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Found 16 papers, 10 papers with code
A hierarchical decomposition for explaining ML performance discrepancies
Machine learning (ML) algorithms can often differ in performance across domains.
A Brief Tutorial on Sample Size Calculations for Fairness Audits
In fairness audits, a standard objective is to detect whether a given algorithm performs substantially differently between subgroups.
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
When an ML algorithm interacts with its environment, the algorithm can affect the data-generating mechanism and be a major source of bias when evaluating its standalone performance, an issue known as performativity.
Is this model reliable for everyone? Testing for strong calibration
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup.
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions
Performance monitoring of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the very target that the algorithm aims to predict.
Sequential algorithmic modification with test data reuse
Each modification introduces a risk of deteriorating performance and must be validated on a test dataset.
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees
In the COPD study, BLR and MarBLR dynamically combined the original model with a continually-refitted gradient boosted tree to achieve aAUCs of 0. 924 (95%CI 0. 913-0. 935) and 0. 925 (95%CI 0. 914-0. 935), compared to the static model's aAUC of 0. 904 (95%CI 0. 892-0. 916).
Learning how to approve updates to machine learning algorithms in non-stationary settings
Machine learning algorithms in healthcare have the potential to continually learn from real-world data generated during healthcare delivery and adapt to dataset shifts.
Efficient nonparametric statistical inference on population feature importance using Shapley values
The true population-level importance of a variable in a prediction task provides useful knowledge about the underlying data-generating mechanism and can help in deciding which measurements to collect in subsequent experiments.
Ensembled sparse-input hierarchical networks for high-dimensional datasets
Neural networks have seen limited use in prediction for high-dimensional data with small sample sizes, because they tend to overfit and require tuning many more hyperparameters than existing off-the-shelf machine learning methods.
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creep
Successful deployment of machine learning algorithms in healthcare requires careful assessments of their performance and safety.
Selective prediction-set models with coverage guarantees
Though black-box predictors are state-of-the-art for many complex tasks, they often fail to properly quantify predictive uncertainty and may provide inappropriate predictions for unfamiliar data.
An analysis of the cost of hyper-parameter selection via split-sample validation, with applications to penalized regression
We establish that the fitted models are Lipschitz in the penalty parameters and thus our oracle inequalities apply.
Nonparametric variable importance using an augmented neural network with multi-task learning
In predictive modeling applications, it is often of interest to determine the relative contribution of subsets of features in explaining the variability of an outcome.
Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and Classification
In addition, we characterize the statistical convergence of the penalized empirical risk minimizer to the optimal neural network: we show that the excess risk of this penalized estimator only grows with the logarithm of the number of input features; and we show that the weights of irrelevant features converge to zero.
Gradient-based Regularization Parameter Selection for Problems with Non-smooth Penalty Functions
It is more efficient to tune parameters if the gradient can be determined, but this is often difficult for problems with non-smooth penalty functions.
