Victor Veitch

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Referințe bibliografice	4895	4012
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i10-index	35	34

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Joseph EmersonProfessor of Applied Math, University of WaterlooAdresă de e-mail confirmată pe uwaterloo.ca
David BleiProfessor of Statistics and Computer Science, Columbia UniversityAdresă de e-mail confirmată pe columbia.edu
Dhanya SridharUniversity of Montreal, Mila-Quebec AI InstituteAdresă de e-mail confirmată pe mila.quebec
Jacob EisensteinGoogle ResearchAdresă de e-mail confirmată pe google.com
Christopher FerrieUniversity of Technology SydneyAdresă de e-mail confirmată pe uts.edu.au
Reid PryzantFreestone Grove PartnersAdresă de e-mail confirmată pe stanford.edu
Zihao WangUniversity of ChicagoAdresă de e-mail confirmată pe uchicago.edu
Alexander D'AmourResearch Scientist, Google DeepMindAdresă de e-mail confirmată pe google.com
Claudia ShiPhD Student, Columbia UniversityAdresă de e-mail confirmată pe columbia.edu
Amir FederColumbia University, GoogleAdresă de e-mail confirmată pe columbia.edu
Emaad ManzoorAssistant Professor of Marketing, Cornell UniversityAdresă de e-mail confirmată pe cornell.edu
Daniel M. RoyResearch Director, Vector Institute; Prof., U. Toronto (Statistics, CS)Adresă de e-mail confirmată pe utoronto.ca
Peter OrbanzColumbia UniversityAdresă de e-mail confirmată pe stat.columbia.edu
Wenda ZhouNew York UniversityAdresă de e-mail confirmată pe nyu.edu
Morgane AusternHarvard UniversityAdresă de e-mail confirmată pe fas.harvard.edu
Lin GuiDepartment of Statistics, The University of ChicagoAdresă de e-mail confirmată pe uchicago.edu
Joel J. WallmanOrca ComputingAdresă de e-mail confirmată pe psiquantum.com
Mark HowardLecturer, University of GalwayAdresă de e-mail confirmată pe universityofgalway.ie
Roi ReichartTechnion - Israel Institute of TechnologyAdresă de e-mail confirmată pe technion.ac.il
Zach Wood-DoughtyNorthwestern UniversityAdresă de e-mail confirmată pe northwestern.edu

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Victor Veitch

University of Chicago, Google

Adresă de e-mail confirmată pe uchicago.edu - Pagina de pornire

Artificial Intelligence Machine Learning Causality Statistics


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research 23 (226), 1-61, 2022	829	2022
Contextuality supplies the ‘magic’for quantum computation M Howard, J Wallman, V Veitch, J Emerson Nature 510 (7505), 351-355, 2014	725	2014
The resource theory of stabilizer quantum computation V Veitch, SAH Mousavian, D Gottesman, J Emerson New Journal of Physics 16 (1), 013009, 2014	502	2014
Adapting neural networks for the estimation of treatment effects C Shi, D Blei, V Veitch Advances in neural information processing systems 32, 2019	459	2019
Negative quasi-probability as a resource for quantum computation V Veitch, C Ferrie, D Gross, J Emerson New Journal of Physics 14 (11), 113011, 2012	417	2012
Causal inference in natural language processing: Estimation, prediction, interpretation and beyond A Feder, KA Keith, E Manzoor, R Pryzant, D Sridhar, Z Wood-Doughty, ... Transactions of the Association for Computational Linguistics 10, 1138-1158, 2022	277	2022
Non-vacuous generalization bounds at the imagenet scale: a PAC-bayesian compression approach W Zhou, V Veitch, M Austern, RP Adams, P Orbanz arXiv preprint arXiv:1804.05862, 2018	242	2018
Counterfactual invariance to spurious correlations: Why and how to pass stress tests V Veitch, A D'Amour, S Yadlowsky, J Eisenstein arXiv preprint arXiv:2106.00545, 2021	172*	2021
Adapting text embeddings for causal inference V Veitch, D Sridhar, D Blei Conference on Uncertainty in Artificial Intelligence, 919-928, 2020	147	2020
Efficient simulation scheme for a class of quantum optics experiments with non-negative Wigner representation V Veitch, N Wiebe, C Ferrie, J Emerson New Journal of Physics 15 (1), 013037, 2013	139	2013
The linear representation hypothesis and the geometry of large language models K Park, YJ Choe, V Veitch arXiv preprint arXiv:2311.03658, 2023	123	2023
The class of random graphs arising from exchangeable random measures V Veitch, DM Roy arXiv preprint arXiv:1512.03099, 2015	109	2015
Using embeddings to correct for unobserved confounding in networks V Veitch, Y Wang, D Blei Advances in Neural Information Processing Systems 32, 2019	87*	2019
The holdout randomization test: Principled and easy black box feature selection W Tansey, V Veitch, H Zhang, R Rabadan, DM Blei arXiv preprint arXiv:1811.00645 1 (3), 2018	85*	2018
Sense and sensitivity analysis: Simple post-hoc analysis of bias due to unobserved confounding V Veitch, A Zaveri Advances in neural information processing systems 33, 10999-11009, 2020	57	2020
Causal effects of linguistic properties R Pryzant, D Card, D Jurafsky, V Veitch, D Sridhar arXiv preprint arXiv:2010.12919, 2020	54	2020
Concept algebra for (score-based) text-controlled generative models Z Wang, L Gui, J Negrea, V Veitch Advances in Neural Information Processing Systems 36, 35331-35349, 2023	53*	2023
Sampling and estimation for (sparse) exchangeable graphs V Veitch, DM Roy	53	2019
Invariant representation learning for treatment effect estimation C Shi, V Veitch, DM Blei Uncertainty in artificial intelligence, 1546-1555, 2021	42	2021
Invariant and transportable representations for anti-causal domain shifts Y Jiang, V Veitch Advances in Neural Information Processing Systems 35, 20782-20794, 2022	35	2022

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