Search Results for author: Sonali Parbhoo

Found 19 papers, 3 papers with code

Policy Optimization with Sparse Global Contrastive Explanations

no code implementations13 Jul 2022 Jiayu Yao, Sonali Parbhoo, Weiwei Pan, Finale Doshi-Velez

We develop a Reinforcement Learning (RL) framework for improving an existing behavior policy via sparse, user-interpretable changes.

reinforcement-learning

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

no code implementations20 Jan 2022 Sonali Parbhoo, Shalmali Joshi, Finale Doshi-Velez

A precise description of the causal estimand highlights which OPE estimands are identifiable from observational data under the stated generative assumptions.

Association Decision Making +1

On Learning Prediction-Focused Mixtures

no code implementations25 Oct 2021 Abhishek Sharma, Catherine Zeng, Sanjana Narayanan, Sonali Parbhoo, Finale Doshi-Velez

Probabilistic models help us encode latent structures that both model the data and are ideally also useful for specific downstream tasks.

Time Series

Learning Predictive and Interpretable Timeseries Summaries from ICU Data

no code implementations22 Sep 2021 Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez

Machine learning models that utilize patient data across time (rather than just the most recent measurements) have increased performance for many risk stratification tasks in the intensive care unit.

Time Series

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

no code implementations13 Sep 2021 Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez

We propose SLTD (`Sequential Learning-to-Defer') a framework for learning-to-defer pre-emptively to an expert in sequential decision-making settings.

Decision Making Decision Making Under Uncertainty

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments

no code implementations20 Mar 2021 Sonali Parbhoo, Stefan Bauer, Patrick Schwab

Estimating an individual's potential response to interventions from observational data is of high practical relevance for many domains, such as healthcare, public policy or economics.

Counterfactual Inference Representation Learning

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

no code implementations13 Jan 2021 Melanie F. Pradier, Javier Zazo, Sonali Parbhoo, Roy H. Perlis, Maurizio Zazzi, Finale Doshi-Velez

We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance.

Decision Making Management

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

no code implementations31 Aug 2020 Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients.

Specificity

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

no code implementations ICML 2020 Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez

Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deployment in high stakes settings requires ways of assessing its validity.

Off-policy evaluation

Inverse Learning of Symmetries

1 code implementation NeurIPS 2020 Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth

Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser.

Optimizing for Interpretability in Deep Neural Networks with Tree Regularization

no code implementations14 Aug 2019 Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez

Moreover, for situations in which a single, global tree is a poor estimator, we introduce a regional tree regularizer that encourages the deep model to resemble a compact, axis-aligned decision tree in predefined, human-interpretable contexts.

Estimating Causal Effects With Partial Covariates For Clinical Interpretability

no code implementations26 Nov 2018 Sonali Parbhoo, Mario Wieser, Volker Roth

Estimating the causal effects of an intervention in the presence of confounding is a frequently occurring problem in applications such as medicine.

Causal Inference

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

no code implementations19 Nov 2018 Adam Kortylewski, Mario Wieser, Andreas Morel-Forster, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth, Thomas Vetter

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects.

Bayesian Inference

Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates

no code implementations6 Jul 2018 Sonali Parbhoo, Mario Wieser, Aleksander Wieczorek, Volker Roth

Estimating the causal effects of an intervention from high-dimensional observational data is difficult due to the presence of confounding.

Causal Inference

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

2 code implementations16 Nov 2017 Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

The lack of interpretability remains a key barrier to the adoption of deep models in many applications.

Time Series

Greedy Structure Learning of Hierarchical Compositional Models

no code implementations CVPR 2019 Adam Kortylewski, Aleksander Wieczorek, Mario Wieser, Clemens Blumer, Sonali Parbhoo, Andreas Morel-Forster, Volker Roth, Thomas Vetter

In this work, we consider the problem of learning a hierarchical generative model of an object from a set of images which show examples of the object in the presence of variable background clutter.

Transfer Learning

Bayesian Markov Blanket Estimation

no code implementations6 Oct 2015 Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth

This paper considers a Bayesian view for estimating a sub-network in a Markov random field.

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