AmrMKayid / nam
Neural Additive Models (Google Research)
☆67Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for nam
- Code for "NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning"☆43Updated 2 years ago
- PyTorch implementation for Neural Additive Models☆23Updated 3 years ago
- Neural Additive Models (Google Research)☆26Updated 6 months ago
- A lightweight implementation of removal-based explanations for ML models.☆57Updated 3 years ago
- An Empirical Framework for Domain Generalization In Clinical Settings☆27Updated 2 years ago
- A benchmark for distribution shift in tabular data☆44Updated 5 months ago
- For calculating Shapley values via linear regression.☆65Updated 3 years ago
- Training and evaluating NBM and SPAM for interpretable machine learning.☆76Updated last year
- Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"☆36Updated 6 months ago
- This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help …☆23Updated last year
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆35Updated last year
- ☆60Updated 3 years ago
- A repo for transfer learning with deep tabular models☆101Updated last year
- Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…☆68Updated 3 years ago
- Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)☆25Updated 2 years ago
- Our maintained PFN repository. Come here to train SOTA PFNs.☆50Updated last week
- Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR☆60Updated 4 years ago
- Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vi…☆59Updated last year
- Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"☆80Updated 7 months ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆129Updated 4 years ago
- Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI☆52Updated 2 years ago
- Amortized Inference for Causal Structure Learning, NeurIPS 2022☆54Updated 8 months ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆40Updated 2 years ago
- A Data-Centric library providing a unified interface for state-of-the-art methods for hardness characterisation of data points.☆23Updated last month
- Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.☆65Updated 2 weeks ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆64Updated this week
- Local explanations with uncertainty 💐!☆39Updated last year
- XAI-Bench is a library for benchmarking feature attribution explainability techniques☆57Updated last year
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.☆29Updated 5 years ago
- Model-agnostic posthoc calibration without distributional assumptions☆42Updated last year