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AI Blogs
Cluster-Specific Predictions with Multi-Task Gaussian Processes Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey; 24(5):1−49, 2023. Abstract This study introduces a model that utilizes Gaussian processes (GPs) to handle multitask learning, clustering, and prediction for multiple functional data simultaneously. The…
This article examines the potential of deep reinforcement learning (DRL) methods in algorithmic commodities trading. The study presents a novel time-discretization scheme that adjusts to market volatility, improving the statistical properties of financial time series. Two policy gradient algorithms, actor-based…
A Comprehensive Definition of Redundancy and Relevance in Feature Selection Based on Information Decomposition Patricia Wollstadt, Sebastian Schmitt, Michael Wibral; 24(131):1−44, 2023. Abstract In machine learning and statistics, the selection of a minimal set of features that provides maximum information…
We present a new approach to enhance the performance of trading strategies developed through deep reinforcement learning algorithms in the highly unpredictable environment of intraday cryptocurrency portfolio trading. Our method involves using an ensemble technique to improve the generalizability of…
On the Relationship Between Distance and Kernel Measures of Conditional Dependence Tianhong Sheng, Bharath K. Sriperumbudur; 24(7):1−16, 2023. Abstract Measuring conditional dependence is a crucial task in statistical inference and plays a fundamental role in various areas such as causal…
In the realm of finance, accurately predicting stock market trends has always been a formidable challenge. However, with the emergence of machine learning as a powerful tool for forecasting, this research paper undertakes a comparative analysis of four machine learning…
Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu; 24(132):1−57, 2023. Abstract This paper examines the estimation of smooth Generalized Linear Models (GLMs) in the Non-interactive Local Differential…
Sampling Random Graph Homomorphisms and Its Applications in Network Data Analysis Hanbaek Lyu, Facundo Memoli, David Sivakoff; 24(9):1−79, 2023. Abstract A graph homomorphism refers to a mapping between two graphs that preserves adjacency relations. This study focuses on the problem…
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, Daniel B. Neill; 24(133):1−57, 2023. Abstract This study introduces a novel method for addressing the limitations of standard approaches to regression discontinuity (RD) analysis. RD…
Optimal Strategies for Reject Option Classifiers Vojtech Franc, Daniel Prusa, Vaclav Voracek; 24(11):1−49, 2023. Abstract In the context of classification with a reject option, classifiers have the ability to abstain from making predictions in uncertain cases. Traditional cost-based models of…
