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ORCIDAndrew Slavin Ross
- > Home > Persons > Andrew Slavin Ross
Publications
- 2021
[c8]Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez:
Learning Predictive and Interpretable Timeseries Summaries from ICU Data. AMIA 2021- [c7]Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez:
Evaluating the Interpretability of Generative Models by Interactive Reconstruction. CHI 2021: 80:1-80:15 - [c6]Andrew Slavin Ross, Finale Doshi-Velez:
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. ICML 2021: 9084-9094 - [i11]Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez:
Evaluating the Interpretability of Generative Models by Interactive Reconstruction. CoRR abs/2102.01264 (2021) - [i10]Andrew Slavin Ross, Finale Doshi-Velez:
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. CoRR abs/2102.05185 (2021) - [i9]Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez:
Learning Predictive and Interpretable Timeseries Summaries from ICU Data. CoRR abs/2109.11043 (2021) - 2020
- [c5]Andrew Slavin Ross, Weiwei Pan, Leo A. Celi, Finale Doshi-Velez:
Ensembles of Locally Independent Prediction Models. AAAI 2020: 5527-5536 - 2019
- [i8]Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez:
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. CoRR abs/1901.04670 (2019) - [i6]Andrew Slavin Ross, Weiwei Pan, Leo Anthony Celi, Finale Doshi-Velez:
Ensembles of Locally Independent Prediction Models. CoRR abs/1911.01291 (2019) - 2018
- [c4]Andrew Slavin Ross, Finale Doshi-Velez:
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. AAAI 2018: 1660-1669 - [c3]Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez:
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. AMIA 2018 - [c2]Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez:
Human-in-the-Loop Interpretability Prior. NeurIPS 2018: 10180-10189 - [i5]Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez:
Human-in-the-Loop Interpretability Prior. CoRR abs/1805.11571 (2018) - [i4]Andrew Slavin Ross, Weiwei Pan, Finale Doshi-Velez:
Learning Qualitatively Diverse and Interpretable Rules for Classification. CoRR abs/1806.08716 (2018) - 2017
- [c1]Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez:
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. IJCAI 2017: 2662-2670 - [i2]Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez:
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. CoRR abs/1703.03717 (2017) - [i1]Andrew Slavin Ross, Finale Doshi-Velez:
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients. CoRR abs/1711.09404 (2017)
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