RunzheStat

aaai2021

This is the repo for the source code of the AAAI2021 paper ``Near-Optimal MNL Bandits Under Risk Criteria"

bandits

Public repository for the work on bandit problems

Causal-Decision-Making

CausalDM

A Tutorial on Causal Decision Making with an Accompanying Python Package

CausalMARL

causalml

Uplift modeling and causal inference with machine learning algorithms

ContextDistributions

Code accompanying the Neurips 2019 paper "Stochastic Bandits with Context Distributions"

continuous-policy-learning

D2OPE

TestMDP

Implementation of "Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making”(ICML 2020) in Python

RunzheStat/TestMDP

Implementation of "Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making”(ICML 2020) in Python

RunzheStat/D2OPE

RunzheStat/CausalMARL

RunzheStat/aaai2021

This is the repo for the source code of the AAAI2021 paper ``Near-Optimal MNL Bandits Under Risk Criteria"

RunzheStat/bandits

Public repository for the work on bandit problems

RunzheStat/Causal-Decision-Making

RunzheStat/CausalDM

A Tutorial on Causal Decision Making with an Accompanying Python Package

RunzheStat/causalml

Uplift modeling and causal inference with machine learning algorithms

RunzheStat/ContextDistributions

Code accompanying the Neurips 2019 paper "Stochastic Bandits with Context Distributions"

RunzheStat/continuous-policy-learning

RunzheStat/COVID

RunzheStat/Deep-Bayesian-Bandits-Showdown

Models and examples built with TensorFlow

RunzheStat/DeepBeerInventory-RL

The code for the SRDQN algorithm to train an agent for the beer game problem

RunzheStat/DoubleReinforcementLearningMDP

RunzheStat/EconML

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.

RunzheStat/ESPRM

Code for Efficient Policy Learning from Surrogate-Loss Classification Reductions paper

RunzheStat/google-research

Google Research

RunzheStat/Movie-Recommendation-using-Cascading-Bandits

Movie Recommendation using Cascading Bandits namely CascadeLinTS and CascadeLinUCB

RunzheStat/PCMC-Net

PCMC-Net: Feature-based Pairwise Choice Markov Chains

RunzheStat/PolicyLearning

RunzheStat/RetargetedPolicyLearning

RunzheStat/rl-baselines-zoo-1

A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.

RunzheStat/StochOptForest

Replication Code for Paper "Stochastic Optimization Forests".

RunzheStat/Synthetic-Control