Systems | Information | Learning | Optimization
 

Nonconvex Distributed Optimization

We consider the distributed optimization problem where a group of agents seeks to cooperatively compute the optimizer of the average of local functions. To solve this problem, we propose a novel algorithm which adjusts the ratio between the number of communications and computations to achieve fast convergence. In particular, the …

Sample Complexity of Species Tree Estimation From a Linear Combination of Internode Distances

We consider the problem of estimating the species tree from large numbers of unrooted gene tree topologies in the presence of incomplete lineage sorting, a phenomenon which enforces heterogeneity among the set of gene trees. This phenomenon is modeled with the Multispecies Coalescent Process. We make progress towards deriving a …

Deep Learning with Small Datasets: Tips, Tricks, and Cautionary Tales

Many modern applications of deep networks are generative or discriminatory tasks that leverage large datasets and computational power to achieve state-of-the-art results on tasks in important, wide-reaching regimes. However, some applications are more data-constrained. This talk will focus on uses of neural networks in medical imaging and remote sensing, two …

Species tree reconstruction from locus-based data under gene duplication and loss

Evolutionary relationships between species are often depicted using a phylogenetic tree. The structure of the tree depends on the species’ genomes. A common method for deducing the tree is to analyze the genomic evolution of particular loci, but under a basic assumption of gene duplication and loss, the tree implied …