syadlowsky Goto Github PK
Name: Steve Yadlowsky
Type: User
Location: Berkeley, CA
Name: Steve Yadlowsky
Type: User
Location: Berkeley, CA
Caffe: a fast open framework for deep learning.
Code implementing "A Calibration Metric for Risk Scores with Survival Data" (MLHC 2019)
Methods for heterogeneous treatment effect estimation
my vim config
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
This is a small repo with my emacs migration plan. Hopefully I will update with my success / failure as it goes.
A user-equilibrium traffic assignment library.
Demo application for the 'Jenkins: The Definitive Guide' book
Generalized Random Forests
Hackers@Berkeley's apps to help people hack better!
This project is a Ruby gem ('hmm') for machine learning that natively implements a (somewhat) generalized Hidden Markov Model classifier. At present, it is capable of supervised learning (using labeled training data) and Viterbi decoding. Unsupervised learning is on the way.
Predicting treatment effects from RCTs (Circulation: CQO 2019).
Simulation demonstrating concerns with IV analysis in "Incremental effects of antihypertensive drugs: instrumental variable analysis"
Basic CMS used for ieee.berkeley.edu
Implementation of iterative hard thresholding in python with the scipy/numpy libraries
Survival analysis in Python
@memoize macro for Julia
Library for simplex constrained optimization via mirror descent
See https://github.com/HackBerkeley/omniauth-hackid for the official version.
A simple gem for bootstrapping DBs for things
VEGETA! WHAT DOES THE SCOUTER SAY ABOUT HIS POWER LEVEL?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.