Sonali Parbhoo

Orcid: 0000-0001-8400-3732

According to our database1, Sonali Parbhoo authored at least 38 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2024
Task-Relevant Feature Selection with Prediction Focused Mixture Models.
Trans. Mach. Learn. Res., 2024

Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning.
J. Mach. Learn. Res., 2024

Towards Integrating Personal Knowledge into Test-Time Predictions.
CoRR, 2024

Guarantee Regions for Local Explanations.
CoRR, 2024

2023
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning.
Trans. Mach. Learn. Res., 2023

Learning-to-defer for sequential medical decision-making under uncertainty.
Trans. Mach. Learn. Res., 2023

Bayesian Inverse Transition Learning for Offline Settings.
CoRR, 2023

Leveraging Factored Action Spaces for Off-Policy Evaluation.
CoRR, 2023

Robust Decision-Focused Learning for Reward Transfer.
CoRR, 2023

Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk.
Proceedings of the Machine Learning for Healthcare Conference, 2023

The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning.
Proceedings of the International Conference on Machine Learning, 2023

Adaptive Experimental Design for Intrusion Data Collection.
Proceedings of the Conference on Applied Machine Learning in Information Security, 2023

2022
Policy Optimization with Sparse Global Contrastive Explanations.
CoRR, 2022

Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making.
CoRR, 2022

Addressing Leakage in Concept Bottleneck Models.
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.
Proceedings of the Machine Learning for Healthcare Conference, 2022

2021
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization.
J. Artif. Intell. Res., 2021

On Learning Prediction-Focused Mixtures.
CoRR, 2021

Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty.
CoRR, 2021

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments.
CoRR, 2021

Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible.
CoRR, 2021

Learning Conditional Invariance Through Cycle Consistency.
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.
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.
Entropy, 2020

Real-time Prediction of COVID-19 related Mortality using Electronic Health Records.
CoRR, 2020

Inverse Learning of Symmetry Transformations.
CoRR, 2020

Inverse Learning of Symmetries.
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.
Proceedings of the Machine Learning for Healthcare Conference, 2020

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions.
Proceedings of the 37th International Conference on Machine Learning, 2020

Regional Tree Regularization for Interpretability in Deep Neural Networks.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Regional Tree Regularization for Interpretability in Black Box Models.
CoRR, 2019

Greedy Structure Learning of Hierarchical Compositional Models.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Estimating Causal Effects With Partial Covariates For Clinical Interpretability.
CoRR, 2018

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis.
CoRR, 2018

Causal Deep Information Bottleneck.
CoRR, 2018

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Combining Kernel and Model Based Learning for HIV Therapy Selection.
Proceedings of the Summit on Clinical Research Informatics, 2017

2016
Bayesian Markov Blanket Estimation.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016


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