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2020 – today
- 2023
[j2]David Rolnick
, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Sasha Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer T. Chayes, Yoshua Bengio:
Tackling Climate Change with Machine Learning. ACM Comput. Surv. 55(2): 42:1-42:96 (2023)- 2022
- [j1]Nora Loose, Ryan Abernathey, Ian G. Grooms, Julius Busecke, Arthur Guillaumin, Elizabeth Yankovsky, Gustavo Marques, Jacob Steinberg, Andrew Slavin Ross, Hemant Khatri, Scott Bachman, Laure Zanna, Paige Martin:
GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data. J. Open Source Softw. 7(69): 3947 (2022) - 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
2010 – 2019
- 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) - [i7]David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Körding, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer T. Chayes, Yoshua Bengio:
Tackling Climate Change with Machine Learning. CoRR abs/1906.05433 (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) - [i3]Andrew Slavin Ross:
Training Machine Learning Models by Regularizing their Explanations. CoRR abs/1810.00869 (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)
Coauthor Index
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