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 | 713 | 2022 |
Contextuality supplies the ‘magic’for quantum computation M Howard, J Wallman, V Veitch, J Emerson Nature 510 (7505), 351-355, 2014 | 682 | 2014 |
The resource theory of stabilizer quantum computation V Veitch, SAH Mousavian, D Gottesman, J Emerson New Journal of Physics 16 (1), 013009, 2014 | 414 | 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 | 389 | 2012 |
Adapting neural networks for the estimation of treatment effects C Shi, D Blei, V Veitch Advances in neural information processing systems 32, 2019 | 364 | 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 | 222 | 2018 |
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 | 198 | 2022 |
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 | 149 | 2021 |
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 | 130 | 2013 |
Adapting text embeddings for causal inference V Veitch, D Sridhar, D Blei Conference on Uncertainty in Artificial Intelligence, 919-928, 2020 | 126 | 2020 |
The class of random graphs arising from exchangeable random measures V Veitch, DM Roy arXiv preprint arXiv:1512.03099, 2015 | 106 | 2015 |
Using embeddings to correct for unobserved confounding in networks V Veitch, Y Wang, D Blei Advances in Neural Information Processing Systems 32, 2019 | 78* | 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 | 73* | 2018 |
Sampling and estimation for (sparse) exchangeable graphs V Veitch, DM Roy | 47 | 2019 |
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 | 45 | 2020 |
Causal effects of linguistic properties R Pryzant, D Card, D Jurafsky, V Veitch, D Sridhar arXiv preprint arXiv:2010.12919, 2020 | 41 | 2020 |
Invariant representation learning for treatment effect estimation C Shi, V Veitch, DM Blei Uncertainty in artificial intelligence, 1546-1555, 2021 | 33 | 2021 |
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 | 30* | 2024 |
The linear representation hypothesis and the geometry of large language models K Park, YJ Choe, V Veitch arXiv preprint arXiv:2311.03658, 2023 | 30 | 2023 |
Sampling perspectives on sparse exchangeable graphs C Borgs, JT Chayes, H Cohn, V Veitch | 30 | 2019 |