applicable models
parametric
Models with a fixed number of parameters
16 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Coefficient Magnitudes (in Linear Models) | Metric | Architecture/linear Models Paradigm/parametric +2 | Tabular | Coefficient Magnitudes assess feature influence in linear models by examining the absolute values of their coefficients.... | ||
| Integrated Gradients | Algorithmic | Architecture/neural Networks Paradigm/parametric +3 | Any | Integrated Gradients is an attribution technique that explains a model's prediction by quantifying the contribution of... | ||
| Layer-wise Relevance Propagation | Algorithmic | Architecture/neural Networks Paradigm/parametric +2 | Any | Layer-wise Relevance Propagation (LRP) explains neural network predictions by working backwards through the network to... | ||
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +6 | Any | Influence functions quantify how much each training example influenced a model's predictions by computing the change in... | ||
| Federated Learning | Algorithmic | Architecture/linear Models Architecture/neural Networks +4 | Any | Federated learning enables collaborative model training across multiple distributed parties (devices, organisations, or... | ||
| Homomorphic Encryption | Algorithmic | Architecture/linear Models Architecture/neural Networks/feedforward +4 | Any | Homomorphic encryption allows computation on encrypted data without decrypting it first, producing encrypted results... | ||
| Quantile Regression | Algorithmic | Architecture/linear Models/regression Architecture/neural Networks +4 | Any | Quantile regression estimates specific percentiles (quantiles) of the target variable rather than just predicting the... | ||
| Deep Ensembles | Algorithmic | Architecture/neural Networks Paradigm/parametric +2 | Any | Deep ensembles combine predictions from multiple neural networks trained independently with different random... | ||
| Model Distillation | Algorithmic | Architecture/neural Networks Paradigm/parametric +3 | Any | Model distillation transfers knowledge from a large, complex model (teacher) to a smaller, more efficient model... | ||
| Generalized Additive Models | Algorithmic | Architecture/linear Models/gam Paradigm/parametric +2 | Tabular | An intrinsically interpretable modelling technique that extends linear models by allowing flexible, nonlinear... | ||
| Model Pruning | Algorithmic | Architecture/neural Networks Paradigm/parametric +4 | Any | Model pruning systematically removes less important weights, neurons, or entire layers from neural networks to create... | ||
| Fair Adversarial Networks | Algorithmic | Architecture/neural Networks Paradigm/discriminative +6 | Any | An in-processing fairness technique that employs adversarial training with dual neural networks to learn fair... | ||
| Exponentiated Gradient Reduction | Algorithmic | Architecture/model Agnostic Paradigm/discriminative +5 | Any | An in-processing fairness technique based on Agarwal et al.'s reductions approach that transforms fair classification... | ||
| Fair Transfer Learning | Algorithmic | Architecture/neural Networks Paradigm/parametric +4 | Any | An in-processing fairness technique that adapts pre-trained models from one domain to another whilst explicitly... | ||
| Adaptive Sensitive Reweighting | Algorithmic | Architecture/model Agnostic Paradigm/parametric +3 | Any | Adaptive Sensitive Reweighting dynamically adjusts the importance of training examples during model training based on... | ||
| Bayesian Fairness Regularization | Algorithmic | Architecture/model Agnostic Paradigm/parametric +4 | Any | Bayesian Fairness Regularization incorporates fairness constraints into machine learning models through Bayesian... |
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