kdd-lab / XAI-Lib

Related projects ⓘ

Alternatives and complementary repositories for XAI-Lib

• AIgen / df-posthoc-calibration
  Model-agnostic posthoc calibration without distributional assumptions
  ☆42Updated last year
• stefanoteso / caipi
  CAIPI turns LIMEs into trust!
  ☆12Updated 4 years ago
• EleMisi / VAEL
  Codebase for VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
  ☆19Updated last year
• clementchadebec / pyraug
  Data Augmentation with Variational Autoencoders (TPAMI)
  ☆136Updated 2 years ago
• almost-matching-exactly / DAME-FLAME-Python-Package
  A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data
  ☆57Updated 5 months ago
• JAEarly / MILLI
  Code for the paper "Model Agnostic Interpretability for Multiple Instance Learning".
  ☆13Updated 2 years ago
• utwente-dmb / xai-papers
  ☆16Updated 3 months ago
• annahedstroem / MetaQuantus
  MetaQuantus is an XAI performance tool to identify reliable evaluation metrics
  ☆30Updated 7 months ago
• dmitrykazhdan / concept-based-xai
  Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
  ☆52Updated 2 years ago
• ubc-systopia / treeFarms
  Trees FAst RashoMon Sets
  ☆37Updated 3 months ago
• cambridge-mlg / CLUE
  Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
  ☆36Updated 6 months ago
• sherbold / autorank
  ☆71Updated last month
• pietrobarbiero / pytorch_explain
  PyTorch Explain: Interpretable Deep Learning in Python.
  ☆146Updated 6 months ago
• HelmholtzAI-Consultants-Munich / PySDDR
  A python package for semi-structured deep distributional regression
  ☆20Updated last year
• Jimmy-Lin / GeneralizedOptimalSparseDecisionTrees
  Generalized Optimal Sparse Decision Trees
  ☆62Updated 8 months ago
• ema-marconato / glancenet
  Updated code base for GlanceNets: Interpretable, Leak-proof Concept-based models
  ☆25Updated last year
• mlfoundations / tableshift
  A benchmark for distribution shift in tabular data
  ☆44Updated 5 months ago
• fel-thomas / Sobol-Attribution-Method
  👋 Code for the paper: "Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis" (NeurIPS 2021)
  ☆27Updated 2 years ago
• DBrughmans / NICE
  ☆22Updated last year
• ahmedmalaa / Symbolic-Metamodeling
  Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.
  ☆49Updated 5 years ago
• wilsonjr / ClusterShapley
  Explaining dimensionality results using SHAP values
  ☆52Updated 2 years ago
• sciai-lab / cl-tsne-umap
  ☆33Updated last year
• aangelopoulos / conformal-risk
  Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vi…
  ☆59Updated last year
• bdwilliamson / vimpy
  Perform inference on algorithm-agnostic variable importance in Python
  ☆20Updated 2 years ago
• benchopt / template_benchmark
  ☆25Updated last month
• tml-tuebingen / nshap
  Python package to compute interaction indices that extend the Shapley Value. AISTATS 2023.
  ☆17Updated last year
• tornede / py_experimenter
  The PyExperimenter is a tool for the automatic execution of experiments, e.g. for machine learning (ML), capturing corresponding results …
  ☆33Updated last month
• seedatnabeel / Data-SUITE
  Data-SUITE: Data-centric identification of in-distribution incongruous examples (ICML 2022)
  ☆9Updated last year
• zzzace2000 / nodegam
  Code for "NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning"
  ☆43Updated 2 years ago
• donato-maragno / robust-CE
  Generate robust counterfactual explanations for machine learning models
  ☆14Updated last year