Pinned Repositories
air
Data Engineering for Data Scientists
clairvoyance
Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series
kubernetes_example
5-Step Kubernetes CI/CD Process using Artifactory & Helm
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
pymc
PyMC version 3 (PyMC 2 is in branch 2.3)
ray_tutorial
An introductory tutorial about leveraging Ray core features for distributed patterns.
SIN
Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)
pgnepal's Repositories
pgnepal/clairvoyance
Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series
pgnepal/ray_tutorial
An introductory tutorial about leveraging Ray core features for distributed patterns.
pgnepal/SIN
Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)
pgnepal/APA
Uplift Modeling for Multiple Treatments
pgnepal/awesome-causality
Resources related to causality
pgnepal/az304
Exam AZ-304 - Microsoft Azure Architect Design Crash Course
pgnepal/causal_salad_2021
One day course on causal inference, MPI-EVA 9 September 2021
pgnepal/causalinf_ex_elasticity
A full example for causal inference on real-world retail data, for elasticity estimation
pgnepal/counterfactual-cv
(ICML2020) “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models’’
pgnepal/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020
Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020.
pgnepal/DeepReinforcementLearningInAction
Code from the Deep Reinforcement Learning in Action book from Manning, Inc
pgnepal/DICE
The official implementation of "Disentangling User Interest and Conformity for Recommendation with Causal Embedding" (WWW '21)
pgnepal/Ensemble_model_PySpark
ensemble model, PySpark, stacking, bagging, boosting
pgnepal/faiss-on-disk-example
Example of out-of-RAM k-nearest neighbors search using faiss
pgnepal/garage
A toolkit for reproducible reinforcement learning research.
pgnepal/gluon-ts
Probabilistic time series modeling in Python
pgnepal/intro_bayesian_causal
Repository for Introduction to Bayesian Estimation of Causal Effects
pgnepal/keras-io
Keras documentation, hosted live at keras.io
pgnepal/lost-stats.github.io
Source code for the Library of Statistical Techniques
pgnepal/mr_uplift
Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and multiple responses
pgnepal/portfolio
My Portfolio
pgnepal/practical-python
Practical Python Programming (course by @dabeaz)
pgnepal/Practical_RL
A course in reinforcement learning in the wild
pgnepal/pymc-marketing
Bayesian marketing toolbox in PyMC. Media Mix, CLV models and more.
pgnepal/python-causality-handbook
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.
pgnepal/recsys2021-tutorial
https://sites.google.com/cornell.edu/recsys2021tutorial
pgnepal/sktime
A unified toolbox for machine learning with time series
pgnepal/synthetic_control
synthetic control using count data
pgnepal/timeseers
Time should be taken seer-iously
pgnepal/TMLEworkshop
Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.