In this talk, the change points of the time series of PM10 of the city of Bogotá are considered. The number of change points and their respective locations are determined using the genetic algorithm. This algorithm considers the interaction of two chromosomes (mother and father) and their mutations, to conceive new generations of descendants …
This talk will focus on how a Lagrangian perspective on dynamics is used to capture the time evolution of complex systems, e.g., granular flows, fluid-solid interaction problems, etc. In this context, the aspects that turn out to be more challenging are tied to the handling of friction, contact, geometry, large …
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 …
Many engineering applications such as infrastructure operation and model predictive control (MPC) involve solving similar optimization problems over and over and over and over again, with slightly varying input parameters. Electric grid optimization, which is influenced by variable renewable energy generation, is a prominent example. In this talk, we consider …
Imagine a drone looking for a safe landing site in a dense forest, or a social robot trying to determine the emotional state of a person by measuring her skin-tremors due to pulse beats. In these applications, it is critical to resolve fine geometric details, such as tree twigs; and …
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in waiting for the slowest workers (stragglers). Asynchronous methods can alleviate stragglers, but cause gradient staleness that can adversely affect convergence. In this work, we present the first theoretical characterization of the speed-up offered by asynchronous …
The problem of optimizing objective functions that involve expectation or integral operators is common in a number of fields. This problem is commonly addressed using one of three frameworks or hybrids thereof: Sample Average Approximation/Monte Carlo (SAA/MC), Bayesian Optimization (BO), and Stochastic Approximation (SA). While the methods that belong to …
A striking feature of modern supervised machine learning is its consistent use of techniques that interpolate the data. Deep networks, often containing several orders of magnitude more parameters than data points, are trained to obtain near zero error on the training set. Yet, at odds with most theory, they show …
From the confusion surrounding the optimization and generalization of deep networks has arisen an exciting possibility: gradient descent is implicitly regularized, meaning it not only outputs iterates of low error, but moreover iterates of low complexity. This talk starts with a “spectrally-normalized” generalization bound which is small if gradient descent …
In this talk, I will present my work on learning policies for navigation. I will talk about three projects. First, I will show how insights from classical mapping and planning can be operationalized in context of learning based policies to arrive at policies that can effectively leverage statistical regularities in …