Andrew Slavin Ross

21 papers A* 6Misc 2Journal 13

Year	Rank	Type	Title / Venue / Authors
2023	J	`jnl`	Tackling Climate Change with Machine Learning. ACM Comput. Surv. 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
2022	J	`jnl`	GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data. J. Open Source Softw. 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
2021	A*	`conf`	Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. ICML Andrew Slavin Ross, Finale Doshi-Velez
2021	J	`jnl`	Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. CoRR Andrew Slavin Ross, Finale Doshi-Velez
2021	A*	`conf`	Evaluating the Interpretability of Generative Models by Interactive Reconstruction. CHI Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez
2021	J	`jnl`	Evaluating the Interpretability of Generative Models by Interactive Reconstruction. CoRR Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez
2021	Misc	`conf`	Learning Predictive and Interpretable Timeseries Summaries from ICU Data. AMIA Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez
2021	J	`jnl`	Learning Predictive and Interpretable Timeseries Summaries from ICU Data. CoRR Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez
2020	A*	`conf`	Ensembles of Locally Independent Prediction Models. AAAI Andrew Slavin Ross, Weiwei Pan, Leo A. Celi, Finale Doshi-Velez
2019	J	`jnl`	Ensembles of Locally Independent Prediction Models. CoRR Andrew Slavin Ross, Weiwei Pan, Leo Anthony Celi, Finale Doshi-Velez
2019	J	`jnl`	Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. CoRR Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez
2019	J	`jnl`	Tackling Climate Change with Machine Learning. CoRR 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
2018	A*	`conf`	Human-in-the-Loop Interpretability Prior. NeurIPS Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez
2018	J	`jnl`	Human-in-the-Loop Interpretability Prior. CoRR Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez
2018	Misc	`conf`	Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. AMIA Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez
2018	A*	`conf`	Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. AAAI Andrew Slavin Ross, Finale Doshi-Velez
2018	J	`jnl`	Learning Qualitatively Diverse and Interpretable Rules for Classification. CoRR Andrew Slavin Ross, Weiwei Pan, Finale Doshi-Velez
2018	J	`jnl`	Training Machine Learning Models by Regularizing their Explanations. CoRR Andrew Slavin Ross
2017	J	`jnl`	Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients. CoRR Andrew Slavin Ross, Finale Doshi-Velez
2017	A*	`conf`	Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. IJCAI Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
2017	J	`jnl`	Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. CoRR Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez