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Found 16 papers, 12 papers with code
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI
Additionally, we propose the Hardness Characterization Analysis Toolkit (H-CAT), which supports comprehensive and quantitative benchmarking of HCMs across the hardness taxonomy and can easily be extended to new HCMs, hardness types, and datasets.
A Neural Framework for Generalized Causal Sensitivity Analysis
Unobserved confounding is common in many applications, making causal inference from observational data challenging.
Redefining Digital Health Interfaces with Large Language Models
We develop a new prognostic tool using automated machine learning and demonstrate how LLMs can provide a unique interface to both our model and existing risk scores, highlighting the benefit compared to traditional interfaces for digital tools.
Machine Learning with Requirements: a Manifesto
In the recent years, machine learning has made great advancements that have been at the root of many breakthroughs in different application domains.
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
In this work, we introduce Tabular Neural Gradient Orthogonalization and Specialization (TANGOS), a novel framework for regularization in the tabular setting built on latent unit attributions.
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
SurvivalGAN outperforms multiple baselines at generating survival data, and in particular addresses the failure modes as measured by the new metrics, in addition to improving downstream performance of survival models trained on the synthetic data.
Improving Adaptive Conformal Prediction Using Self-Supervised Learning
However, the use of self-supervision beyond model pretraining and representation learning has been largely unexplored.
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
However, this remains a nascent area with no standardized framework to guide practitioners to the necessary data-centric considerations or to communicate the design of data-centric driven ML systems.
Composite Feature Selection using Deep Ensembles
To do so, we define predictive groups in terms of linear and non-linear interactions between features.
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning
However, the use of machine learning introduces a number of technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings.
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
To assess solutions to this problem, we propose a controllable simulation environment based on a model of tumor growth for a range of scenarios with irregular sampling reflective of a variety of clinical scenarios.
Differentiable and Transportable Structure Learning
Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their structure.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
However, no imputation at all also leads to biased estimates, as missingness determined by treatment introduces bias in covariates.
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Significant effort has been placed on developing decision support tools to improve patient care.
Explaining Latent Representations with a Corpus of Examples
SimplEx uses the corpus to improve the user's understanding of the latent space with post-hoc explanations answering two questions: (1) Which corpus examples explain the prediction issued for a given test example?
Self-Supervision Enhanced Feature Selection with Correlated Gates
Discovering relevant input features for predicting a target variable is a key scientific question.
