Andrew Slavin Ross

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 21

Top Topics

Greenhouse Gases

Machine Learning

Spatial Filtering

Top Venues

Publications

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) (2023)
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) (2022)
Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez
Learning Predictive and Interpretable Timeseries Summaries from ICU Data. AMIA (2021)
Andrew Slavin Ross, Finale Doshi-Velez
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. ICML (2021)
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)
Andrew Slavin Ross, Finale Doshi-Velez
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. CoRR (2021)
Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez
Evaluating the Interpretability of Generative Models by Interactive Reconstruction. CoRR (2021)
Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez
Learning Predictive and Interpretable Timeseries Summaries from ICU Data. CoRR (2021)
Andrew Slavin Ross, Weiwei Pan, Leo A. Celi, Finale Doshi-Velez
Ensembles of Locally Independent Prediction Models. AAAI (2020)
Andrew Slavin Ross, Weiwei Pan, Leo Anthony Celi, Finale Doshi-Velez
Ensembles of Locally Independent Prediction Models. CoRR (2019)
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 (2019)
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 (2019)
Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez
Human-in-the-Loop Interpretability Prior. CoRR (2018)
Andrew Slavin Ross
Training Machine Learning Models by Regularizing their Explanations. CoRR (2018)
Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez
Human-in-the-Loop Interpretability Prior. NeurIPS (2018)
Andrew Slavin Ross, Weiwei Pan, Finale Doshi-Velez
Learning Qualitatively Diverse and Interpretable Rules for Classification. CoRR (2018)
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)
Andrew Slavin Ross, Finale Doshi-Velez
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. AAAI (2018)
Andrew Slavin Ross, Finale Doshi-Velez
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients. CoRR (2017)
Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. CoRR (2017)
Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. IJCAI (2017)