Yu-Group / imodels-experimentsLinks
Experiments with experimental rule-based models to go along with imodels.
☆18Updated last month
Alternatives and similar repositories for imodels-experiments
Users that are interested in imodels-experiments are comparing it to the libraries listed below
Sorting:
- ☆16Updated last year
- Duke Natural Language Processing Winter School 2020☆21Updated 5 years ago
- Code and webpages for our study on teaching humans to defer to an AI☆12Updated 2 years ago
- Code for "Consistent Estimators for Learning to Defer to an Expert" (ICML 2020)☆15Updated 2 years ago
- A python package for semi-structured deep distributional regression☆23Updated 3 years ago
- Code to reproduce the numerical experiments in the paper Domain adaptation under structural causal models by Yuansi Chen and Peter Bühlma…☆18Updated 4 years ago
- Implementation of algorithms from the paper "Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application…☆25Updated 3 years ago
- SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks☆14Updated 2 years ago
- Enhanced Explainable Neural Network☆10Updated 3 years ago
- Source code for the ACML 2019 paper "Functional Isolation Forest".☆20Updated 3 years ago
- Code accompanying our ICML 2020 paper on choice set optimization in group decision-making.☆11Updated 5 years ago
- Rule Extraction Methods for Interactive eXplainability☆48Updated 3 years ago
- ☆18Updated 3 years ago
- ☆18Updated 2 years ago
- Code repository for the AAAI 2022 paper "Do Feature Attribution Methods Correctly Attribute Features?"☆21Updated 3 years ago
- ☆12Updated 3 years ago
- Code associated with paper: Plug-in Regularized Estimation of High-Dimensional Parameters in Nonlinear Semiparametric Models, Chernozhuk…☆16Updated 4 years ago
- An optimization-based algorithm to accurately estimate the causal effects and robustly predict under distribution shifts. It leverages th…☆14Updated last year
- ☆16Updated 3 years ago
- A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data☆62Updated 4 months ago
- Bayesian or-of-and☆36Updated 3 years ago
- Implementation of unbiased measurement of feature importance in Random Forests☆19Updated 4 years ago
- Efficient Conformal Prediction via Cascaded Inference with Expanded Admission☆20Updated 4 years ago
- Code and data for "Heterogeneous Supervised Topic Models"☆11Updated 3 years ago
- Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"☆15Updated 3 years ago
- Conditional calibration of conformal p-values for outlier detection.☆37Updated 3 years ago
- Causing: CAUsal INterpretation using Graphs☆60Updated last week
- ☆11Updated 4 years ago
- Multi-Objective Counterfactuals☆43Updated 3 years ago
- Learning clinical-decision rules with interpretable models.☆20Updated 2 years ago