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Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective.
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning
We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and SubscaleHedge, an expert advice algorithm for combining base models trained on subsets of features called subscales.
Towards learning to explain with concept bottleneck models: mitigating information leakage
Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint.
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