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Ensembles for sequence learning

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The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

Model agnostic methods meta-learn despite misspecifications

Data-driven IQC-Based Robust Control Design for Hybrid Micro-disturbance Isolation Platform

Gradient boosting with extreme-value theory for wildfire prediction

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

Generating Controlled Physics-Informed Time-to-failure Trajectories for Prognostics in Unseen Operational Conditions

Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets

Partial Information Sharing Over Social Learning Networks

Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units