Given an algorithmic predictor that is "fair" on somesource distribution, will it still be fair on an unknowntarget distribution that differs from the source withinsomebound?

Moreover, we extend ICQ to multi-agent tasks by decomposing the joint-policy under the implicit constraint. Experimental results demonstrate that the extrapolation error is successfully controlled within a reasonable range and insensitive to the number of agents.

This work was done while the author was an intern at Disney Research | Studios 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

In previous work [12, 13], a method was introduced that leverages compressive sensing techniques tofind thefewest taxa thatfitsthefrequencyofshort sequences ofnucleotides (i.e., k-mers) in a given sample. Consider, for instance, an environment/sample made of s bacterial species but where two of them are almost identical: one would wish to say that the concentration vector is almost(s 1)-sparse rather thans-sparse!

For the Spectral Mixture Kernel, we use 4 mixtures. The CNF component for our model was inspired by FFJORD. For NGGP, we use the same CNF component architecture as in for the sines dataset. Adding noise allows for better performance when learning with the CNF component. We also use the same CNF component architecture as in the sines dataset. For this dataset, we tested NGGP and DKT models with RBF and Spectral kernels only.

Weshow that the eigenvalues decay polynomially, quantify the rate of decay, and derive measures that reflect the composition of hierarchical features in these networks.