Andrew Slavin Ross

According to our database, Andrew Slavin Ross authored at least 12 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Tackling Climate Change with Machine Learning.

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

ACM Comput. Surv., 2023

2022

GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data.

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

J. Open Source Softw., 2022

2021

Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement.

Andrew Slavin Ross, Finale Doshi-Velez

Proceedings of the 38th International Conference on Machine Learning, 2021

Evaluating the Interpretability of Generative Models by Interactive Reconstruction.

Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez

Proceedings of the CHI '21: CHI Conference on Human Factors in Computing Systems, 2021

Learning Predictive and Interpretable Timeseries Summaries from ICU Data.

Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez

Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

2020

Ensembles of Locally Independent Prediction Models.

Andrew Slavin Ross, Weiwei Pan, Leo A. Celi, Finale Doshi-Velez

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2018

Training Machine Learning Models by Regularizing their Explanations.

Andrew Slavin Ross

CoRR, 2018

Learning Qualitatively Diverse and Interpretable Rules for Classification.

Andrew Slavin Ross, Weiwei Pan, Finale Doshi-Velez

CoRR, 2018

Human-in-the-Loop Interpretability Prior.

Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning.

Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez

Proceedings of the AMIA 2018, 2018

Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients.

Andrew Slavin Ross, Finale Doshi-Velez

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations.

Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017