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2020 – today
- 2024
[j12]Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau:
Fortuna: A Library for Uncertainty Quantification in Deep Learning. J. Mach. Learn. Res. 25: 238:1-238:7 (2024)- [j11]Lukas Balles, Prabhu Teja Sivaprasad, Cédric Archambeau:
On the Choice of Learning Rate for Local SGD. Trans. Mach. Learn. Res. 2024 (2024) - [c53]Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger:
Explaining Probabilistic Models with Distributional Values. ICML 2024 - [i39]Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger:
Explaining Probabilistic Models with Distributional Values. CoRR abs/2402.09947 (2024) - [i38]Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cédric Archambeau:
Structural Pruning of Pre-trained Language Models via Neural Architecture Search. CoRR abs/2405.02267 (2024) - 2023
- [c52]Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi:
Geographical Erasure in Language Generation. EMNLP (Findings) 2023: 12310-12324 - [c51]Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella:
PASHA: Efficient HPO and NAS with Progressive Resource Allocation. ICLR 2023 - [c50]David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau:
Optimizing Hyperparameters with Conformal Quantile Regression. ICML 2023: 29876-29893 - [c49]Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger:
Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. TML4H 2023: 11-24 - [i37]Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau:
Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR abs/2302.04019 (2023) - [i36]Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella:
Renate: A Library for Real-World Continual Learning. CoRR abs/2304.12067 (2023) - [i35]David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau:
Optimizing Hyperparameters with Conformal Quantile Regression. CoRR abs/2305.03623 (2023) - [i34]Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi:
Geographical Erasure in Language Generation. CoRR abs/2310.14777 (2023) - [i33]Lukas Balles, Cédric Archambeau, Giovanni Zappella:
A Negative Result on Gradient Matching for Selective Backprop. CoRR abs/2312.05021 (2023) - 2022
- [c48]Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau:
Automatic Termination for Hyperparameter Optimization. AutoML 2022: 7/1-21 - [c47]David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau:
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML 2022: 16/1-23 - [c46]Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau:
Continual Learning with Transformers for Image Classification. CVPR Workshops 2022: 3773-3780 - [c45]Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau:
Memory Efficient Continual Learning with Transformers. NeurIPS 2022 - [c44]Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu:
Private Synthetic Data for Multitask Learning and Marginal Queries. NeurIPS 2022 - [i32]Beyza Ermis, Giovanni Zappella, Martin Wistuba, Cédric Archambeau:
Memory Efficient Continual Learning for Neural Text Classification. CoRR abs/2203.04640 (2022) - [i31]Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau:
Diverse Counterfactual Explanations for Anomaly Detection in Time Series. CoRR abs/2203.11103 (2022) - [i30]Lukas Balles, Giovanni Zappella, Cédric Archambeau:
Gradient-Matching Coresets for Rehearsal-Based Continual Learning. CoRR abs/2203.14544 (2022) - [i29]Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau:
Continual Learning with Transformers for Image Classification. CoRR abs/2206.14085 (2022) - [i28]Ondrej Bohdal, Lukas Balles, Beyza Ermis, Cédric Archambeau, Giovanni Zappella:
PASHA: Efficient HPO with Progressive Resource Allocation. CoRR abs/2207.06940 (2022) - [i27]Gianluca Detommaso, Alberto Gasparin, Andrew Wilson, Cédric Archambeau:
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors. CoRR abs/2207.08200 (2022) - [i26]Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth
, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu:
Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR abs/2209.07400 (2022) - 2021
- [c43]Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi:
On the Lack of Robust Interpretability of Neural Text Classifiers. ACL/IJCNLP (Findings) 2021: 3730-3740 - [c42]Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau:
Fair Bayesian Optimization. AIES 2021: 854-863 - [c41]Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau:
Hyperparameter Transfer Learning with Adaptive Complexity. AISTATS 2021: 1378-1386 - [c40]Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos:
BORE: Bayesian Optimization by Density-Ratio Estimation. ICML 2021: 10289-10300 - [c39]Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau:
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD 2021: 3463-3471 - [c38]Beyza Ermis, Giovanni Zappella, Cédric Archambeau:
Towards robust episodic meta-learning. UAI 2021: 1342-1351 - [i25]Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos:
BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR abs/2102.09009 (2021) - [i24]Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau:
Hyperparameter Transfer Learning with Adaptive Complexity. CoRR abs/2102.12810 (2021) - [i23]Giovanni Zappella, David Salinas, Cédric Archambeau:
A resource-efficient method for repeated HPO and NAS problems. CoRR abs/2103.16111 (2021) - [i22]Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau:
Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. CoRR abs/2104.08166 (2021) - [i21]Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi:
On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR abs/2106.04631 (2021) - [i20]David Salinas, Valerio Perrone, Olivier Cruchant, Cédric Archambeau:
A multi-objective perspective on jointly tuning hardware and hyperparameters. CoRR abs/2106.05680 (2021) - [i19]Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau:
Multi-objective Asynchronous Successive Halving. CoRR abs/2106.12639 (2021) - [i18]Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias W. Seeger, Cédric Archambeau:
Meta-Forecasting by combining Global Deep Representations with Local Adaptation. CoRR abs/2111.03418 (2021) - [i17]Lukas Balles, Giovanni Zappella, Cédric Archambeau:
Gradient-matching coresets for continual learning. CoRR abs/2112.05025 (2021) - [i16]Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi:
More Than Words: Towards Better Quality Interpretations of Text Classifiers. CoRR abs/2112.12444 (2021) - 2020
- [c37]Cuong V. Nguyen, Tal Hassner, Matthias W. Seeger, Cédric Archambeau:
LEEP: A New Measure to Evaluate Transferability of Learned Representations. ICML 2020: 7294-7305 - [c36]Piali Das, Nikita Ivkin, Tanya Bansal, Laurence Rouesnel, Philip Gautier, Zohar S. Karnin, Leo Dirac, Lakshmi Ramakrishnan, Andre Perunicic, Iaroslav Shcherbatyi, Wilton Wu, Aida Zolic, Huibin Shen, Amr Ahmed, Fela Winkelmolen, Miroslav Miladinovic, Cédric Archambeau, Alex Tang, Bhaskar Dutt, Patricia Grao, Kumar Venkateswar:
Amazon SageMaker Autopilot: a white box AutoML solution at scale. DEEM@SIGMOD 2020: 2:1-2:7 - [i15]Cuong V. Nguyen, Tal Hassner, Cédric Archambeau, Matthias W. Seeger:
LEEP: A New Measure to Evaluate Transferability of Learned Representations. CoRR abs/2002.12462 (2020) - [i14]Louis C. Tiao, Aaron Klein, Cédric Archambeau, Matthias W. Seeger:
Model-based Asynchronous Hyperparameter Optimization. CoRR abs/2003.10865 (2020) - [i13]Eric Hans Lee, Valerio Perrone, Cédric Archambeau, Matthias W. Seeger:
Cost-aware Bayesian Optimization. CoRR abs/2003.10870 (2020) - [i12]Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau:
Fair Bayesian Optimization. CoRR abs/2006.05109 (2020) - [i11]Gauthier Guinet, Valerio Perrone, Cédric Archambeau:
Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization. CoRR abs/2011.11456 (2020) - [i10]Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar S. Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Liénart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias W. Seeger, Bhaskar Dutt, Laurence Rouesnel:
Amazon SageMaker Autopilot: a white box AutoML solution at scale. CoRR abs/2012.08483 (2020) - [i9]Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau:
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. CoRR abs/2012.08489 (2020)
2010 – 2019
- 2019
- [i8]Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton:
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. CoRR abs/1909.12552 (2019) - [i7]Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cédric Archambeau, Matthias W. Seeger:
Constrained Bayesian Optimization with Max-Value Entropy Search. CoRR abs/1910.07003 (2019) - 2018
- [c35]Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau:
Scalable Hyperparameter Transfer Learning. NeurIPS 2018: 6846-6856 - 2017
- [c34]Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias W. Seeger:
Bayesian Optimization with Tree-structured Dependencies. ICML 2017: 1655-1664 - [i6]Tammo Rukat, Dustin Lange, Cédric Archambeau:
An interpretable latent variable model for attribute applicability in the Amazon catalogue. CoRR abs/1712.00126 (2017) - 2016
- [c33]Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau:
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. ICML 2016: 402-411 - [c32]Jim C. Huang, Rodolphe Jenatton, Cédric Archambeau:
Online Dual Decomposition for Performance and Delivery-Based Distributed Ad Allocation. KDD 2016: 117-126 - [i5]Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau:
Online optimization and regret guarantees for non-additive long-term constraints. CoRR abs/1602.05394 (2016) - 2015
- [j10]Cédric Archambeau, Balaji Lakshminarayanan, Guillaume Bouchard:
Latent IBP Compound Dirichlet Allocation. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 321-333 (2015) - [c31]Antonino Freno, Martin Saveski, Rodolphe Jenatton, Cédric Archambeau:
One-Pass Ranking Models for Low-Latency Product Recommendations. KDD 2015: 1789-1798 - [i4]Maxim Rabinovich, Cédric Archambeau:
Online Inference for Relation Extraction with a Reduced Feature Set. CoRR abs/1504.04770 (2015) - [i3]Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau:
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. CoRR abs/1512.07422 (2015) - 2014
- [c30]Tiziano Piccardi, Gregorio Convertino, Massimo Zancanaro, Ji Wang, Cédric Archambeau:
Towards crowd-based customer service: a mixed-initiative tool for managing Q&A sites. CHI 2014: 2725-2734 - [i2]Behrouz Behmardi, Cédric Archambeau, Guillaume Bouchard:
Overlapping Trace Norms in Multi-View Learning. CoRR abs/1404.6163 (2014) - 2013
- [c29]Anil Kumar Nelakanti, Cédric Archambeau, Julien Mairal, Francis R. Bach, Guillaume Bouchard:
Structured Penalties for Log-Linear Language Models. EMNLP 2013: 233-243 - [c28]Michael Kaschesky, Pawel Sobkowicz, José Miguel Hernández-Lobato, Guillaume Bouchard, Cédric Archambeau, Nicolas Scharioth, Robert Manchin, Adrian Gschwend, Reinhard Riedl:
Bringing Representativeness into Social Media Monitoring and Analysis. HICSS 2013: 2003-2012 - [c27]William Darling, Cédric Archambeau, Shachar Mirkin, Guillaume Bouchard:
Error Prediction with Partial Feedback. ECML/PKDD (2) 2013: 80-94 - [c26]Dawei Yin, Shengbo Guo, Boris Chidlovskii, Brian D. Davison, Cédric Archambeau, Guillaume Bouchard:
Connecting comments and tags: improved modeling of social tagging systems. WSDM 2013: 547-556 - 2012
- [j9]Yuan Shen, Dan Cornford, Manfred Opper, Cédric Archambeau:
Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions. Comput. Stat. 27(1): 149-176 (2012) - [c25]Cédric Archambeau, Francois Caron:
Plackett-Luce regression: A new Bayesian model for polychotomous data. UAI 2012: 84-92 - 2011
- [j8]Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh:
The sequence memoizer. Commun. ACM 54(2): 91-98 (2011) - [c24]Benjamin V. Hanrahan, Guillaume Bouchard, Gregorio Convertino, Thiébaud Weksteen, Nicholas Kong, Cédric Archambeau, Ed H. Chi:
Mail2Wiki: low-cost sharing and early curation from email to wikis. C&T 2011: 98-107 - [c23]Benjamin V. Hanrahan, Thiébaud Weksteen, Nicholas Kong, Gregorio Convertino, Guillaume Bouchard, Cédric Archambeau, Ed H. Chi:
Mail2Wiki: posting and curating Wiki content from email. IUI 2011: 441-442 - [c22]Cédric Archambeau, Shengbo Guo, Onno Zoeter:
Sparse Bayesian Multi-Task Learning. NIPS 2011: 1755-1763 - [c21]Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau:
Robust Bayesian Matrix Factorisation. AISTATS 2011: 425-433 - 2010
- [j7]Yuan Shen, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor, Remi Louis Barillec:
A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems. J. Signal Process. Syst. 61(1): 51-59 (2010)
2000 – 2009
- 2009
- [j6]Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cédric Archambeau:
Switching regulatory models of cellular stress response. Bioinform. 25(10): 1280-1286 (2009) - [j5]Stefano Lise, Cédric Archambeau, Massimiliano Pontil, David T. Jones:
Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. BMC Bioinform. 10: 365 (2009) - [j4]Manfred Opper, Cédric Archambeau:
The Variational Gaussian Approximation Revisited. Neural Comput. 21(3): 786-792 (2009) - [c20]Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh:
A stochastic memoizer for sequence data. ICML 2009: 1129-1136 - 2008
- [j3]Cédric Archambeau, Nicolas Delannay, Michel Verleysen:
Mixtures of robust probabilistic principal component analyzers. Neurocomputing 71(7-9): 1274-1282 (2008) - [c19]François-Xavier Standaert, Cédric Archambeau:
Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages. CHES 2008: 411-425 - [c18]Cédric Archambeau, Francis R. Bach:
Sparse probabilistic projections. NIPS 2008: 73-80 - [c17]Nicolas Delannay, Cédric Archambeau, Michel Verleysen:
Improving the Robustness to Outliers of Mixtures of Probabilistic PCAs. PAKDD 2008: 527-535 - 2007
- [j2]Cédric Archambeau, Michel Verleysen:
Robust Bayesian clustering. Neural Networks 20(1): 129-138 (2007) - [c16]Cédric Archambeau, Nicolas Delannay, Michel Verleysen:
Mixtures of robust probabilistic principal component analyzers. ESANN 2007: 229-234 - [c15]Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor:
Variational Inference for Diffusion Processes. NIPS 2007: 17-24 - [c14]Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor:
Gaussian Process Approximations of Stochastic Differential Equations. Gaussian Processes in Practice 2007: 1-16 - [i1]François-Xavier Standaert, Eric Peeters, Cédric Archambeau, Jean-Jacques Quisquater:
Towards Security Limits in Side-Channel Attacks. IACR Cryptol. ePrint Arch. 2007: 222 (2007) - 2006
- [c13]Cédric Archambeau, Eric Peeters, François-Xavier Standaert, Jean-Jacques Quisquater:
Template Attacks in Principal Subspaces. CHES 2006: 1-14 - [c12]François-Xavier Standaert, Eric Peeters, Cédric Archambeau, Jean-Jacques Quisquater:
Towards Security Limits in Side-Channel Attacks. CHES 2006: 30-45 - [c11]Cédric Archambeau, Nicolas Delannay, Michel Verleysen:
Robust probabilistic projections. ICML 2006: 33-40 - [c10]Nicolas Delannay, Cédric Archambeau, Michel Verleysen:
Automatic Adjustment of Discriminant Adaptive Nearest Neighbor. ICPR (2) 2006: 552-535 - [c9]Cédric Archambeau, Maurizio Valle, Alex Assenza, Michel Verleysen:
Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures. ISCAS 2006 - 2005
- [b1]Cédric Archambeau:
Probabilistic models in noisy environments : and their application to a visual prosthesis for the blind/. Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2005 - [c8]Cédric Archambeau, Michel Verleysen:
Manifold Constrained Variational Mixtures. ICANN (2) 2005: 279-284 - [c7]Cédric Archambeau, Michel Verleysen:
Manifold Constrained Finite Gaussian Mixtures. IWANN 2005: 820-828 - 2004
- [j1]Cédric Archambeau, Jean Delbeke, Claude Veraart, Michel Verleysen:
Prediction of visual perceptions with artificial neural networks in a visual prosthesis for the blind. Artif. Intell. Medicine 32(3): 183-194 (2004) - [c6]Cédric Archambeau, Frédéric Vrins, Michel Verleysen:
Flexible and Robust Bayesian Classification by Finite Mixture Models. ESANN 2004: 75-80 - [c5]Frédéric Vrins, Cédric Archambeau, Michel Verleysen:
Towards a Local Separation Performances Estimator Using Common ICA Contrast Functions? ESANN 2004: 211-216 - [c4]Cédric Archambeau, Torsten Butz, Vlad Popovici, Michel Verleysen, Jean-Philippe Thiran:
Supervised Nonparametric Information Theoretic Classification. ICPR (3) 2004: 414-417 - 2003
- [c3]Cédric Archambeau, John Aldo Lee, Michel Verleysen:
On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures. ESANN 2003: 99-106 - [c2]John Aldo Lee, Cédric Archambeau, Michel Verleysen:
Locally Linear Embedding versus Isotop. ESANN 2003: 527-534 - 2002
- [c1]Nabil Benoudjit, Cédric Archambeau, Amaury Lendasse, John Aldo Lee, Michel Verleysen:
Width optimization of the Gaussian kernels in Radial Basis Function Networks. ESANN 2002: 425-432
Coauthor Index
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