Alternatives and similar repositories for controllabledl

Users that are interested in controllabledl are comparing it to the libraries listed below

• iancovert / removal-explanations
  A lightweight implementation of removal-based explanations for ML models.
  ☆58Updated 4 years ago
• facebookresearch / nbm-spam
  Training and evaluating NBM and SPAM for interpretable machine learning.
  ☆78Updated 2 years ago
• YilunZhou / ExSum
  Code repository for the NAACL 2022 paper "ExSum: From Local Explanations to Model Understanding"
  ☆64Updated 3 years ago
• steverab / failing-loudly
  Code repository for our paper "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift": https://arxiv.org/abs/1810.119…
  ☆106Updated last year
• benbo / interactive-weak-supervision
  Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
  ☆31Updated 4 years ago
• ykwon0407 / beta_shapley
  Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)
  ☆41Updated 2 years ago
• AmrMKayid / nam
  Neural Additive Models (Google Research)
  ☆71Updated 3 years ago
• mtsang / archipelago
  Official Code Repo for the Paper: "How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions", In NeurIPS 2…
  ☆40Updated 2 years ago
• carolinlawrence / gradient-rollback
  Code for gradient rollback, which explains predictions of neural matrix factorization models, as for example used for knowledge base comp…
  ☆21Updated 4 years ago
• pietrobarbiero / logic_explained_networks
  Logic Explained Networks is a python repository implementing explainable-by-design deep learning models.
  ☆51Updated 2 years ago
• sxjscience / automl_multimodal_benchmark
  Repository for Multimodal AutoML Benchmark
  ☆65Updated 3 years ago
• HazyResearch / domino
  ☆139Updated last year
• google-deepmind / conformal_training
  This repository contains a Jax implementation of conformal training corresponding to the ICLR'22 paper "learning optimal conformal classi…
  ☆130Updated 3 years ago
• nalexai / hyperlib
  Library that contains implementations of machine learning components in the hyperbolic space
  ☆141Updated last year
• bhaweshiitk / ConformalLLM
  Extending Conformal Prediction to LLMs
  ☆67Updated last year
• autonlab / weasel
  Weakly Supervised End-to-End Learning (NeurIPS 2021)
  ☆156Updated 2 years ago
• jjbrophy47 / tree_influence
  Influence Estimation for Gradient-Boosted Decision Trees
  ☆30Updated last year
• automl / zero-shot-automl-with-pretrained-models
  Official repository for the paper "Zero-Shot AutoML with Pretrained Models"
  ☆47Updated last year
• interpretml / TalkToEBM
  A Natural Language Interface to Explainable Boosting Machines
  ☆68Updated last year
• microsoft / augmented-interpretable-models
  Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
  ☆44Updated 6 months ago
• abacusai / xai-bench
  XAI-Bench is a library for benchmarking feature attribution explainability techniques
  ☆70Updated 2 years ago
• dmitrykazhdan / concept-based-xai
  Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
  ☆54Updated 3 years ago
• UW-Madison-Lee-Lab / LanguageInterfacedFineTuning
  Code for Language-Interfaced FineTuning for Non-Language Machine Learning Tasks.
  ☆130Updated 10 months ago
• machinelearningnuremberg / WellTunedSimpleNets
  [NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets
  ☆87Updated 2 years ago
• easeml / datascope
  Measuring data importance over ML pipelines using the Shapley value.
  ☆43Updated last month
• data-centric-ai / dcbench
  A benchmark of data-centric tasks from across the machine learning lifecycle.
  ☆72Updated 3 years ago
• pietrobarbiero / pytorch_explain
  PyTorch Explain: Interpretable Deep Learning in Python.
  ☆161Updated last year
• jamestszhim / modals
  MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space
  ☆41Updated 4 years ago
• suinleelab / path_explain
  A repository for explaining feature attributions and feature interactions in deep neural networks.
  ☆188Updated 3 years ago
• yannadani / cbed
  Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.
  ☆20Updated 2 years ago