Sonali Parbhoo

According to our database, Sonali Parbhoo authored at least 38 papers between 2016 and 2024.

Collaborative distances:

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

2024

Task-Relevant Feature Selection with Prediction Focused Mixture Models.

Abhishek Sharma, Catherine Zeng, Sanjana Narayanan, Sonali Parbhoo, Roy H. Perlis, Finale Doshi-Velez

Trans. Mach. Learn. Res., 2024

Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning.

Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez

J. Mach. Learn. Res., 2024

Towards Integrating Personal Knowledge into Test-Time Predictions.

Isaac Lage, Sonali Parbhoo, Finale Doshi-Velez

CoRR, 2024

Guarantee Regions for Local Explanations.

Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez

CoRR, 2024

2023

Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning.

Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi

Trans. Mach. Learn. Res., 2023

Learning-to-defer for sequential medical decision-making under uncertainty.

Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez

Trans. Mach. Learn. Res., 2023

Bayesian Inverse Transition Learning for Offline Settings.

Leo Benac, Sonali Parbhoo, Finale Doshi-Velez

CoRR, 2023

Leveraging Factored Action Spaces for Off-Policy Evaluation.

Aaman Rebello, Shengpu Tang, Jenna Wiens, Sonali Parbhoo

CoRR, 2023

Robust Decision-Focused Learning for Reward Transfer.

Abhishek Sharma, Sonali Parbhoo, Omer Gottesman, Finale Doshi-Velez

CoRR, 2023

Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk.

Célia Wafa Ayad, Thomas Bonnier, Benjamin Bosch, Jesse Read, Sonali Parbhoo

Proceedings of the Machine Learning for Healthcare Conference, 2023

The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning.

Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez

Proceedings of the International Conference on Machine Learning, 2023

Adaptive Experimental Design for Intrusion Data Collection.

Kate Highnam, Zach Hanif, Ellie Van Vogt, Sonali Parbhoo, Sergio Maffeis, Nicholas R. Jennings

Proceedings of the Conference on Applied Machine Learning in Information Security, 2023

2022

Policy Optimization with Sparse Global Contrastive Explanations.

Jiayu Yao, Sonali Parbhoo, Weiwei Pan, Finale Doshi-Velez

CoRR, 2022

Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making.

Sonali Parbhoo, Shalmali Joshi, Finale Doshi-Velez

CoRR, 2022

Addressing Leakage in Concept Bottleneck Models.

Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Optimal Summaries of Clinical Time-series with Concept Bottleneck Models.

Carissa Wu, Sonali Parbhoo, Marton Havasi, Finale Doshi-Velez

Proceedings of the Machine Learning for Healthcare Conference, 2022

2021

Optimizing for Interpretability in Deep Neural Networks with Tree Regularization.

Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez

J. Artif. Intell. Res., 2021

On Learning Prediction-Focused Mixtures.

Abhishek Sharma, Catherine Zeng, Sanjana Narayanan, Sonali Parbhoo, Finale Doshi-Velez

CoRR, 2021

Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty.

Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez

CoRR, 2021

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments.

Sonali Parbhoo, Stefan Bauer, Patrick Schwab

CoRR, 2021

Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible.

Melanie F. Pradier, Javier Zazo, Sonali Parbhoo, Roy H. Perlis, Maurizio Zazzi, Finale Doshi-Velez

CoRR, 2021

Learning Conditional Invariance Through Cycle Consistency.

Maxim Samarin, Vitali Nesterov, Mario Wieser, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth

Proceedings of the Pattern Recognition - 43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28, 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

Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates.

Sonali Parbhoo, Mario Wieser, Aleksander Wieczorek, Volker Roth

Entropy, 2020

Real-time Prediction of COVID-19 related Mortality using Electronic Health Records.

Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer

CoRR, 2020

Inverse Learning of Symmetry Transformations.

Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth

CoRR, 2020

Inverse Learning of Symmetries.

Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Transfer Learning from Well-Curated to Less-Resourced Populations with HIV.

Sonali Parbhoo, Mario Wieser, Volker Roth, Finale Doshi-Velez

Proceedings of the Machine Learning for Healthcare Conference, 2020

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions.

Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez

Proceedings of the 37th International Conference on Machine Learning, 2020

Regional Tree Regularization for Interpretability in Deep Neural Networks.

Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

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

2019

Regional Tree Regularization for Interpretability in Black Box Models.

Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

CoRR, 2019

Greedy Structure Learning of Hierarchical Compositional Models.

Adam Kortylewski, Aleksander Wieczorek, Mario Wieser, Clemens Blumer, Sonali Parbhoo, Andreas Morel-Forster, Volker Roth, Thomas Vetter

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Estimating Causal Effects With Partial Covariates For Clinical Interpretability.

Sonali Parbhoo, Mario Wieser, Volker Roth

CoRR, 2018

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis.

Adam Kortylewski, Mario Wieser, Andreas Morel-Forster, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth, Thomas Vetter

CoRR, 2018

Causal Deep Information Bottleneck.

Sonali Parbhoo, Mario Wieser, Volker Roth

CoRR, 2018

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability.

Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

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

2017

Combining Kernel and Model Based Learning for HIV Therapy Selection.

Sonali Parbhoo, Jasmina Bogojeska, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

Proceedings of the Summit on Clinical Research Informatics, 2017

2016

Bayesian Markov Blanket Estimation.

Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016