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Machine learning (ML) methods are proliferating in scientific research.
Statistical methods in psychology journals: Guidelines and explanations
Leland Wilkinson · 1999
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Statistical modeling: The two cultures (with comments and a rejoinder by the author)
Leo Breiman · 2001
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Statistical modeling: The two cultures (with comments and a rejoinder by the author)
Leo Breiman · 2001
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Experimental and Quasi-Experimental Designs for Generalized Causal Inference
William R. Shadish, Thomas D. Cook, and Donald T. Campbell · 2001
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A hierarchy of limitations in machine learning, February 2020
Momin M. Malik · 2002
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Learning with skewed class distributions
Maria Carolina Monard and GEAPA Batista · 2002
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Missing data in educational research: A review of reporting practices and suggestions for improvement
James L. Peugh and Craig K. Enders · 2004
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Quality of reporting of observational longitudinal research
Leigh Tooth, Robert Ware, Chris Bain, David M. Purdie, and Annette Dobson · 2005
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Does the CONSORT checklist improve the quality of reports of randomised controlled trials? A systematic review
Amy C. Plint, David Moher, Andra Morrison, Kenneth Schulz, Douglas G. Altman, Catherine Hill, and Isabelle Gaboury · 2006
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Machine learning and its applications to biology
Adi L Tarca, Vincent J Carey, Xue wen Chen, Roberto Romero, and Sorin Drăghici · 2007
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Missing Data : A Gentle Introduction
Patrick McKnight, Katherine McKnight, Souraya Sidani, and Aurelio José Figueredo · 2007
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Auc: A misleading measure of the performance of predictive distribution models
Jorge M Lobo, Alberto Jiménez-Valverde, and Raimundo Real · 2008
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Repeatability of published microarray gene expression analyses
John P. A. Ioannidis, David B. Allison, Catherine A. Ball, Issa Coulibaly, Xiangqin Cui, Aedín C. Culhane, Mario Falchi, Cesare Furlanello, Laurence Game, Giuseppe Jurman, Jon Mangion, Tapan Mehta, Michael Nitzberg, Grier P. Page, Enrico Petretto, and Vera van Noort · 2009
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Science in the age of computer simulation
Eric Winsberg · 2010
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On over-fitting in model selection and subsequent selection bias in performance evaluation
Gavin C. Cawley and Nicola L. C. Talbot · 2010
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What is missing in counseling research? reporting missing data
William R. Sterner · 2011
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Leakage in data mining: Formulation, detection, and avoidance
Shachar Kaufman, Saharon Rosset, Claudia Perlich, and Ori Stitelman · 2012
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On the use of cross-validation for time series predictor evaluation
Christoph Bergmeir and José M. Benítez · 2012
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WILDS: A benchmark of in-the-wild distribution shifts, 2020
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2012
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Ten simple rules for reproducible computational research
Geir Kjetil Sandve, Anton Nekrutenko, James Taylor, and Eivind Hovig · 2013
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Scientists losing data at a rapid rate
Elizabeth Gibney and Richard Van Noorden · 2013
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Study quality in SLA: An assessment of designs, analyses, and reporting practices in quantitative L2 research
Luke Plonsky · 2013
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On the joys of missing data
Todd D. Little, Terrence D. Jorgensen, Kyle M. Lang, and E. Whitney G. Moore · 2013
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Applied Predictive Modeling
Max Kuhn and Kjell Johnson · 2013
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Influenza forecasting with Google Flu trends
Andrea Freyer Dugas, Mehdi Jalalpour, Yulia Gel, Scott Levin, Fred Torcaso, Takeru Igusa, and Richard E. Rothman · 2013
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Big data: New tricks for econometrics
Hal R. Varian · 2014
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Does high public debt consistently stifle economic growth? A critique of Reinhart and Rogoff
T. Herndon, M. Ash, and R. Pollin · 2014
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The availability of research data declines rapidly with article age
Timothy H. Vines, Arianne Y.K. Albert, Rose L. Andrew, Florence Débarre, Dan G. Bock, Michelle T. Franklin, Kimberly J. Gilbert, Jean-Sébastien Moore, Sébastien Renaut, and Diana J. Rennison · 2014
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Best practices for computational science: Software infrastructure and environments for reproducible and extensible research
Victoria Stodden and Sheila Miguez · 2014
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STARD 2015: An updated list of essential items for reporting diagnostic accuracy studies
Patrick M Bossuyt, Johannes B Reitsma, David E Bruns, Constantine A Gatsonis, Paul P Glasziou, Les Irwig, Jeroen G Lijmer, David Moher, Drummond Rennie, Henrica C W de Vet, Herbert Y Kressel, Nader Rifai, Robert M Golub, Douglas G Altman, Lotty Hooft, Daniël A Korevaar, and Jérémie F Cohen · 2015
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Strengthening the Reporting of Observational Studies in Epidemiology for respondent-driven sampling studies: “STROBE-RDS” statement
Richard G. White, Avi J. Hakim, Matthew J. Salganik, Michael W. Spiller, Lisa G. Johnston, Ligia Kerr, Carl Kendall, Amy Drake, David Wilson, Kate Orroth, Matthias Egger, and Wolfgang Hladik · 2015
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Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD statement
Gary S. Collins, Johannes B. Reitsma, Douglas G. Altman, and Karel GM Moons · 2015
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Principles and guidelines for reporting preclinical research. https://www.nih.gov/research-training/rigor-reproducibility/principles-guidelines-reporting-preclinical-research, August 2015
2015
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Reproducing statistical results
Victoria Stodden · 2015
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Promoting an open research culture
B. A. Nosek, G. Alter, G. C. Banks, D. Borsboom, S. D. Bowman, S. J. Breckler, S. Buck, C. D. Chambers, G. Chin, G. Christensen, M. Contestabile, A. Dafoe, E. Eich, J. Freese, R. Glennerster, D. Goroff, D. P. Green, B. Hesse, M. Humphreys, J. Ishiyama, D. Karlan, A. Kraut, A. Lupia, P. Mabry, T. Madon, N. Malhotra, E. Mayo-Wilson, M. McNutt, E. Miguel, E. Levy Paluck, U. Simonsohn, C. Soderberg, B. A. Spellman, J. Turitto, G. VandenBos, S. Vazire, E. J. Wagenmakers, R. Wilson, and T. Yarkoni · 2015
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Nature Methods
Reviewing computational methods · 2015
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Reporting and interpreting quantitative research findings: What gets reported and recommendations for the field
Jenifer Larson-Hall and Luke Plonsky · 2015
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Let’s (not) stick together: Pairwise similarity biases cross-validation in activity recognition
Nils Y. Hammerla and Thomas Plötz · 2015
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Machine learning, statistical learning and the future of biological research in psychiatry
R. Iniesta, D. Stahl, and P. McGuffin · 2016
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synthpop : Bespoke creation of synthetic data in R
Beata Nowok, Gillian M. Raab, and Chris Dibben · 2016
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Garbage in, garbage out: Data collection, quality assessment and reporting standards for social media data use in health research, infodemiology and digital disease detection
Yoonsang Kim, Jidong Huang, and Sherry Emery · 2016
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Attrition in developmental psychology
Jody S. Nicholson, Pascal R. Deboeck, and Waylon Howard · 2016
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Data cleaning: Overview and emerging challenges
Xu Chu, Ihab F Ilyas, Sanjay Krishnan, and Jiannan Wang · 2016
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Applications of machine learning in animal behaviour studies
John Joseph Valletta, Colin Torney, Michael Kings, Alex Thornton, and Joah Madden · 2017
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Choosing prediction Over explanation in psychology: Lessons from machine learning
Tal Yarkoni and Jacob Westfall · 2017
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Machine learning: An applied econometric approach
Sendhil Mullainathan and Jann Spiess · 2017
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Meaningless comparisons lead to false optimism in medical machine learning
Orianna DeMasi, Konrad Kording, and Benjamin Recht · 2017
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Prediction and explanation in social systems
Jake M. Hofman, Amit Sharma, and Duncan J. Watts · 2017
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On the reproducibility of psychological science
Valen E. Johnson, Richard D. Payne, Tianying Wang, Alex Asher, and Soutrik Mandal · 2017
Earlier work this paper cites.
A checklist is associated with increased quality of reporting preclinical biomedical research: A systematic review
SeungHye Han, Tolani F. Olonisakin, John P. Pribis, Jill Zupetic, Joo Heung Yoon, Kyle M. Holleran, Kwonho Jeong, Nader Shaikh, Doris M. Rubio, and Janet S. Lee · 2017
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50 years of data science
David Donoho · 2017
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Simulating the dynamics of socio-economic systems
Jürgen Pfeffer and Momin M. Malik · 2017
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Machine learning phases of matter
Juan Carrasquilla and Roger G. Melko · 2017
Earlier work this paper cites.
Prediction and explanation in social systems
Jake M. Hofman, Amit Sharma, and Duncan J. Watts · 2017
Earlier work this paper cites.
Constraints on generality (COG): A proposed addition to all empirical papers
Daniel J. Simons, Yuichi Shoda, and D. Stephen Lindsay · 2017
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Reproducible and reusable research: Are journal data sharing policies meeting the mark?
Nicole A. Vasilevsky, Jessica Minnier, Melissa A. Haendel, and Robin E. Champieux · 2017
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Quality of missing data reporting and handling in palliative care trials demonstrates that further development of the CONSORT statement is required: a systematic review
Jamilla A. Hussain, Martin Bland, Dean Langan, Miriam J. Johnson, David C. Currow, and Ian R. White · 2017
Earlier work this paper cites.
Prediction and explanation in social systems
Jake M Hofman, Amit Sharma, and Duncan J Watts · 2017
Earlier work this paper cites.
The theory is predictive, but is it complete? An application to human perception of randomness
Jon Kleinberg, Annie Liang, and Sendhil Mullainathan · 2017
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Reproducibility of benchmarked deep reinforcement learning tasks for continuous control
Riashat Islam, Peter Henderson, Maziar Gomrokchi, and Doina Precup · 2017
Earlier work this paper cites.
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
David R. Roberts, Volker Bahn, Simone Ciuti, Mark S. Boyce, Jane Elith, Gurutzeta Guillera-Arroita, Severin Hauenstein, José J. Lahoz-Monfort, Boris Schröder, Wilfried Thuiller, David I. Warton, Brendan A. Wintle, Florian Hartig, and Carsten F. Dormann · 2017
Earlier work this paper cites.
Supervised machine learning for population genetics: A new paradigm
Daniel R. Schrider and Andrew D. Kern · 2018
Cited alongside, same era.
Big data methods: Leveraging modern data analytic techniques to build organizational science
Scott Tonidandel, Eden B. King, and Jose M. Cortina · 2018
Cited alongside, same era.
State of the art: Reproducibility in artificial intelligence
Odd Erik Gundersen and Sigbjørn Kjensmo · 2018
Cited alongside, same era.
Winner’s curse? on pace, progress, and empirical rigor
D. Sculley, Jasper Snoek, Alex Wiltschko, and Ali Rahimi · 2018
Cited alongside, same era.
On reproducible AI: Towards reproducible research, open science, and digital scholarship in AI publications
Odd Erik Gundersen, Yolanda Gil, and David W. Aha · 2018
Cited alongside, same era.
Big data and machine learning in health care
Andrew L. Beam and Isaac S. Kohane · 2018
The MDAR (Materials Design Analysis Reporting) framework for transparent reporting in the life sciences
Malcolm Macleod, Andrew M. Collings, Chris Graf, Veronique Kiermer, David Mellor, Sowmya Swaminathan, Deborah Sweet, and Valda Vinson · 2021
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What is your estimand? Defining the target quantity connects statistical evidence to theory
Ian Lundberg, Rebecca Johnson, and Brandon M. Stewart · 2021
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Describing populations and samples in doctoral student research
Alex Casteel and Nancy Bridier · 2021
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Discussion of breiman's "two cultures": From two cultures to one
Anna Neufeld and Daniela Witten · 2021
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Comment on breiman's "two cultures" (2002): From two cultures to multicultural
Galit Shmueli · 2021
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Reasoning using data: Two old ways and one new
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Cited alongside, same era.
An empirical analysis of journal policy effectiveness for computational reproducibility
Victoria Stodden, Jennifer Seiler, and Zhaokun Ma · 2018
Cited alongside, same era.
Deep Reinforcement Learning That Matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
Cited alongside, same era.
Bash scripting, October 2018
Harbert · 2018
Cited alongside, same era.
Managing missing data in patient registries: Addendum to registries for evaluating patient outcomes: A user’s guide (Third edition)
Christina Mack, Zhaohui Su, and Daniel Westreich · 2018
Cited alongside, same era.
Methods to detect low quality data and its implication for psychological research
Erin M Buchanan and John E Scofield · 2018
Cited alongside, same era.
Prediction of incident hypertension within the next year: Prospective study using statewide electronic health records and machine learning
Chengyin Ye, Tianyun Fu, Shiying Hao, Yan Zhang, Oliver Wang, Bo Jin, Minjie Xia, Modi Liu, Xin Zhou, Qian Wu, Yanting Guo, Chunqing Zhu, Yu-Ming Li, Devore S Culver, Shaun T Alfreds, Frank Stearns, Karl G Sylvester, Eric Widen, Doff McElhinney, and Xuefeng Ling · 2018
Cited alongside, same era.
Michael Baiocchi and Jordan Rodu · 2021
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Causal modelling: The two cultures
Elizabeth L. Ogburn and Ilya Shpitser · 2021
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Machine learning for social science: An agnostic approach
Justin Grimmer, Margaret Roberts, and Brandon Stewart · 2021
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Machine learning in health care and laboratory medicine: General overview of supervised learning and auto-ML
Hooman H. Rashidi, Nam Tran, Samer Albahra, and Luke T. Dang · 2021
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Retraction for Shu et al., Signing at the beginning makes ethics salient and decreases dishonest self-reports in comparison to signing at the end
May R. Berenbaum · 2021
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Expanding the scope of reproducibility research through data analysis replications
Jake M. Hofman, Daniel G. Goldstein, Siddhartha Sen, Forough Poursabzi-Sangdeh, Jennifer Allen, Ling Liang Dong, Brenda Fried, Harpreet Gaur, Adnan Hoq, Emeka Mbazor, Naomi Moreira, Cindy Muso, Etta Rapp, and Roymil Terrero · 2021
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Overly optimistic prediction results on imbalanced data: A case study of flaws and benefits when applying over-sampling
Gilles Vandewiele, Isabelle Dehaene, György Kovács, Lucas Sterckx, Olivier Janssens, Femke Ongenae, Femke De Backere, Filip De Turck, Kristien Roelens, Johan Decruyenaere, Sofie Van Hoecke, and Thomas Demeester · 2021
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Mitigating dataset harms requires stewardship: Lessons from 1000 papers
Kenneth Peng, Arunesh Mathur, and Arvind Narayanan · 2021
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford · 2021
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Measurement and fairness
Abigail Z. Jacobs and Hanna Wallach · 2021
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Three cheers for descriptive statistics—and five more reasons why they matter
Marcus Credé and P. D. Harms · 2021
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Unrepresentative big surveys significantly overestimated US vaccine uptake
Valerie C. Bradley, Shiro Kuriwaki, Michael Isakov, Dino Sejdinovic, Xiao-Li Meng, and Seth Flaxman · 2021
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Overly optimistic prediction results on imbalanced data: A case study of flaws and benefits when applying over-sampling
Gilles Vandewiele, Isabelle Dehaene, György Kovács, Lucas Sterckx, Olivier Janssens, Femke Ongenae, Femke De Backere, Filip De Turck, Kristien Roelens, Johan Decruyenaere, et al · 2021
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Hyperparameter Optimization Is Deceiving Us, and How to Stop It
A. Feder Cooper, Yucheng Lu, Jessica Forde, and Christopher M De Sa · 2021
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Epic’s sepsis algorithm is going off the rails in the real world. The use of these variables may explain why, September 2021
Casey Ross · 2021
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Data leakage in health outcomes prediction with machine learning. Comment on “Prediction of incident hypertension within the next year: Prospective study using statewide electronic health records and machine learning”
Alexandre Chiavegatto Filho, André Filipe De Moraes Batista, and Hellen Geremias Dos Santos · 2021
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Model Selection’s Disparate Impact in Real-World Deep Learning Applications, September 2021
Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey, Chris De Sa, and Michael Littman · 2021
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Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Umang Bhatt, Javier Antorán, Yunfeng Zhang, Q. Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Melançon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, Lama Nachman, Rumi Chunara, Madhulika Srikumar, Adrian Weller, and Alice Xiang · 2021
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Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training
Shangshu Qian, Viet Hung Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, and Sameena Shah · 2021
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Are we learning yet? A meta review of evaluation failures across machine learning
Thomas Liao, Rohan Taori, Deborah Raji, and Ludwig Schmidt · 2021
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A fine-grained analysis on distribution shift, 2021
Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre Alvise-Rebuffi, Ira Ktena, Krishnamurthy Dvijotham, and Taylan Cemgil · 2021
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The clinician and dataset shift in artificial intelligence
Samuel G. Finlayson, Adarsh Subbaswamy, Karandeep Singh, John Bowers, Annabel Kupke, Jonathan Zittrain, Isaac S. Kohane, and Suchi Saria · 2021
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Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences
Anja K. Leist, Matthias Klee, Jung Hyun Kim, David H. Rehkopf, Stéphane P. A. Bordas, Graciela Muniz-Terrera, and Sara Wade · 2022
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The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning
Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, and Arvind Narayanan · 2022
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Machine learning for medical imaging: Methodological failures and recommendations for the future
Gaël Varoquaux and Veronika Cheplygina · 2022
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Do machine learning platforms provide out-of-the-box reproducibility?
Odd Erik Gundersen, Saeid Shamsaliei, and Richard Juul Isdahl · 2022
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Improving reproducibility in machine learning research (a report from the NeurIPS 2019 reproducibility program)
Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d’Alché Buc, Emily Fox, and Hugo Larochelle · 2022
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How is model-related uncertainty quantified and reported in different disciplines?
Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen, Janne Cathrin Hetle Aspheim, Claudia Battistin, Nicola Bulso, Hannah Christensen, Benjamin Cretois, Ryan Cubero, Ivan A. Davidovich, Lisa Dickel, Benjamin Dunn, Etienne Dunn-Sigouin, Karin Dyrstad, Sigurd Einum, Donata Giglio, Haakon Gjerlow, Amelie Godefroidt, Ricardo Gonzalez-Gil, Soledad Gonzalo Cogno, Fabian Grosse, Paul Halloran, Mari F. Jensen, John James Kennedy, Peter Egge Langsaether, Jack H. Laverick, Debora Lederberger, Camille Li, Elizabeth Mandeville, Caitlin Mandeville, Espen Moe, Tobias Navarro Schroder, David Nunan, Jorge Sicacha Parada, Melanie Rae Simpson, Emma Sofie Skarstein, Clemens Spensberger, Richard Stevens, Aneesh Subramanian, Lea Svendsen, Ole Magnus Theisen, Connor Watret, and Robert B. OHara · 2022
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Leakage and the reproducibility crisis in ML-based science, July 2022
Sayash Kapoor and Arvind Narayanan · 2022
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On the opportunities and risks of foundation models, 2022
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al · 2022
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OpenAI’s policies hinder reproducible research on language models, 2022
Sayash Kapoor and Arvind Narayanan · 2022
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Text as data: A new framework for Machine Learning and the Social Sciences
Justin Grimmer, Margaret E. Roberts, and Brandon Stewart · 2022
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Researcher reasoning meets computational capacity: Machine learning for social science
Ian Lundberg, Jennie E. Brand, and Nanum Jeon · 2022
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Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based
Liam G. McCoy, Connor T.A. Brenna, Stacy S. Chen, Karina Vold, and Sunit Das · 2022
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Retraction note: A mechanistic model of the neural entropy increase elicited by psychedelic drugs
Rubén Herzog, Pedro A. M. Mediano, Fernando E. Rosas, Robin Carhart - · 2022
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Many researchers were not compliant with their published data sharing statement: A mixed-methods study
Mirko Gabelica, Ružica Bojčić, and Livia Puljak · 2022
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Data and code availability standard
Koren, Miklós, Connolly, Marie, Lull, Joan, and Vilhuber, Lars · 2022
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Completeness of reporting of clinical prediction models developed using supervised machine learning: A systematic review
Constanza L. Andaur Navarro, Johanna A. A. Damen, Toshihiko Takada, Steven W. J. Nijman, Paula Dhiman, Jie Ma, Gary S. Collins, Ram Bajpai, Richard D. Riley, Karel G. M. Moons, and Lotty Hooft · 2022
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Missing data is poorly handled and reported in prediction model studies using machine learning: A literature review
SWJ Nijman, AM Leeuwenberg, I Beekers, I Verkouter, JJL Jacobs, ML Bots, FW Asselbergs, KGM Moons, and TPA Debray · 2022
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Classification of datasets with imputed missing values: Does imputation quality matter?
Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramon Vinas Torne, Evis Sala, Pietro Lio, et al · 2022
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Deep learning for automatic brain tumour segmentation on mri: Evaluation of recommended reporting criteria via a reproduction and replication study
Emilia Gryska, Isabella Björkman-Burtscher, Asgeir Store Jakola, Tora Dunås, Justin Schneiderman, and Rolf A Heckemann · 2022
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Model multiplicity: Opportunities, concerns, and solutions
Emily Black, Manish Raghavan, and Solon Barocas · 2022
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How is model-related uncertainty quantified and reported in different disciplines?
Emily G Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen, Janne Cathrin Hetle Aspheim, Claudia Battistin, Nicola Bulso, Hannah Christensen, Benjamin Cretois, Ryan Cubero, Ivan A Davidovich, et al · 2022
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Elements of external validity: Framework, design, and analysis
Naoki Egami and Erin Hartman · 2022
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The fallacy of AI functionality
Inioluwa Deborah Raji, I. Elizabeth Kumar, Aaron Horowitz, and Andrew Selbst · 2022
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Navigating the development challenges in creating complex data systems
Sören Dittmer, Michael Roberts, Julian Gilbey, Ander Biguri, AIX-COVNET Collaboration, Ian Selby, Anna Breger, Matthew Thorpe, Jonathan R. Weir-McCall, Effrossyni Gkrania-Klotsas, Anna Korhonen, Emily Jefferson, Georg Langs, Guang Yang, Helmut Prosch, Jan Stanczuk, Jing Tang, Judith Babar, Lorena Escudero Sánchez, Philip Teare, Mishal Patel, Marcel Wassin, Markus Holzer, Nicholas Walton, Pietro Lió, Tolou Shadbahr, Evis Sala, Jacobus Preller, James H. F. Rudd, John A. D. Aston, and Carola-Bibiane Schönlieb · 2023
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Overfitting to ‘predict’ suicidal ideation
Timothy Verstynen and Konrad Paul Kording · 2023
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NeurIPS 2023 paper guidelines. https://neurips.cc/public/guides/PaperChecklist
2023
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ICML 2023 paper guidelines. https://icml.cc/Conferences/2023/PaperGuidelines
2023
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Predictive Multiplicity in Probabilistic Classification | Proceedings of the AAAI Conference on Artificial Intelligence
Jamelle Watson-Daniels, David C. Parkes, and Berk Ustun · 2023
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Benchmarking Neural Network Training Algorithms, June 2023
George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, and Peter Mattson · 2023
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How to avoid machine learning pitfalls: A guide for academic researchers, February 2023
Michael A. Lones · 2023
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A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon Kleinberg, Siddhartha Sen, and Baobao Zhang · 2023
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Manipulative tactics are the norm in political emails: Evidence from 300k emails from the 2020 US election cycle
Arunesh Mathur, Angelina Wang, Carsten Schwemmer, Maia Hamin, Brandon M Stewart, and Arvind Narayanan · 2023
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Systematic review finds “spin” practices and poor reporting standards in studies on machine learning-based prediction models
Constanza L. Andaur Navarro, Johanna A.A. Damen, Toshihiko Takada, Steven W.J. Nijman, Paula Dhiman, Jie Ma, Gary S. Collins, Ram Bajpai, Richard D. Riley, Karel G.M. Moons, and Lotty Hooft · 2023
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