1 code implementation • 19 Feb 2023 • You Lu, Chidubem Arachie, Bert Huang
In this paper, we develop label learning flows (LLF), a general framework for weakly supervised learning problems.
no code implementations • Proceedings of the 25th International Conference on Artificial Intelligence and Statistics 2022 • Zhaobin Kuang, Chidubem Arachie, Bangyong Liang, Pradyumna Narayana, Giulia Desalvo, MICHAEL QUINN, Bert Huang, Geoffrey Downs, Yang Yang
In particular, Firebolt learns the class balance and class-specific accuracy of LFs jointly from unlabeled data.
no code implementations • 8 Feb 2022 • Chidubem Arachie, Bert Huang
Instead, we use weak signals and the data features to solve a constrained optimization that enforces data consistency among the labels we generate.
1 code implementation • 15 Sep 2020 • Chidubem Arachie, Bert Huang
Curation of large fully supervised datasets has become one of the major roadblocks for machine learning.
1 code implementation • NeurIPS 2020 • You Lu, Bert Huang
In this paper, we introduce Woodbury transformations, which achieve efficient invertibility via the Woodbury matrix identity and efficient determinant calculation via Sylvester's determinant identity.
no code implementations • 17 Jun 2019 • Alyssa Herbst, Bert Huang
We introduce a scheme for inferring labels of unlabeled data at a fraction of the cost of labeling the entire dataset.
no code implementations • 7 Jun 2019 • Shuangfei Fan, Bert Huang
To further evaluate the quality of the generated graphs, we use them on a downstream task of graph classification, and the results show that LGGAN can faithfully capture the important aspects of the graph structure.
no code implementations • 3 Jun 2019 • Chidubem Arachie, Bert Huang
In this paper, we propose stochastic generalized adversarial label learning (Stoch-GALL), a framework for training machine learning models that perform well when noisy and possibly correlated labels are provided.
2 code implementations • 30 May 2019 • You Lu, Bert Huang
Traditional structured prediction models try to learn the conditional likelihood, i. e., p(y|x), to capture the relationship between the structured output y and the input features x.
no code implementations • ICLR Workshop DeepGenStruct 2019 • Shuangfei Fan, Bert Huang
As a new way to train generative models, generative adversarial networks (GANs) have achieved considerable success in image generation, and this framework has also recently been applied to data with graph structures.
no code implementations • 9 Nov 2018 • You Lu, Zhiyuan Liu, Bert Huang
Traditional learning methods for training Markov random fields require doing inference over all variables to compute the likelihood gradient.
no code implementations • 22 May 2018 • Chidubem Arachie, Bert Huang
We propose a weakly supervised method---adversarial label learning---that trains classifiers to perform well against an adversary that chooses labels for training data.
no code implementations • 29 Jun 2017 • Sirui Yao, Bert Huang
We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data.
no code implementations • 25 May 2017 • Walid Chaabene, Bert Huang
In this paper, we address key computational bottlenecks that current incremental techniques still suffer by introducing best-choice edge grafting, an incremental, structured method that activates edges as groups of features in a streaming setting.
1 code implementation • NeurIPS 2017 • Sirui Yao, Bert Huang
We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data.
no code implementations • 19 Mar 2017 • Shuangfei Fan, Bert Huang
We propose a new method for training iterative collective classifiers for labeling nodes in network data.
no code implementations • 26 Jun 2016 • Elaheh Raisi, Bert Huang
In this study, we propose a model that simultaneously discovers instigators and victims of bullying as well as new bullying vocabulary by starting with a corpus of social interactions and a seed dictionary of bullying indicators.
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.
no code implementations • 26 Sep 2013 • Stephen Bach, Bert Huang, Ben London, Lise Getoor
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable.
no code implementations • 30 Aug 2013 • Hui Miao, Xiangyang Liu, Bert Huang, Lise Getoor
In modern data science problems, techniques for extracting value from big data require performing large-scale optimization over heterogenous, irregularly structured data.
no code implementations • 7 Mar 2013 • Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor
For the typical cases of real-valued functions and binary relations, we propose several loss functions and derive the associated parameter gradients.
no code implementations • 21 Feb 2013 • Ben London, Bert Huang, Lise Getoor
We investigate the generalizability of learned binary relations: functions that map pairs of instances to a logical indicator.
no code implementations • NeurIPS 2011 • Blake Shaw, Bert Huang, Tony Jebara
To better model and understand these networks, we present structure preserving metric learning (SPML), an algorithm for learning a Mahalanobis distance metric from a network such that the learned distances are tied to the inherent connectivity structure of the network.