward, and its corresponding trellis for exact probability distributions in Markov models).
no code implementations • AKBC 2019 • Ari Kobren, Nicholas Monath, Andrew McCallum
Users have tremendous potential to aid in the construction and maintenance of knowledges bases (KBs) through the contribution of feedback that identifies incorrect and missing entity attributes and relations.
2 code implementations • 6 Apr 2017 • Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum
Many modern clustering methods scale well to a large number of data items, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy algorithm for online hierarchical clustering that scales to both massive N and K--a problem setting we term extreme clustering.