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Found 20 papers, 6 papers with code
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
Predictive Churn with the Set of Good Models
And we characterize expected churn over model updates via the Rashomon set, pairing our analysis with empirical results on real-world datasets -- showing how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
Learning from Time Series under Temporal Label Noise
We first demonstrate the importance of modelling the temporal nature of the label noise function and how existing methods will consistently underperform.
FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning
Modern recommender systems may output considerably different recommendations due to small perturbations in the training data.
Prediction without Preclusion: Recourse Verification with Reachable Sets
In turn, models can assign predictions that are fixed, meaning that consumers who are denied loans, interviews, or benefits may be permanently locked out from access to credit, employment, or assistance.
Algorithmic Censoring in Dynamic Learning Systems
In applications like consumer finance, this results in groups of applicants that are persistently denied and thus never enter into the training data.
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction
In this work, we show models that are personalized with group attributes can reduce performance at a group level.
Predictive Multiplicity in Probabilistic Classification
We demonstrate the incidence and prevalence of predictive multiplicity in real-world tasks.
Rank List Sensitivity of Recommender Systems to Interaction Perturbations
We introduce a measure of stability for recommender systems, called Rank List Sensitivity (RLS), which measures how rank lists generated by a given recommender system at test time change as a result of a perturbation in the training data.
Learning Optimal Predictive Checklists
Our results show that our method can fit simple predictive checklists that perform well and that can easily be customized to obey a rich class of custom constraints.
Predictive Multiplicity in Classification
We apply our tools to measure predictive multiplicity in recidivism prediction problems.
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
When the performance of a machine learning model varies over groups defined by sensitive attributes (e. g., gender or ethnicity), the performance disparity can be expressed in terms of the probability distributions of the input and output variables over each group.
Actionable Recourse in Linear Classification
We present integer programming tools to ensure recourse in linear classification problems without interfering in model development.
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning
In the context of machine learning, disparate impact refers to a form of systematic discrimination whereby the output distribution of a model depends on the value of a sensitive attribute (e. g., race or gender).
Learning Optimized Risk Scores
Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers.
Interpretable Classification Models for Recidivism Prediction
We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-making.
Supersparse Linear Integer Models for Optimized Medical Scoring Systems
Scoring systems are linear classification models that only require users to add, subtract and multiply a few small numbers in order to make a prediction.
Methods and Models for Interpretable Linear Classification
We present an integer programming framework to build accurate and interpretable discrete linear classification models.
Supersparse Linear Integer Models for Interpretable Classification
We illustrate the practical and interpretable nature of SLIM scoring systems through applications in medicine and criminology, and show that they are are accurate and sparse in comparison to state-of-the-art classification models using numerical experiments.
Supersparse Linear Integer Models for Predictive Scoring Systems
We introduce Supersparse Linear Integer Models (SLIM) as a tool to create scoring systems for binary classification.
