COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > New directions in solving structured nonconvex problems in multivariate statistics

## New directions in solving structured nonconvex problems in multivariate statisticsAdd to your list(s) Download to your calendar using vCal - Rahul Mazumder (Massachusetts Institute of Technology)
- Tuesday 06 March 2018, 11:00-12:00
- Seminar Room 2, Newton Institute.
If you have a question about this talk, please contact info@newton.ac.uk. STS - Statistical scalability Nonconvex problems arise frequently in modern applied statistics and machine learning, posing outstanding challenges from a computational and statistical viewpoint. Continuous especially convex optimization, has played a key role in our computational understanding of (relaxations or approximations of) these problems. However, some other well-grounded techniques in mathematical optimization (for example, mixed integer optimization) have not been explored to their fullest potential. When the underlying statistical problem becomes difficult, simple convex relaxations and/or greedy methods have shortcomings. Fortunately, many of these can be ameliorated by using estimators that can be posed as solutions to structured discrete optimization problems. To this end, I will demonstrate how techniques in modern computational mathematical optimization (especially, discrete optimization) can be used to address the canonical problem of best-subset selection and cousins. I will describe how recent algorithms based on local combinatorial optimization can lead to high quality solutions in times comparable to (or even faster than) the fastest algorithms based on L1-regularization. I will also discuss the relatively less understood low Signal to Noise ratio regime, where usual subset selection performs unfavorably from a statistical viewpoint; and propose simple alternatives that rely on nonconvex optimization. If time permits, I will outline problems arising in the context robust statistics (least median squares/least trimmed squares), low-rank factor analysis and nonparametric function estimation where, these techniques seem to be promising. This talk is part of the Isaac Newton Institute Seminar Series series. ## This talk is included in these lists:- All CMS events
- Featured lists
- INI info aggregator
- Isaac Newton Institute Seminar Series
- School of Physical Sciences
- Seminar Room 2, Newton Institute
- bld31
Note that ex-directory lists are not shown. |
## Other listsCambridge Social Ontology Group (CSOG) Cancer Research UK Cambridge Institute Imaging Seminars Cambridge Mathematics Placements (CMP) Seminars## Other talksJoinings of higher rank diagonalizable actions Babraham Lecture - Deciphering the gene regulation network in human germline cells at single-cell & single base resolution Multi-model and model structural uncertainty quantification with applications to climate science CANCELLED-Open tools in Marchantia for plant bioengineering work and as a platform for elucidating morphogenesis Simulating Neutron Star Mergers Southern Africa; Northern Cape |