Alexander D'Amour
Alexander D'Amour
Research Scientist, Google Brain
Verified email at - Homepage
Cited by
Cited by
Disambiguation and co-authorship networks of the US patent inventor database (1975–2010)
GC Li, R Lai, A D’Amour, DM Doolin, Y Sun, VI Torvik, ZY Amy, L Fleming
Research Policy 43 (6), 941-955, 2014
Underspecification presents challenges for credibility in modern machine learning
A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
arXiv preprint arXiv:2011.03395, 2020
Algorithmic fairness: Choices, assumptions, and definitions
S Mitchell, E Potash, S Barocas, A D'Amour, K Lum
Annual Review of Statistics and Its Application 8, 141-163, 2021
Pointwise: Predicting points and valuing decisions in real time with nba optical tracking data
D Cervone, A D’Amour, L Bornn, K Goldsberry
Proceedings of the 8th MIT Sloan Sports Analytics Conference, Boston, MA …, 2014
A multiresolution stochastic process model for predicting basketball possession outcomes
D Cervone, A D’Amour, L Bornn, K Goldsberry
Journal of the American Statistical Association 111 (514), 585-599, 2016
Overlap in observational studies with high-dimensional covariates
A D’Amour, P Ding, A Feller, L Lei, J Sekhon
Journal of Econometrics 221 (2), 644-654, 2021
Fairness is not static: deeper understanding of long term fairness via simulation studies
A D'Amour, H Srinivasan, J Atwood, P Baljekar, D Sculley, Y Halpern
Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020
Estimating rates of carriage acquisition and clearance and competitive ability for pneumococcal serotypes in Kenya with a Markov transition model
M Lipsitch, O Abdullahi, A D’Amour, W Xie, DM Weinberger, ET Tchetgen, ...
Epidemiology (Cambridge, Mass.) 23 (4), 510, 2012
Disambiguation and co-authorship networks of the US patent inventor database
R Lai, A D’Amour, A Yu, Y Sun, V Torvik, L Fleming
Harvard Institute for Quantitative Social Science, Cambridge, MA 2138, 2011
Flexible sensitivity analysis for observational studies without observable implications
AM Franks, A D’Amour, A Feller
Journal of the American Statistical Association, 2019
Reducing reparameterization gradient variance
A Miller, N Foti, A D'Amour, RP Adams
Advances in Neural Information Processing Systems 30, 2017
On robustness and transferability of convolutional neural networks
J Djolonga, J Yung, M Tschannen, R Romijnders, L Beyer, A Kolesnikov, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
On multi-cause causal inference with unobserved confounding: Counterexamples, impossibility, and alternatives
A D'Amour
arXiv preprint arXiv:1902.10286, 2019
Evaluating prediction-time batch normalization for robustness under covariate shift
Z Nado, S Padhy, D Sculley, A D'Amour, B Lakshminarayanan, J Snoek
arXiv preprint arXiv:2006.10963, 2020
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
Meta-analytics: tools for understanding the statistical properties of sports metrics
AM Franks, A D’Amour, D Cervone, L Bornn
Journal of Quantitative Analysis in Sports 12 (4), 151-165, 2016
Move or die: How ball movement creates open shots in the NBA
A D’Amour, D Cervone, L Bornn, K Goldsberry
Sloan Sports Analytics Conference, 2015
The multiberts: Bert reproductions for robustness analysis
T Sellam, S Yadlowsky, J Wei, N Saphra, A D'Amour, T Linzen, J Bastings, ...
arXiv preprint arXiv:2106.16163, 2021
Comment: Reflections on the deconfounder
A D’Amour
Journal of the American Statistical Association 114 (528), 1597-1601, 2019
Improving major league baseball park factor estimates
RA Acharya, AJ Ahmed, AN D'Amour, H Lu, CN Morris, BD Oglevee, ...
Journal of Quantitative Analysis in Sports 4 (2), 2008
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