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- Zoubin GhahramaniSuggested from co-authorship
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Works234 from public data
- 3,100
- Do no harm: a roadmap for responsible machine learning for health care1,228
In this Perspective, the authors present a framework, context and guidelines for accelerating the translation of machine-learning-based interventions in health care.
- Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients774
It is demonstrated that regularizing input gradients makes them more naturally interpretable as rationales for model predictions, and also exhibits robustness to transferred adversarial examples generated to fool all of the other models.
- Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations717
This work introduces a method for efficiently explaining and regularizing differentiable models by examining and selectively penalizing their input gradients, which provide a normal to the decision boundary.
- Accountability of AI Under the Law: The Role of Explanation510
Contrary to popular wisdom of AI systems as indecipherable black boxes, it is found that this level of explanation should often be technically feasible but may sometimes be practically onerous; in the future, AI systems can and should be held to a similar standard of explanation as humans currently are.
- Guidelines for reinforcement learning in healthcare493
New guidelines for reinforcement learning for decisions about patient treatment are provided that are hoped will accelerate the rate at which observational cohorts can inform healthcare practice in a safe, risk-conscious manner.
- 454
- Comorbidity Clusters in Autism Spectrum Disorders: An Electronic Health Record Time-Series Analysis417
Three distinct patterns of medical trajectories were identified by unsupervised clustering of electronic health record diagnoses that may point to distinct etiologies with different genetic and environmental contributions in ASDs.
- Ethical and regulatory challenges of large language models in medicine331
Ethical concerns stemming from the perspectives of users, developers, and regulators are highlighted, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs.
- Beyond Sparsity: Tree Regularization of Deep Models for Interpretability302
This work explicitly regularizes deep models so human users might step through the process behind their predictions in little time, and trains deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes.
- How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection295
It is found that interacting with ML recommendations did not significantly improve clinicians’ treatment selection accuracy, assessed as concordance with expert psychopharmacologist consensus, compared to baseline scenarios in which clinicians made treatment decisions independently.
- Unfolding physiological state: mortality modelling in intensive care units233
This work examined the use of latent variable models to decompose free-text hospital notes into meaningful features, and found that latent topic-derived features were effective in determining patient mortality under three timelines: in-hospital, 30 day post- Discharge, and 1 year post-discharge mortality.
- Unfolding physiological state233
This work examined the use of latent variable models to decompose free-text hospital notes into meaningful features, and found that latent topic-derived features were effective in determining patient mortality under three timelines: in-hospital, 30 day post- Discharge, and 1 year post-discharge mortality.
- Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens202
It is demonstrated how current trends in explainable AI may be inappropriate for clinical environments and paths towards designing these tools for real-world medical systems are considered.
- Considerations for Evaluation and Generalization in Interpretable Machine Learning192
A definitions of interpretability are discussed and described and when interpretability is needed (and when it is not) and a taxonomy for rigorous evaluation is described, and recommendations for researchers are made.
- A Bayesian nonparametric approach to modeling motion patterns164
This work proposes modeling target motion patterns as a mixture of Gaussian processes with a Dirichlet process prior over mixture weights, which provides an adaptive representation for each individual motion pattern and automatically adjusts the complexity of the motion model based on the available data.
- Human Evaluation of Models Built for Interpretability161
This work conducts carefully controlled human-subject experiments in two domains across three tasks based on human-simulatability through which it identifies specific types of complexity that affect performance more heavily than others–trends that are consistent across tasks and domains.
- A Roadmap for a Rigorous Science of Interpretability.160
There is little consensus on what interpretability in machine learning is and how to evaluate it for benchmarking, and the objective of this review is to chart a path toward the definition and rigorous evaluation of interpretability.
- Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report142
The report concludes that AI has made a major leap from the lab to people's lives in recent years, which increases the urgency to understand its potential negative effects, and asks a set of 14 questions probing critical areas of AI development.
- Model Selection in Bayesian Neural Networks via Horseshoe Priors134
This work applies a horseshoe prior over node pre-activations of a Bayesian neural network, which effectively turns off nodes that do not help explain the data, and prevents the BNN from under-fitting even when the number of nodes required is grossly over-estimated.
- 121
- Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes117
This work introduces a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings and replaces the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference.
- Mind the Gap: a generative approach to interpretable feature selection and extraction115
The Mind the Gap Model is presented, an approach for interpretable feature extraction and selection that allows for the model to both optimize parameters related to interpretability and to directly report a global set of distinguishable dimensions to assist with further data exploration and hypothesis generation.
- 113
- The Infinite Partially Observable Markov Decision Process112
An infinite POMDP (iPOMDP) model is defined that does not require knowledge of the size of the state space and assumes that the number of visited states will grow as the agent explores its world and only models visited states explicitly.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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