Today, machine learning is responsible for most of what we perceive as the personalization of the web: automatic recommendations for movies (Netflix) or music (Spotify,Last.fm), personalized search results based on your recent searches or email (Google), automatic credit card fraud detection (Chase), social network friend identification (Facebook,Linked-in), and, of course, …
We consider the problem of determining server schedules in multi-class service systems under uncertainty in the customer volumes. Common practice in such systems is to first identify server staffing levels that meet the quality of service targets, and then determine schedules for the servers that cover these staffing requirements. We …
In photon-limited image reconstruction, observations can be modeled as y~Poisson(f), where f:=exp(g) is the intensity of interest and g is the log-intensity. Previous work in this area has considered applying regularizers such as the total variation semi-norm to either f or to g:=log(f). The former is less stable at very …
The talk will discuss recent advances in the analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, as well as application of such estimation to climate science. Analysis of estimation error for regularized problems needs to consider four aspects: the norm, the …
This talk reviews some recent work on (unpenalized) linear re- gression M-estimators in high-dimensions. Extending the seminal work of Peter Huber, Steve Portnoy and others to the setting where n, the number of ob- servations, is large and comparable to p, the number of predictors, we obtain updated results for …
Proteins can be thought of as the machines inside cells, participating in nearly all biological processes. By physically binding to and interacting with one another, proteins form pathways dedicated to specific functions and vast networks. Mapping these pathways is critical for understanding how their disruption causes disease, but biological experiments …
In many scientific disciplines, new technologies are enabling researchers to obtain measurements of unprecedented scale and resolution. These huge data sets present many new challenges and opportunities for the experimental scientists generating the data and for researchers like those in the SILO community developing data analyses. In this talk, three …
Consider a generic r-dimensional subspace, and suppose that we are only given projections of this subspace onto small subsets of the canonical coordinates. In this talk I will show the necessary and sufficient conditions on such subsets to guarantee that there is only one r-dimensional subspace consistent with all the …
With the recent data revolution, statisticians are considering larger datasets, more sophisticated models. As a consequence, it is important to seek methods that are computationally efficient as well as statistically powerful. We will show how hypotheses from theoretical computer science on the hardness of some problems in average case can …