Victor Veitch
Victor Veitch
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Cited by
Cited by
Contextuality supplies the ‘magic’for quantum computation
M Howard, J Wallman, V Veitch, J Emerson
Nature 510 (7505), 351-355, 2014
Underspecification presents challenges for credibility in modern machine learning
A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
The Journal of Machine Learning Research 23 (1), 10237-10297, 2022
The resource theory of stabilizer quantum computation
V Veitch, SAH Mousavian, D Gottesman, J Emerson
New Journal of Physics 16 (1), 013009, 2014
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
Adapting neural networks for the estimation of treatment effects
C Shi, D Blei, V Veitch
Advances in neural information processing systems 32, 2019
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
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
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
The class of random graphs arising from exchangeable random measures
V Veitch, DM Roy
arXiv preprint arXiv:1512.03099, 2015
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
Adapting text embeddings for causal inference
V Veitch, D Sridhar, D Blei
Conference on Uncertainty in Artificial Intelligence, 919-928, 2020
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
Using embeddings to correct for unobserved confounding in networks
V Veitch, Y Wang, D Blei
Advances in Neural Information Processing Systems 32, 2019
Sampling and estimation for (sparse) exchangeable graphs
V Veitch, DM Roy
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
Causal effects of linguistic properties
R Pryzant, D Card, D Jurafsky, V Veitch, D Sridhar
arXiv preprint arXiv:2010.12919, 2020
Sampling perspectives on sparse exchangeable graphs
C Borgs, JT Chayes, H Cohn, V Veitch
The whole is greater than the sum of the parts: on the possibility of purely statistical interpretations of quantum theory
J Emerson, D Serbin, C Sutherland, V Veitch
arXiv preprint arXiv:1312.1345, 2013
Invariant representation learning for treatment effect estimation
C Shi, V Veitch, DM Blei
Uncertainty in Artificial Intelligence, 1546-1555, 2021
Valid causal inference with (some) invalid instruments
JS Hartford, V Veitch, D Sridhar, K Leyton-Brown
International Conference on Machine Learning, 4096-4106, 2021
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