Interactive Visualization to empower domain experts to align ML model behaviors with their knowledge.

Overview

An interactive visualization system designed to helps domain experts responsibly edit Generalized Additive Models (GAMs).

build pypi license arxiv badge

For more information, check out our manuscript:

GAM Changer: Editing Generalized Additive Models with Interactive Visualization. Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana. Research2Clinics Workshop at NeurIPS, 2021.

Live Demo

For a live demo, visit: http://interpret.ml/gam-changer/

Running Locally

Clone or download this repository:

git clone [email protected]:interpretml/gam-changer.git

# use degit if you don't want to download commit histories
degit interpretml/gam-changer.git

Install the dependencies:

npm install

Then run GAM Changer:

npm run dev

Navigate to localhost:5000. You should see GAM Changer running in your browser :)

Credits

GAM Changer is created by Jay Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Polo Chau, Mickey Vorvoreanu, Jenn Wortman Vaughan, and Rich Caruana, which was the result of a research collaboration between Microsoft Research, NYU Langone Health, Georgia Tech and University of Washington. Jay Wang and Alex Kale were summer interns at Microsoft Research.

We thank Steven Drucker, Adam Fourney, Saleema Amershi, Dean Carignan, Rob DeLine, and the InterpretML team for their support and constructive feedback.

Citation

@article{wangGAMChangerEditing2021,
  title = {{{GAM Changer}}: {{Editing Generalized Additive Models}} with {{Interactive Visualization}}},
  shorttitle = {{{GAM Changer}}},
  author = {Wang, Zijie J. and Kale, Alex and Nori, Harsha and Stella, Peter and Nunnally, Mark and Chau, Duen Horng and Vorvoreanu, Mihaela and Vaughan, Jennifer Wortman and Caruana, Rich},
  year = {2021},
  month = dec,
  journal = {arXiv:2112.03245 [cs]},
  url = {https://interpret.ml/gam-changer},
  archiveprefix = {arXiv}
}

License

The software is available under the MIT License.

Contact

If you have any questions, feel free to open an issue or contact Jay Wang.

Owner
InterpretML
If a tree fell in your random forest, would anyone notice?
InterpretML
Safe Model-Based Reinforcement Learning using Robust Control Barrier Functions

README Repository containing the code for the paper "Safe Model-Based Reinforcement Learning using Robust Control Barrier Functions". Specifically, an

Yousef Emam 13 Nov 24, 2022
This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022

CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC

LEAP Lab 2 Sep 15, 2022
🥇Samsung AI Challenge 2021 1등 솔루션입니다🥇

MoT - Molecular Transformer Large-scale Pretraining for Molecular Property Prediction Samsung AI Challenge for Scientific Discovery This repository is

Jungwoo Park 44 Dec 03, 2022
Official implementation for the paper "SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization".

SAPE Project page Paper Official implementation for the paper "SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization". Environment Cre

36 Dec 09, 2022
Demystifying How Self-Supervised Features Improve Training from Noisy Labels

Demystifying How Self-Supervised Features Improve Training from Noisy Labels This code is a PyTorch implementation of the paper "[Demystifying How Sel

<a href=[email protected]"> 4 Oct 14, 2022
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR)

Ilya Kostrikov 3k Dec 31, 2022
ChebLieNet, a spectral graph neural network turned equivariant by Riemannian geometry on Lie groups.

ChebLieNet: Invariant spectral graph NNs turned equivariant by Riemannian geometry on Lie groups Hugo Aguettaz, Erik J. Bekkers, Michaël Defferrard We

haguettaz 12 Dec 10, 2022
FindFunc is an IDA PRO plugin to find code functions that contain a certain assembly or byte pattern, reference a certain name or string, or conform to various other constraints.

FindFunc: Advanced Filtering/Finding of Functions in IDA Pro FindFunc is an IDA Pro plugin to find code functions that contain a certain assembly or b

213 Dec 17, 2022
Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

LEXA Benchmark Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models

Oleg Rybkin 36 Dec 22, 2022
text_recognition_toolbox: The reimplementation of a series of classical scene text recognition papers with Pytorch in a uniform way.

text recognition toolbox 1. 项目介绍 该项目是基于pytorch深度学习框架,以统一的改写方式实现了以下6篇经典的文字识别论文,论文的详情如下。该项目会持续进行更新,欢迎大家提出问题以及对代码进行贡献。 模型 论文标题 发表年份 模型方法划分 CRNN 《An End-t

168 Dec 24, 2022
Learning To Have An Ear For Face Super-Resolution

Learning To Have An Ear For Face Super-Resolution [Project Page] This repository contains demo code of our CVPR2020 paper. Training and evaluation on

50 Nov 16, 2022
A Runtime method overload decorator which should behave like a compiled language

strongtyping-pyoverload A Runtime method overload decorator which should behave like a compiled language there is a override decorator from typing whi

20 Oct 31, 2022
A PyTorch Implementation of the Luna: Linear Unified Nested Attention

Unofficial PyTorch implementation of Luna: Linear Unified Nested Attention The quadratic computational and memory complexities of the Transformer’s at

Soohwan Kim 32 Nov 07, 2022
Research on Event Accumulator Settings for Event-Based SLAM

Research on Event Accumulator Settings for Event-Based SLAM This is the source code for paper "Research on Event Accumulator Settings for Event-Based

Robin Shaun 26 Dec 21, 2022
Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection (CVPR 2021)

GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Mo

Abhinav Kumar 76 Jan 02, 2023
Atomistic Line Graph Neural Network

Table of Contents Introduction Installation Examples Pre-trained models Quick start using colab JARVIS-ALIGNN webapp Peformances on a few datasets Use

National Institute of Standards and Technology 91 Dec 30, 2022
A library of extension and helper modules for Python's data analysis and machine learning libraries.

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2020 Links Doc

Sebastian Raschka 4.2k Jan 02, 2023
Complex Answer Generation For Conversational Search Systems.

Complex Answer Generation For Conversational Search Systems. Code for Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex

Hanane Djeddal 0 Dec 06, 2021
Repository for the COLING 2020 paper "Explainable Automated Fact-Checking: A Survey."

Explainable Fact Checking: A Survey This repository and the accompanying webpage contain resources for the paper "Explainable Fact Checking: A Survey"

Neema Kotonya 42 Nov 17, 2022