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Elias Chaibub Neto
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2020 – today
- 2024
- [j6]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) - [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, Jennifer Schaff, Marlena Duda, Bálint Á Pataki, Ming Sun, Phil Snyder, Jean-Francois Daneault, Federico Parisi, Gianluca Costante, Udi Rubin, Peter Banda, Yooree Chae, Elias Chaibub Neto, Earl Ray Dorsey, Zafer Aydin, Aipeng Chen, Laura L. Elo, Carlos Espino, Enrico Glaab, Ethan Goan, Fatemeh Noushin Golabchi, Yasin Görmez, Maria K. Jaakkola, Jitendra Jonnagaddala, Riku Klén, Dongmei Li, Christian McDaniel, Dimitri Perrin, Thanneer M. Perumal, Nastaran Mohammadian Rad, Erin Rainaldi, Stefano Sapienza, Patrick Schwab, Nikolai Shokhirev, Mikko S. Venäläinen, Gloria Vergara-Diaz, Yuqian Zhang, Avner G. S. Abrami, Aditya Adhikary, Carla Agurto, Sherry Bhalla, Halil Ibrahim Bilgin, Vittorio Caggiano, Jun Cheng, Eden Deng, Qiwei Gan, Rajan Girsa, Zhi Han, Stephen Heisig, Kun Huang, Samad Jahandideh, Wolfgang Kopp, Christoph F. Kurz, Gregor Lichtner, Raquel Norel, G. P. S. Raghava, Tavpritesh Sethi, Nicholas Shawen, Vaibhav Tripathi, Matthew Tsai, Tongxin Wang, Yi Wu, Jie Zhang, Xinyu Zhang, Yuanjia Wang, Yuanfang Guan, Daniela Brunner, Paolo Bonato, Lara M. Mangravite, Larsson Omberg:
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, Elias Chaibub Neto, Phil Snyder, Carl Stepnowsky, Noémie Elhadad, Daniel Grant, Matthew H. Mohebbi, Sean D. Mooney, Christine Suver, John Wilbanks, Lara M. Mangravite, Patrick J. Heagerty, Pat A. Areán, Larsson Omberg:
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, Thanneer M. Perumal, Meghasyam Tummalacherla, Phil Snyder, Brian M. Bot, Andrew D. Trister, Stephen H. Friend, Lara M. Mangravite, Larsson Omberg:
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, Meghasyam Tummalacherla, Brian M. Bot, Lara M. Mangravite, Larsson Omberg:
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, John Wilbanks, Lara M. Mangravite, Patricki J. Heagerty, Pat A. Areán, Larsson Omberg:
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, Meghasyam Tummalacherla, Phil Snyder, Elias Chaibub Neto, Earl Ray Dorsey, Lara M. Mangravite, Larsson Omberg:
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, Satrajit S. Ghosh, Forrest Sheng Bao, Joachim Giard, Yrjö Häme, Eliezer Stavsky, Noah Lee, Brian Rossa, Martin Reuter, Elias Chaibub Neto, Anisha Keshavan:
Mindboggling morphometry of human brains. PLoS Comput. Biol. 13(2) (2017) - [c4]Abhishek Pratap, Joaquin A. Anguera, Brenna N. Renn, Elias Chaibub Neto, Joshua Volponi, Sean D. Mooney, Pat A. Areán:
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, Mukesh Bansal, Christopher Bare, Erhan Bilal, Brian M. Bot, Elias Chaibub Neto, Federica Eduati, Mehmet Gönen, Steven M. Hill, Bruce R. Hoff, Jonathan R. Karr, Robert Küffner, Michael P. Menden, Pablo Meyer, Raquel Norel, Abhishek Pratap, Robert J. Prill, Matthew T. Weirauch, James C. Costello, Gustavo Stolovitzky, Julio Saez-Rodriguez:
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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last updated on 2024-06-10 21:25 CEST by the dblp team
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