Victor Veitch

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Joseph EmersonProfessor of Applied Math, University of WaterlooDirección de correo verificada de uwaterloo.ca
David BleiProfessor of Statistics and Computer Science, Columbia UniversityDirección de correo verificada de columbia.edu
Dhanya SridharUniversity of Montreal, Mila-Quebec AI InstituteDirección de correo verificada de mila.quebec
Christopher FerrieUniversity of Technology SydneyDirección de correo verificada de uts.edu.au
Jacob EisensteinGoogle ResearchDirección de correo verificada de google.com
David GrossUniversity of CologneDirección de correo verificada de thp.uni-koeln.de
Reid PryzantMicrosoftDirección de correo verificada de stanford.edu
Zihao WangUniversity of ChicagoDirección de correo verificada de uchicago.edu
Claudia ShiPhD Student, Columbia UniversityDirección de correo verificada de columbia.edu
Amir FederColumbia University, GoogleDirección de correo verificada de columbia.edu
Emaad ManzoorAssistant Professor of Marketing, Cornell UniversityDirección de correo verificada de cornell.edu
Daniel M. RoyResearch Director, Vector Institute; Prof., U. Toronto (Statistics, CS)Dirección de correo verificada de utoronto.ca
Peter OrbanzColumbia UniversityDirección de correo verificada de stat.columbia.edu
Wenda ZhouNew York UniversityDirección de correo verificada de nyu.edu
Morgane AusternHarvard UniversityDirección de correo verificada de fas.harvard.edu
Joel J. WallmanOrca ComputingDirección de correo verificada de orcacomputing.com
Mark HowardLecturer, University of GalwayDirección de correo verificada de universityofgalway.ie
Alexander D'AmourResearch Scientist, Google DeepMindDirección de correo verificada de google.com
Lin GuiDepartment of Statistics, The University of ChicagoDirección de correo verificada de uchicago.edu
Roi ReichartTechnion - Israel Institute of TechnologyDirección de correo verificada de ie.technion.ac.il

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Victor Veitch

University of Chicago, Google

Dirección de correo verificada de uchicago.edu - Página principal

Artificial Intelligence Machine Learning Causality Statistics


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	818	2022
Contextuality supplies the ‘magic’for quantum computation M Howard, J Wallman, V Veitch, J Emerson Nature 510 (7505), 351-355, 2014	729	2014
The resource theory of stabilizer quantum computation V Veitch, SAH Mousavian, D Gottesman, J Emerson New Journal of Physics 16 (1), 013009, 2014	484	2014
Adapting neural networks for the estimation of treatment effects C Shi, D Blei, V Veitch Advances in neural information processing systems 32, 2019	446	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	419	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	266	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 in text classification V Veitch, A D'Amour, S Yadlowsky, J Eisenstein Advances in neural information processing systems 34, 16196-16208, 2021	168	2021
Adapting text embeddings for causal inference V Veitch, D Sridhar, D Blei Conference on Uncertainty in Artificial Intelligence, 919-928, 2020	146	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	141	2013
The class of random graphs arising from exchangeable random measures V Veitch, DM Roy arXiv preprint arXiv:1512.03099, 2015	111	2015
The linear representation hypothesis and the geometry of large language models K Park, YJ Choe, V Veitch arXiv preprint arXiv:2311.03658, 2023	95	2023
Using embeddings to correct for unobserved confounding in networks V Veitch, Y Wang, D Blei Advances in Neural Information Processing Systems 32, 2019	89*	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	84*	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	58	2020
Causal effects of linguistic properties R Pryzant, D Card, D Jurafsky, V Veitch, D Sridhar arXiv preprint arXiv:2010.12919, 2020	54	2020
Sampling and estimation for (sparse) exchangeable graphs V Veitch, DM Roy	52	2019
Concept algebra for (score-based) text-controlled generative models Z Wang, L Gui, J Negrea, V Veitch Advances in Neural Information Processing Systems 36, 2024	47*	2024
Invariant representation learning for treatment effect estimation C Shi, V Veitch, DM Blei Uncertainty in artificial intelligence, 1546-1555, 2021	40	2021
Sampling perspectives on sparse exchangeable graphs C Borgs, JT Chayes, H Cohn, V Veitch	36	2019

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