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∙ 02/19/2023

Weakly Supervised Label Learning Flows

Supervised learning usually requires a large amount of labelled data. Ho...

0 You Lu, et al. ∙

∙ 02/08/2022

Data Consistency for Weakly Supervised Learning

In many applications, training machine learning models involves using la...

1 Chidubem Arachie, et al. ∙

∙ 09/15/2020

Constrained Labeling for Weakly Supervised Learning

Curation of large fully supervised datasets has become one of the major ...

0 Chidubem Arachie, et al. ∙

∙ 02/27/2020

Woodbury Transformations for Deep Generative Flows

Normalizing flows are deep generative models that allow efficient likeli...

0 You Lu, et al. ∙

∙ 06/17/2019

Active Learning by Greedy Split and Label Exploration

Annotating large unlabeled datasets can be a major bottleneck for machin...

0 Alyssa Herbst, et al. ∙

∙ 06/07/2019

Labeled Graph Generative Adversarial Networks

As a new way to train generative models, generative adversarial networks...

0 Shuangfei Fan, et al. ∙

∙ 06/03/2019

An Adaptable Framework for Deep Adversarial Label Learning from Weak Supervision

In this paper, we propose a general framework for using adversarial labe...

0 Chidubem Arachie, et al. ∙

∙ 05/30/2019

Structured Output Learning with Conditional Generative Flows

Traditional structured prediction models try to learn the conditional li...

0 You Lu, et al. ∙

∙ 11/09/2018

Block Belief Propagation for Parameter Learning in Markov Random Fields

Traditional learning methods for training Markov random fields require d...

0 You Lu, et al. ∙

∙ 05/22/2018

Adversarial Labeling for Learning without Labels

We consider the task of training classifiers without labels. We propose ...

0 Chidubem Arachie, et al. ∙

∙ 06/29/2017

New Fairness Metrics for Recommendation that Embrace Differences

We study fairness in collaborative-filtering recommender systems, which ...

0 Sirui Yao, et al. ∙

∙ 05/25/2017

Online Edge Grafting for Efficient MRF Structure Learning

Incremental methods for structure learning of pairwise Markov random fie...

0 Walid Chaabene, et al. ∙

∙ 05/24/2017

Beyond Parity: Fairness Objectives for Collaborative Filtering

We study fairness in collaborative-filtering recommender systems, which ...

0 Sirui Yao, et al. ∙

∙ 06/26/2016

Cyberbullying Identification Using Participant-Vocabulary Consistency

With the rise of social media, people can now form relationships and com...

0 Elaheh Raisi, et al. ∙

∙ 05/17/2015

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic

A fundamental challenge in developing high-impact machine learning techn...

0 Stephen H. Bach, et al. ∙

∙ 09/26/2013

Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction

Graphical models for structured domains are powerful tools, but the comp...

0 Stephen Bach, et al. ∙

∙ 08/30/2013

A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization

In modern data science problems, techniques for extracting value from bi...

0 Hui Miao, et al. ∙

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