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02/19/2023
Weakly Supervised Label Learning Flows
Supervised learning usually requires a large amount of labelled data. Ho...
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02/08/2022
Data Consistency for Weakly Supervised Learning
In many applications, training machine learning models involves using la...
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09/15/2020
Constrained Labeling for Weakly Supervised Learning
Curation of large fully supervised datasets has become one of the major ...
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02/27/2020
Woodbury Transformations for Deep Generative Flows
Normalizing flows are deep generative models that allow efficient likeli...
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06/17/2019
Active Learning by Greedy Split and Label Exploration
Annotating large unlabeled datasets can be a major bottleneck for machin...
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06/07/2019
Labeled Graph Generative Adversarial Networks
As a new way to train generative models, generative adversarial networks...
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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...
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05/30/2019
Structured Output Learning with Conditional Generative Flows
Traditional structured prediction models try to learn the conditional li...
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11/09/2018
Block Belief Propagation for Parameter Learning in Markov Random Fields
Traditional learning methods for training Markov random fields require d...
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05/22/2018
Adversarial Labeling for Learning without Labels
We consider the task of training classifiers without labels. We propose ...
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06/29/2017
New Fairness Metrics for Recommendation that Embrace Differences
We study fairness in collaborative-filtering recommender systems, which ...
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05/25/2017
Online Edge Grafting for Efficient MRF Structure Learning
Incremental methods for structure learning of pairwise Markov random fie...
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05/24/2017
Beyond Parity: Fairness Objectives for Collaborative Filtering
We study fairness in collaborative-filtering recommender systems, which ...
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06/26/2016
Cyberbullying Identification Using Participant-Vocabulary Consistency
With the rise of social media, people can now form relationships and com...
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05/17/2015
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
A fundamental challenge in developing high-impact machine learning techn...
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09/26/2013
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
Graphical models for structured domains are powerful tools, but the comp...
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08/30/2013
