MONET represents tasks as graph nodes and uses neighbor-based crossover plus per-task mutation to transfer knowledge, matching or exceeding MAP-Elites performance on four large-scale simulation domains.
Otsu, A threshold selection method from gray- level histograms, IEEE Transactions on Systems, Man, and Cybernetics 9 (1979) 62–66
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UniJDOT is a new optimal-transport method for universal domain adaptation on time series that accounts for unknown target samples in the transport cost, adds a joint decision space and auto-thresholding, and uses a Fourier layer to reach state-of-the-art performance.
The paper proposes a Causal-Agency Framework to restore human causal control at AI interfaces by combining causal models, uncertainty quantification, and human-centered evaluation.
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Multi-Task Optimization over Networks of Tasks
MONET represents tasks as graph nodes and uses neighbor-based crossover plus per-task mutation to transfer knowledge, matching or exceeding MAP-Elites performance on four large-scale simulation domains.
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Deep Joint Distribution Optimal Transport for Universal Domain Adaptation on Time Series
UniJDOT is a new optimal-transport method for universal domain adaptation on time series that accounts for unknown target samples in the transport cost, adds a joint decision space and auto-thresholding, and uses a Fourier layer to reach state-of-the-art performance.
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Human Agency, Causality, and the Human Computer Interface in High-Stakes Artificial Intelligence
The paper proposes a Causal-Agency Framework to restore human causal control at AI interfaces by combining causal models, uncertainty quantification, and human-centered evaluation.