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With the increasing use of Machine Learning (ML) algorithms in scientific research comes the need for reliable uncertainty quantification.
A limited memory algorithm for bound constrained optimization
Richard H Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu · 1995
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Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization
Ciyou Zhu, Richard H Byrd, Peihuang Lu, and Jorge Nocedal · 1997
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Learning by transduction
A. Gammerman, V. Vovk, and V. Vapnik · 1998
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Statistical data analysis
Glen Cowan · 1998
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Machine-learning applications of algorithmic randomness
Volodya Vovk, Alex Gammerman, and Craig Saunders · 1999
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Testing For Normality
H.C. Thode · 2002
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Multiresolution techniques for the detection of gravitational-wave bursts
Shourov Chatterji, Lindy Blackburn, Gregory Martin, and Erik Katsavounidis · 2004
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Algorithmic learning in a random world , volume 29
Vladimir Vovk, Alexander Gammerman, and Glenn Shafer · 2005
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Increasing the reliability of reliability diagrams
Jochen Bröcker and Leonard A Smith · 2007
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Conformal prediction with neural networks
Harris Papadopoulos, Volodya Vovk, and Alex Gammerman · 2007
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A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk · 2008
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Real-time detection of unmodelled gravitational-wave transients using convolutional neural networks
Vasileios Skliris, Michael RK Norman, and Patrick J Sutton · 2009
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Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
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Interferometer design of the kagra gravitational wave detector
Yoichi Aso, Yuta Michimura, Kentaro Somiya, Masaki Ando, Osamu Miyakawa, Takanori Sekiguchi, Daisuke Tatsumi, Hiroaki Yamamoto, Kagra Collaboration, et al · 2013
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Advanced virgo: a second-generation interferometric gravitational wave detector
F. Acernese, M. Agathos, K. Agatsuma, D. Aisa, N. Allemandou, A. Allocca, J. Amarni, P. Astone, G. Balestri, G. Ballardin, et al · 2014
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Optimal thresholding of classifiers to maximize f1 measure
Zachary C Lipton, Charles Elkan, and Balakrishnan Naryanaswamy · 2014
Cited alongside, same era.
Advanced ligo
Junaid Aasi, BP Abbott, Richard Abbott, Thomas Abbott, MR Abernathy, Kendall Ackley, Carl Adams, Thomas Adams, Paolo Addesso, RX Adhikari, et al · 2015
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Observation of gravitational waves from a binary black hole merger
Benjamin P Abbott, Richard Abbott, TDe Abbott, MR Abernathy, Fausto Acernese, Kendall Ackley, Carl Adams, Thomas Adams, Paolo Addesso, Rana X Adhikari, et al · 2016
Cited alongside, same era.
Gravity spy: integrating advanced ligo detector characterization, machine learning, and citizen science
Michael Zevin, Scott Coughlin, Sara Bahaadini, Emre Besler, Neda Rohani, Sarah Allen, Miriam Cabero, Kevin Crowston, Aggelos K Katsaggelos, Shane L Larson, et al · 2017
Cited alongside, same era.
Model-agnostic nonconformity functions for conformal classification
Ulf Johansson, Henrik Linusson, Tuve Löfström, and Henrik Boström · 2017
Cited alongside, same era.
Simulating transient noise bursts in ligo with generative adversarial networks
Melissa Lopez, Vincent Boudart, Kerwin Buijsman, Amit Reza, and Sarah Caudill · 2022
Later among the works it cites.
Gwitchhunters: Machine learning and citizen science to improve the performance of gravitational wave detector
Massimiliano Razzano, Francesco Di Renzo, Francesco Fidecaro, Gary Hemming, and Stavros Katsanevas · 2022
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Gravity spy volunteer classifications of ligo glitches from observing runs o1, o2, o3a, and o3b
Michael Zevin, Scott Coughlin, Eve Chase, Sara Allen, Sara Bahaadini, and Christopher et al. Berry · 2022
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Machine-learning classification of astronomical sources: estimating f1-score in the absence of ground truth
A Humphrey, W Kuberski, J Bialek, N Perrakis, W Cools, N Nuyttens, H Elakhrass, and PAC Cunha · 2022
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Antiglitch: a quasi-physical model for removing short glitches from ligo and virgo data
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Mitigation of the instrumental noise transient in gravitational-wave data surrounding gw170817
Chris Pankow, Katerina Chatziioannou, Eve A. Chase, Tyson B. Littenberg, Matthew Evans, Jessica McIver, Neil J. Cornish, Carl-Johan Haster, Jonah Kanner, Vivien Raymond, Salvatore Vitale, and Aaron Zimmerman · 2018
Cited alongside, same era.
Inductive conformal predictor for convolutional neural networks: Applications to active learning for image classification
Sergio Matiz and Kenneth E Barner · 2019
Cited alongside, same era.
Efficient gravitational-wave glitch identification from environmental data through machine learning
Robert E Colgan, K Rainer Corley, Yenson Lau, Imre Bartos, John N Wright, Zsuzsa Márka, and Szabolcs Márka · 2020
Cited alongside, same era.
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Reddy, et al · 2020
Cited alongside, same era.
Ligo detector characterization in the second and third observing runs
Derek Davis, Joseph S Areeda, Beverly K Berger, R Bruntz, Anamaria Effler, RC Essick, RP Fisher, Patrick Godwin, Evan Goetz, AF Helmling-Cornell, et al · 2021
Cited alongside, same era.
Environmental noise in advanced ligo detectors
Philippe Nguyen, RMS Schofield, Anamaria Effler, Corey Austin, Vaishali Adya, Matthew Ball, Sharan Banagiri, Katherine Banowetz, C Billman, CD Blair, et al · 2021
Cited alongside, same era.
A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N Angelopoulos and Stephen Bates · 2021
Cited alongside, same era.
Ruxandra Bondarescu, Andrew Lundgren, and Ronaldas Macas · 2023
Later among the works it cites.
Convolutional neural networks for the classification of glitches in gravitational-wave data streams
Tiago Fernandes, Samuel Vieira, Antonio Onofre, Juan Calderón Bustillo, Alejandro Torres-Forné, and José A Font · 2023
Later among the works it cites.
Progressive rehabilitation based on emg gesture classification and an mpc-driven exoskeleton
Daniel Bonilla Betancourth, Manuela Bravo, Stephany Bonilla, Angela Iragorri, Diego Mendez, Iván Mondragón, Catalina Alvarado-Rojas, and Julian Colorado · 2023
Later among the works it cites.
Ligo detector characterization in the first half of the fourth observing run
S Soni, BK Berger, D Davis, F Di Renzo, A Effler, TA Ferreira, J Glanzer, E Goetz, G González, A Helmling-Cornell, et al · 2024
Closest in time.
Yunan Wu, Michael Zevin, Christopher PL Berry, Kevin Crowston, Carsten Østerlund, Zoheyr Doctor, Sharan Banagiri, Corey B Jackson, Vicky Kalogera, and Aggelos K Katsaggelos · 2024
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Calibrating gravitational-wave search algorithms with conformal prediction
Gregory Ashton, Nicolo Colombo, Ian Harry, and Surabhi Sachdev · 2024
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Example code for: "classification uncertainty for transient gravitational-wave noise artefacts with optimised conformal prediction"
Ann-Kristin Malz, Gregory Ashton, and Nicolo Colombo · 2024
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Class-conditional conformal prediction with many classes
Tiffany Ding, Anastasios Angelopoulos, Stephen Bates, Michael Jordan, and Ryan J Tibshirani · 2024
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Gravity spy: lessons learned and a path forward
Michael Zevin, Corey B Jackson, Zoheyr Doctor, Yunan Wu, Carsten Østerlund, L Clifton Johnson, Christopher PL Berry, Kevin Crowston, Scott B Coughlin, Vicky Kalogera, et al · 2024
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Entropy reweighted conformal classification
Rui Luo and Nicolo Colombo · 2024
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Ethan Marx, William Benoit, Alec Gunny, Rafia Omer, Deep Chatterjee, Ricco C Venterea, Lauren Wills, Muhammed Saleem, Eric Moreno, Ryan Raikman, et al · 2024
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