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Elias Chaibub Neto
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2020 – today
- 2024
- [j8]Elias Chaibub Neto, Vijay Yadav, Solveig K. Sieberts, Larsson Omberg:
A novel estimator for the two-way partial AUC. BMC Medical Informatics Decis. Mak. 24(1): 57 (2024) - [j7]Ramzi Halabi
, Rahavi Selvarajan, Zixiong Lin, Calvin Herd, Xueying Li
Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings. Sensors 24(19): 6246 (2024) - [j6]Elias Chaibub Neto:
Statistical disclosure control for numeric microdata via sequential joint probability preserving data shuffling. Trans. Data Priv. 17(3): 147-179 (2024) - [j5]Elias Chaibub Neto
Causality-Aware Predictions in Static Anticausal Machine Learning Tasks. IEEE Trans. Neural Networks Learn. Syst. 35(4): 5039-5053 (2024) - 2021
- [j4]Solveig K. Sieberts
Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge. npj Digit. Medicine 4 (2021) - [c8]Elias Chaibub Neto:
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners. ICML 2021: 8034-8044 - 2020
- [j3]Abhishek Pratap
Indicators of retention in remote digital health studies: a cross-study evaluation of 100, 000 participants. npj Digit. Medicine 3 (2020) - [c7]Elias Chaibub Neto:
A Causal Look at Statistical Definitions of Discrimination. KDD 2020: 873-881 - [i7]Elias Chaibub Neto:
Counterfactual confounding adjustment for feature representations learned by deep models: with an application to image classification tasks. CoRR abs/2004.09466 (2020) - [i6]Elias Chaibub Neto, Phil Snyder, Solveig K. Sieberts, Larsson Omberg:
Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features. CoRR abs/2011.04128 (2020) - [i5]Elias Chaibub Neto:
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners. CoRR abs/2011.04605 (2020)
2010 – 2019
- 2019
- [j2]Elias Chaibub Neto, Abhishek Pratap
Detecting the impact of subject characteristics on machine learning-based diagnostic applications. npj Digit. Medicine 2 (2019) - [c6]Elias Chaibub Neto, Abhishek Pratap, Thanneer M. Perumal
A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health. KDD 2019: 54-64 - [i4]Abhishek Pratap, Elias Chaibub Neto, Phil Snyder, Carl Stepnowsky, Noémie Elhadad, Daniel Grant, Matthew H. Mohebbi, Sean D. Mooney, Christine Suver
Indicators of retention in remote digital health studies: A cross-study evaluation of 100, 000 participants. CoRR abs/1910.01165 (2019) - 2018
- [c5]Thanneer M. Perumal
Remote Assessment, in Real-World Setting, of Tremor Severity in Parkinson's Disease Patients Using Smartphone Inertial Sensors. UbiComp/ISWC Adjunct 2018: 215-218 - [i3]Elias Chaibub Neto:
Detecting Learning vs Memorization in Deep Neural Networks using Shared Structure Validation Sets. CoRR abs/1802.07714 (2018) - 2017
- [j1]Arno Klein
Mindboggling morphometry of human brains. PLoS Comput. Biol. 13(2) (2017) - [c4]Abhishek Pratap, Joaquin A. Anguera, Brenna N. Renn
The feasibility of using smartphones to assess and remediate depression in Hispanic/Latino individuals nationally. UbiComp/ISWC Adjunct 2017: 854-860 - 2016
- [c3]Elias Chaibub Neto, Brian M. Bot, Thanneer M. Perumal, Larsson Omberg, Justin Guinney, Mike Kellen, Arno Klein, Stephen H. Friend, Andrew D. Trister:
Personalized Hypothesis Tests for Detecting Medication Response in Parkinson Disease Patients Using iPhone Sensor Data. PSB 2016: 273-284 - 2015
- [i2]Chris Gaiteri, Mingming Chen, Boleslaw K. Szymanski, Konstantin Kuzmin, Jierui Xie, Changkyu Lee, Timothy Blanche, Elias Chaibub Neto, Su-Chun Huang, Thomas J. Grabowski, Tara M. Madhyastha, Vitalina Komashko:
Identifying robust clusters and multi-community nodes by combining top-down and bottom-up approaches to clustering. CoRR abs/1501.04709 (2015) - [i1]Thomas Cokelaer
DREAMTools: a Python package for scoring collaborative challenges. F1000Research 4: 1030 (2015) - 2014
- [c2]Elias Chaibub Neto, In Sock Jang, Stephen H. Friend, Adam A. Margolin:
The Stream Algorithm: Computationally Efficient Ridge-Regression via Bayesian Model Averaging, and Applications to Pharmacogenomic Prediction of Cancer Cell Line Sensitivity. Pacific Symposium on Biocomputing 2014: 27-38 - [c1]In Sock Jang, Elias Chaibub Neto, Justin Guinney, Stephen H. Friend, Adam A. Margolin:
Systematic Assessment of Analytical Methods for Drug Sensitivity Prediction from Cancer Cell Line Data. Pacific Symposium on Biocomputing 2014: 63-74
Coauthor Index
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