Meet YINS, 1/21/15: "Individualized Rank Aggregation using Nuclear Norm Regularization" and "Comparative Cognitive Development in Monkeys and Apes"

Meet YINS, 1/21/15: "Individualized Rank Aggregation using Nuclear Norm Regularization" and "Comparative Cognitive Development in Monkeys and Apes"

Talk Summary: 

Sahand Negahban: In recent years rank aggregation has received significant attention from the machine learning community. The goal of such a problem is to combine the (partially revealed) preferences over objects of a large population into a single, relatively consistent ordering of those objects. However, in many cases, we might not want a single ranking and instead opt for rankings based on individual preferences. We study a version of the problem known as collaborative ranking. In this problem we assume that individual users provide us with pairwise preferences (for example purchasing one item over another). From those preferences we wish to obtain rankings on items that the users have not had an opportunity to explore. We provide a theoretical justification for a nuclear norm regularized optimization procedure, and provide error-bounds for inferring the individual user preferences.

Alexandra Rosati: Human cognition is strikingly different from that of other animals. Hypotheses from evolutionary biology and psychology suggest that unique features of human cognition may emerge due to developmental processes that are specific our species. However, to date there has been few comparative studies that examine cognitive development in nonhumans. I will present recent research examining developmental change in monkeys and apes. I will first show that humans and our closest living relatives, chimpanzees and bonobos, exhibit similar ontogenetic shifts in cognitive skills during their juvenile period. Second, I will present an overview of a project aiming to disentangle the origins of individual variation in rhesus macaque cognition, focusing on variation across the lifespan. Such studies of cognitive development in other species can provide new insights into the origins of human cognition.

Sahand Negahban & Alex Rosati
Sahand Negahban: Assistant Professor in the Statistics Department at Yale University, Alex Rosati: Assistant Professor in the Department of Human Evolutionary Biology at Harvard

Professor Negahband’s research focuses on development of theoretically sound methods, which are both computaionally and statistcally efficient, for extracting information form large datasets. A salient feature of his work has been to understand how hidden low-complexity structure in large datasets can be used to develop computationally and statistically efficient methods for extracting meaningful information for high-dimensional estimation problems. His work borrows from and improves upon tools of statistical signal processing, machine learning, probability and convex optimization.

Professor Rosati’s research explores the evolutionary origins of the human mind. She compares how humans and other primates think about the world in order to understand what cognitive capacities are unique to humans, as well as how these capacities emerged. She integrates experimental methods from psychology aimed at teasing apart the mechanisms supporting behavior, with theoretical ideas from biology concerning the evolutionary function of different skills.  Her current research interests include decision-making and executive function, social cognition, species- and individual-variation in cognition, and comparative cognitive development across the lifespan.