Sonali Parbhoo

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 45

Top Topics

Cervical Cancer

Reinforcement Learning

Information Bottleneck

Top Venues

Trans. Mach. Learn. Res.

Publications

Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez
Guarantee Regions for Local Explanations. CoRR (2024)
Abhishek Sharma, Catherine Zeng, Sanjana Narayanan, Sonali Parbhoo, Roy H. Perlis, Finale Doshi-Velez
Task-Relevant Feature Selection with Prediction Focused Mixture Models. Trans. Mach. Learn. Res. 2024 (2024)
Isaac Lage, Sonali Parbhoo, Finale Doshi-Velez
Towards Integrating Personal Knowledge into Test-Time Predictions. CoRR (2024)
Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning. ICML (2023)
Abhishek Sharma, Sonali Parbhoo, Omer Gottesman, Finale Doshi-Velez
Robust Decision-Focused Learning for Reward Transfer. CoRR (2023)
Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez
Learning-to-defer for sequential medical decision-making under uncertainty. Trans. Mach. Learn. Res. 2023 (2023)
Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning. CoRR (2023)
Kate Highnam, Zach Hanif, Ellie Van Vogt, Sonali Parbhoo, Sergio Maffeis, Nicholas R. Jennings
Adaptive Experimental Design for Intrusion Data Collection. CoRR (2023)
Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)
Célia Wafa Ayad, Thomas Bonnier, Benjamin Bosch, Jesse Read, Sonali Parbhoo
Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk. MLHC (2023)
Leo Benac, Sonali Parbhoo, Finale Doshi-Velez
Bayesian Inverse Transition Learning for Offline Settings. CoRR (2023)
Kate Highnam, Zach Hanif, Ellie Van Vogt, Sonali Parbhoo, Sergio Maffeis, Nicholas R. Jennings
Adaptive Experimental Design for Intrusion Data Collection. CAMLIS (2023)
Aaman Rebello, Shengpu Tang, Jenna Wiens, Sonali Parbhoo
Leveraging Factored Action Spaces for Off-Policy Evaluation. CoRR (2023)
Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning. CoRR (2023)
Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez
Addressing Leakage in Concept Bottleneck Models. NeurIPS (2022)
Sonali Parbhoo, Shalmali Joshi, Finale Doshi-Velez
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making. CoRR (2022)
Jiayu Yao, Sonali Parbhoo, Weiwei Pan, Finale Doshi-Velez
Policy Optimization with Sparse Global Contrastive Explanations. CoRR (2022)
Carissa Wu, Sonali Parbhoo, Marton Havasi, Finale Doshi-Velez
Learning Optimal Summaries of Clinical Time-series with Concept Bottleneck Models. MLHC (2022)
Maxim Samarin, Vitali Nesterov, Mario Wieser, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth
Learning Conditional Invariance through Cycle Consistency. CoRR (2021)
Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez
Learning Predictive and Interpretable Timeseries Summaries from ICU Data. AMIA (2021)
Maxim Samarin, Vitali Nesterov, Mario Wieser, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth
Learning Conditional Invariance Through Cycle Consistency. GCPR (2021)
Abhishek Sharma, Catherine Zeng, Sanjana Narayanan, Sonali Parbhoo, Finale Doshi-Velez
On Learning Prediction-Focused Mixtures. CoRR (2021)
Melanie F. Pradier, Javier Zazo, Sonali Parbhoo, Roy H. Perlis, Maurizio Zazzi, Finale Doshi-Velez
Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible. CoRR (2021)
Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez
Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty. CoRR (2021)
Sonali Parbhoo, Stefan Bauer, Patrick Schwab
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments. CoRR (2021)
Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez
Learning Predictive and Interpretable Timeseries Summaries from ICU Data. CoRR (2021)
Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. J. Artif. Intell. Res. 72 (2021)
Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records. CoRR (2020)
Sonali Parbhoo, Mario Wieser, Aleksander Wieczorek, Volker Roth
Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates. Entropy 22 (4) (2020)
Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
Regional Tree Regularization for Interpretability in Deep Neural Networks. AAAI (2020)
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. CoRR (2020)
Sonali Parbhoo, Mario Wieser, Volker Roth, Finale Doshi-Velez
Transfer Learning from Well-Curated to Less-Resourced Populations with HIV. MLHC (2020)
Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth
Inverse Learning of Symmetries. NeurIPS (2020)
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. ICML (2020)
Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth
Inverse Learning of Symmetry Transformations. CoRR (2020)
Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. CoRR (2019)
Adam Kortylewski, Aleksander Wieczorek, Mario Wieser, Clemens Blumer, Sonali Parbhoo, Andreas Morel-Forster, Volker Roth, Thomas Vetter
Greedy Structure Learning of Hierarchical Compositional Models. CVPR (2019)
Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
Regional Tree Regularization for Interpretability in Black Box Models. CoRR (2019)
Adam Kortylewski, Mario Wieser, Andreas Morel-Forster, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth, Thomas Vetter
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis. CoRR (2018)
Sonali Parbhoo, Mario Wieser, Volker Roth
Causal Deep Information Bottleneck. CoRR (2018)
Sonali Parbhoo, Mario Wieser, Volker Roth
Estimating Causal Effects With Partial Covariates For Clinical Interpretability. CoRR (2018)
Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. AAAI (2018)
Sonali Parbhoo, Jasmina Bogojeska, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
Combining Kernel and Model Based Learning for HIV Therapy Selection. CRI (2017)
Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. CoRR (2017)
Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth
Bayesian Markov Blanket Estimation. AISTATS (2016)
Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth
Bayesian Markov Blanket Estimation. CoRR (2015)