steverab / failing-loudly
Code repository for our paper "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift": https://arxiv.org/abs/1810.11953
☆104Updated last year
Alternatives and similar repositories for failing-loudly:
Users that are interested in failing-loudly are comparing it to the libraries listed below
- Model Agnostic Counterfactual Explanations☆87Updated 2 years ago
- A repo for transfer learning with deep tabular models☆102Updated 2 years ago
- Repository for code release of paper "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" (AISTATS 2020)☆50Updated 5 years ago
- Training and evaluating NBM and SPAM for interpretable machine learning.☆78Updated 2 years ago
- Tools for training explainable models using attribution priors.☆124Updated 4 years ago
- ☆32Updated 3 years ago
- WeightedSHAP: analyzing and improving Shapley based feature attributions (NeurIPS 2022)☆160Updated 2 years ago
- XAI-Bench is a library for benchmarking feature attribution explainability techniques☆65Updated 2 years ago
- Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…☆149Updated 2 years ago
- ☆31Updated 3 years ago
- A benchmark for distribution shift in tabular data☆52Updated 11 months ago
- A practical Active Learning python package with a strong focus on experiments.☆51Updated 2 years ago
- An amortized approach for calculating local Shapley value explanations☆97Updated last year
- Reusable BatchBALD implementation☆79Updated last year
- CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox☆44Updated 2 weeks ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆131Updated 4 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆40Updated 2 years ago
- The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).☆220Updated last year
- ☆134Updated 5 years ago
- Neural Additive Models (Google Research)☆69Updated 3 years ago
- ☆125Updated 3 years ago
- [NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets☆85Updated 2 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 3 years ago
- SPEAR: Programmatically label and build training data quickly.☆106Updated 10 months ago
- Weakly Supervised End-to-End Learning (NeurIPS 2021)☆156Updated 2 years ago
- Model-agnostic posthoc calibration without distributional assumptions☆42Updated last year
- For calculating global feature importance using Shapley values.☆268Updated last week
- A Natural Language Interface to Explainable Boosting Machines☆66Updated 10 months ago
- automatic data slicing☆34Updated 3 years ago
- ☆20Updated 6 years ago