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Found 14 papers, 11 papers with code
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution
Many tasks in explainable machine learning, such as data valuation and feature attribution, perform expensive computation for each data point and can be intractable for large datasets.
Estimating Conditional Mutual Information for Dynamic Feature Selection
Dynamic feature selection, where we sequentially query features to make accurate predictions with a minimal budget, is a promising paradigm to reduce feature acquisition costs and provide transparency into a model's predictions.
Learning to Maximize Mutual Information for Dynamic Feature Selection
Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets.
What does a platypus look like? Generating customized prompts for zero-shot image classification
Unlike traditional classification models, open-vocabulary models classify among any arbitrary set of categories specified with natural language during inference.
Learning to Estimate Shapley Values with Vision Transformers
Transformers have become a default architecture in computer vision, but understanding what drives their predictions remains a challenging problem.
FastSHAP: Real-Time Shapley Value Estimation
Shapley values are widely used to explain black-box models, but they are costly to calculate because they require many model evaluations.
Disrupting Model Training with Adversarial Shortcuts
When data is publicly released for human consumption, it is unclear how to prevent its unauthorized usage for machine learning purposes.
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression
The Shapley value concept from cooperative game theory has become a popular technique for interpreting ML models, but efficiently estimating these values remains challenging, particularly in the model-agnostic setting.
Explaining by Removing: A Unified Framework for Model Explanation
We describe a new unified class of methods, removal-based explanations, that are based on the principle of simulating feature removal to quantify each feature's influence.
Feature Removal Is a Unifying Principle for Model Explanation Methods
Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another.
Understanding Global Feature Contributions With Additive Importance Measures
Understanding the inner workings of complex machine learning models is a long-standing problem and most recent research has focused on local interpretability.
Deep unsupervised feature selection
Unsupervised feature selection involves finding a small number of highly informative features, in the absence of a specific supervised learning task.
Temporal Graph Convolutional Networks for Automatic Seizure Detection
Commonly used deep learning models for time series don't offer a way to leverage structural information, but this would be desirable in a model for structural time series.
Neural Granger Causality
We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data.
