HAlicia's repositories
python-causality-handbook
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.
Papers_NLP
papers & code for papers
alibi
Algorithms for explaining machine learning models
awesome-causality-algorithms
An index of algorithms for learning causality with data
awesome-chatgpt-zh
ChatGPT 中文指南，ChatGPT 中文调教指南，指令指南，精选资源清单，更好的使用 chatGPT 让你的生产力 up up up!
CARLA
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
causalml
Uplift modeling and causal inference with machine learning algorithms
DecryptPrompt
总结Prompt&LLM论文，开源数据&模型，AIGC应用
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.
FinQwen
FinQwen: 致力于构建一个开放、稳定、高质量的金融大模型项目，基于大模型搭建金融场景智能问答系统，利用开源开放来促进「AI+金融」。
Fusion_2021_Wolf_ContinuousHerdedGibbs
L. M. Wolf and M. Baum, "Continuous Herded Gibbs Sampling"
keras-mmoe
A Keras implementation of "Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts" (KDD 2018)
LLMSurvey
The official GitHub page for the survey paper "A Survey of Large Language Models".
multi-task-learning
TensorFlow implementation of multi-task learning architectures, incl. MMoE & PLE, on wechat dataset
network-deconfounder-wsdm20
Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.
Non-AR-Spatial-Temporal-Transformer
Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting.
PaddleOCR
Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
paper-reading
深度学习经典、新论文逐段精读
pytorch_geometric
Graph Neural Network Library for PyTorch
Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
TradeMaster
TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow:
transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. papers, codes. datasets, applications, tutorials.-迁移学习