Popular GitHub Repositories for Spurious Correlations

Your 24/7 On-Call AI Agent - Solve Alerts Faster with Automatic Correlations, Investigations, and More

We research the correlations between astrology planet cycles and price effect for multiples financial markets using statistics and machine learning techniques. Join the community discussions at Telegram:

NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semantic, and taxonomic correlations between EEG electrode locations and brain regions, resulting in improved accuracy. Presented at PAKDD '24.

The code of paper Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs. Jiajun Chen, Huarui He, Feng Wu, Jie Wang. AAAI 2021.

This repository contains the pytorch code for the 2023 ICASSP paper "Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting”

Discover and Cure: Concept-aware Mitigation of Spurious Correlation (ICML 2023)

Label-Representative Graph Convolutional Network for Multi-Label Text Classification

? Go lottery analyzer with cosmic correlations & statistical wizardry

[AAAI24] Learning Invariant Inter-pixel Correlations for Superpixel Generation

Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-tuning

[ICLR 2025] Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization

Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-tuning

[TVCG 2024] Official implementation of the paper "VSFormer: Mining Correlations in Flexible View Set for Multi-view 3D Shape Understanding"

Code for NAACL 2024 (Findings) Paper: CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP

Automatically generate a pdf report containing feature importance, baseline modelling, spurious correlation detection, and more, from a single command line input for any given ML CSV file

Generate synthetic longitudinal correlated data using distributions and correlations from real-world observational data

This repository contains the code for the paper "Disentangling representations of retinal images with generative models".

Porr, B. and W?rg?tter, F. (2006) Strongly Improved Stability and Faster Convergence of Temporal Sequence Learning by Using Input Correlations Only

A theoretical neural architecture where neurons autonomously learn temporal correlations. Enables continuous learning through binary processing of any input modality without preprocessing. Proposes pathway toward AGI via emergent self-organization and swarm intelligence principles.

This project analyzes stock market data for six major stocks (AAPL, AMZN, NVDA, TSLA, GOOG, SPY) using Python and Power BI. It explores trends, volatility, and correlations.