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
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
Tackling Climate Change with Machine Learning
D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ...
arXiv preprint arXiv:1906.05433 [cs, stat], 2019
Human-in-the-loop interpretability prior
I Lage, A Ross, SJ Gershman, B Kim, F Doshi-Velez
Advances in neural information processing systems, 10159-10168, 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
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
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
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
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
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
Generating interpretable predictions about antidepressant treatment stability using supervised topic models
MC Hughes, MF Pradier, AS Ross, TH McCoy, RH Perlis, F Doshi-Velez
medRxiv, 2020
Ensembles of Locally Independent Prediction Models.
AS Ross, W Pan, LA Celi, F Doshi-Velez
AAAI, 5527-5536, 2020
Controlled Direct Effect Priors for Bayesian Neural Networks
J Du, AS Ross, Y Shavit, F Doshi-Velez
NeurIPS 2019 Workshop on Bayesian Deep Learning, 2019
Refactoring Machine Learning
AS Ross, JZ Forde
NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018
Training Machine Learning Models by Regularizing their Explanations
AS Ross
arXiv preprint arXiv:1810.00869, 2018
CS265 Final Project: LSM-tree gradient descent
M Akmanalp, AS Hilgard, A Ross
The Compression and Concentration of Classical and Quantum Information
A Ross, P Love
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