Search Results for author: Ari Kobren

Found 12 papers, 6 papers with code

Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models

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.

Leveraging Extracted Model Adversaries for Improved Black Box Attacks

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.

Model extraction Question Answering +1

Predicting Institution Hierarchies with Set-based Models

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.

Scalable Hierarchical Clustering with Tree Grafting

1 code implementation31 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.

Supervised Hierarchical Clustering with Exponential Linkage

1 code implementation19 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.

Paper Matching with Local Fairness Constraints

3 code implementations28 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

Constructing High Precision Knowledge Bases with Subjective and Factual Attributes

no code implementations28 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.

Vocal Bursts Intensity Prediction

Compact Representation of Uncertainty in Clustering

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).

Small Data Image Classification Structured Prediction

Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction

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.

Entity Resolution

An Online Hierarchical Algorithm for Extreme Clustering

2 code implementations6 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.