Andrew Ross
Andrew Ross
PhD student, Harvard University
Verified email at - Homepage
Cited by
Cited by
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
AS Ross, F Doshi-Velez
Thirty-Second AAAI Conference on Artificial Intelligence, 1660-1669, 2017
Tackling Climate Change with Machine Learning
D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ...
ACM Computing Surveys 55 (2), 1-96, 2019
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
AS Ross, MC Hughes, Doshi-Velez, Finale
Proceedings of the Twenty-Sixth International Joint Conference on Artificial …, 2017
Human-in-the-loop interpretability prior
I Lage, A Ross, SJ Gershman, B Kim, F Doshi-Velez
Advances in neural information processing systems 31, 2018
Improving sepsis treatment strategies by combining deep and kernel-based reinforcement learning
X Peng, Y Ding, D Wihl, O Gottesman, M Komorowski, LH Lehman, ...
AMIA Annual Symposium Proceedings 2018, 887, 2018
Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn.
S Idreos, N Dayan, W Qin, M Akmanalp, S Hilgard, A Ross, J Lennon, ...
CIDR, 2019
Hydrodynamic irreversibility in particle suspensions with nonuniform strain
JS Guasto, AS Ross, JP Gollub
Physical Review E 81 (6), 061401, 2010
The neural lasso: Local linear sparsity for interpretable explanations
A Ross, I Lage, F Doshi-Velez
Workshop on Transparent and Interpretable Machine Learning in Safety …, 2017
Ensembles of locally independent prediction models
A Ross, W Pan, L Celi, F Doshi-Velez
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5527-5536, 2020
Evaluating the interpretability of generative models by interactive reconstruction
A Ross, N Chen, EZ Hang, EL Glassman, F Doshi-Velez
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021
Learning qualitatively diverse and interpretable rules for classification
AS Ross, W Pan, F Doshi-Velez
arXiv preprint arXiv:1806.08716, 2018
Improving counterfactual reasoning with kernelised dynamic mixing models
S Parbhoo, O Gottesman, AS Ross, M Komorowski, A Faisal, I Bon, ...
PloS one 13 (11), e0205839, 2018
Assessment of a prediction model for antidepressant treatment stability using supervised topic models
MC Hughes, MF Pradier, AS Ross, TH McCoy, RH Perlis, F Doshi-Velez
JAMA network open 3 (5), e205308-e205308, 2020
Learning key-value store design
S Idreos, N Dayan, W Qin, M Akmanalp, S Hilgard, A Ross, J Lennon, ...
arXiv preprint arXiv:1907.05443, 2019
Benchmarks, algorithms, and metrics for hierarchical disentanglement
A Ross, F Doshi-Velez
International Conference on Machine Learning, 9084-9094, 2021
Refactoring Machine Learning
AS Ross, JZ Forde
NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018
GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data
N Loose, R Abernathey, I Grooms, J Busecke, A Guillaumin, E Yankovsky, ...
Journal of Open Source Software 7 (70), 3947, 2022
Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
AS Ross, Z Li, P Perezhogin, C Fernandez-Granda, L Zanna
Learning Predictive and Interpretable Timeseries Summaries from ICU Data
N Johnson, S Parbhoo, AS Ross, F Doshi-Velez
AMIA Annual Symposium Proceedings 2021, 581, 2021
Right for the Right Reasons: Training Neural Networks to Be Interpretable, Robust, and Consistent with Expert Knowledge
AS Ross
Harvard University, 2021
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