jphall663
Helping people manage AI, machine learning, and analytics risks at hallresearch.ai; GWU assistant prof.
Washington, DC
Pinned Repositories
awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
diabetes_use_case
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
gai_risk_management
A place for ideas and drafts related to GAI risk management.
GWU_data_mining
Materials for GWU DNSC 6279 and DNSC 6290.
GWU_rml
hc_ml
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
kdd_2019
Paper and talk from KDD 2019 XAI Workshop
secure_ML_ideas
Practical ideas on securing machine learning models
xai_misconceptions
Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!
jphall663's Repositories
jphall663/awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
jphall663/interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
jphall663/GWU_data_mining
Materials for GWU DNSC 6279 and DNSC 6290.
jphall663/secure_ML_ideas
Practical ideas on securing machine learning models
jphall663/GWU_rml
jphall663/xai_misconceptions
Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!
jphall663/diabetes_use_case
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
jphall663/hc_ml
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
jphall663/kdd_2019
Paper and talk from KDD 2019 XAI Workshop
jphall663/responsible_xai
Guidelines for the responsible use of explainable AI and machine learning.
jphall663/jsm_2018_slides
Slides for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
jphall663/jsm_2018_paper
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
jphall663/corr_graph
Short example for creating a correlation graph with Pandas and Gephi.
jphall663/h2oworld_sf_2019
Human-Centered ML Presentation for H2O World SF 2019.
jphall663/gai_risk_management
A place for ideas and drafts related to GAI risk management.
jphall663/ds_interview_qs
Some Data Science Interview Questions (by Me and Former Colleagues at SAS)
jphall663/basic_data_viz_rules_and_links
Some Basic (Hopefully Not Terrible) Data Visualization Rules and Links
jphall663/jsm_2019
Slides for JSM 2019 preso on model debugging strategies
jphall663/lime_xgboost
Simple package for creating LIMEs for XGBoost
jphall663/nafsa_2018_slides
Slides for presentation at NAFSA retreat
jphall663/keep_the_science_in_data_science
Essay about science and data "science".
jphall663/automl_resources
A running list of links for AutoML - very unofficial and incomplete
jphall663/bellarmine_py_intro
Code and materials for Python intro. course.
jphall663/GWU_DNSC_6301_project
Example project for DNSC 6301
jphall663/mli-resources
Machine Learning Interpretability Resources
jphall663/aequitas
Bias and Fairness Audit Toolkit
jphall663/enlighten-apply
Example code and materials that illustrate applications of SAS machine learning techniques.
jphall663/xgb_random_grid_search_example
Example code and data for XGBoost random grid search.
jphall663/DNSC-6301-Project_Group-21
DNSC 6301 Project_Group 21
jphall663/Hello-world
My first repository on GitHub.