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Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

Dec 11, 2020
Benedikt Boecking, Willie Neiswanger, Eric Xing, Artur Dubrawski


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Self-Reflective Variational Autoencoder

Jul 10, 2020
Ifigeneia Apostolopoulou, Elan Rosenfeld, Artur Dubrawski


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Preference-based Reinforcement Learning with Finite-Time Guarantees

Jun 16, 2020
Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski

* 22 pages, 2 figures 

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System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

May 11, 2020
Kyle Miller, Artur Dubrawski

* 24 pages, 3 figures 

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Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks

Mar 02, 2020
Chirag Nagpal, Xinyu Li, Artur Dubrawski


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Pairwise Feedback for Data Programming

Dec 16, 2019
Benedikt Boecking, Artur Dubrawski

* Presented at the NeurIPS 2019 workshop on Learning with Rich Experience: Integration of Learning Paradigms 

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Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning

Nov 12, 2019
Chufan Gao, Fabian Falck, Mononito Goswami, Anthony Wertz, Michael R. Pinsky, Artur Dubrawski

* Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract 

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Zeroth Order Non-convex optimization with Dueling-Choice Bandits

Nov 03, 2019
Yichong Xu, Aparna Joshi, Aarti Singh, Artur Dubrawski

* 19 pages, 3 figures 

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Active Learning for Graph Neural Networks via Node Feature Propagation

Oct 16, 2019
Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, Artur Dubrawski

* 16 pages, 5 figures 

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Thresholding Bandit Problem with Both Duels and Pulls

Oct 14, 2019
Yichong Xu, Xi Chen, Aarti Singh, Artur Dubrawski

* 21 pages, 8 figures 

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Nonlinear Semi-Parametric Models for Survival Analysis

May 14, 2019
Chirag Nagpal, Rohan Sangave, Amit Chahar, Parth Shah, Artur Dubrawski, Bhiksha Raj


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Double Adaptive Stochastic Gradient Optimization

Nov 06, 2018
Kin Gutierrez, Jin Li, Cristian Challu, Artur Dubrawski


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Dependency Leakage: Analysis and Scalable Estimators

Jul 18, 2018
Matt Barnes, Artur Dubrawski


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Learning under selective labels in the presence of expert consistency

Jul 04, 2018
Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova

* Presented at the 2018 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2018) 

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Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information

Jun 08, 2018
Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

* 21 pages, 5 figures; International Conference on Machine Learning 2018 

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Novel Prediction Techniques Based on Clusterwise Linear Regression

Apr 28, 2018
Igor Gitman, Jieshi Chen, Eric Lei, Artur Dubrawski


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Learning Mixtures of Multi-Output Regression Models by Correlation Clustering for Multi-View Data

Sep 17, 2017
Eric Lei, Kyle Miller, Artur Dubrawski


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Scaling Active Search using Linear Similarity Functions

Aug 22, 2017
Sibi Venkatesan, James K. Miller, Jeff Schneider, Artur Dubrawski

* To be published as conference paper at IJCAI 2017, 11 pages, 2 figures 

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Noise-Tolerant Interactive Learning from Pairwise Comparisons

May 19, 2017
Yichong Xu, Hongyang Zhang, Aarti Singh, Kyle Miller, Artur Dubrawski

* 28 pages, 1 figure, 3 tables 

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Clustering on the Edge: Learning Structure in Graphs

May 05, 2016
Matt Barnes, Artur Dubrawski


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Batched Lazy Decision Trees

Mar 08, 2016
Mathieu Guillame-Bert, Artur Dubrawski

* 7 pages, 2 figures, 3 tables, 3 algorithms 

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Lass-0: sparse non-convex regression by local search

Feb 17, 2016
William Herlands, Maria De-Arteaga, Daniel Neill, Artur Dubrawski

* 8 pages, 1 figure. NIPS 2015 Workshop of Optimization (OPT2015) 

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Canonical Autocorrelation Analysis

Nov 19, 2015
Maria De-Arteaga, Artur Dubrawski, Peter Huggins

* 6 pages, 5 figures 

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Performance Bounds for Pairwise Entity Resolution

Sep 10, 2015
Matt Barnes, Kyle Miller, Artur Dubrawski


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