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.

It takes advantage of the efficiency of unidirectional recurrent networksandtheeffectiveness ofsliding-window-based methods.

Stochastic Gradient Descent (SGD) and its variants are the main workhorses of modern machine learning. Distributed implementations of SGD on a cluster of machines with a central server and a large number of workers are frequently used in practice due to the massive size of the data. In distributed SGD each machine holds a copy of the model and the computation proceeds in rounds. In every round, each worker finds a stochastic gradient based on its batch of examples, the server averages these stochastic gradients to obtain the gradient of the entire batch, makes an SGD step, and broadcasts the updated model parameters to the workers. With a large number of workers, computation parallelizes efficiently while communication becomes the main bottleneck [Chilimbi et al., 2014, Strom, 2015], since each worker needsto send its gradients to the server and receive the updatedmodel parameters. Commonsolutions for this probleminclude: local SGDand its variants, when each machine performs multiple local steps before communication [Stich, 2018]; decentralized architectureswhich allow pairwisecommunicationbetween the workers [McMahanet al., 2017] and gradient compression, when a compressed version of the gradient is communicated instead of the full gradient [Bernstein et al., 2018, Stich et al., 2018, Karimireddy et al., 2019]. In this work, we consider the latter approach, which we refer to as compressed SGD. Most machine learning models can be described by a d-dimensional vector of parameters x and themodel quality canbe estimatedas a function f(x).