Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

The narrative surrounding Artificial Intelligence in high-finance, particularly in the high-stakes arena of trading, has long ...

Machine learning systems embed preferences either in training losses or through post-processing of calibrated predictions. Applying information design methods from Strack and Yang (2024), this paper ...

Résumé screeners, keyword-matching tools, AI-assisted video interviews are filtering applicants. For job seekers, the challenge is about learning how to pass digital gatekeepers ...

Investors make predictable mistakes when forecasting earnings and stock returns, but machine learning models avoid them through adaptive learning.

MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more ...

The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to explore the application of causal ...

The full whitepaper is available for download here .

Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...

Researchers used 1 million data points and a machine learning algorithm to estimate groundwater stores with higher resolution ...

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...

For a project in Bangladesh, Prof. Mushfiq Mobarak and his team used machine-learning models applied to mobile phone records ...