Our upcoming event for the Statistics Department Colloquium Series is scheduled for this Monday, April 4th from 12:00 – 1:00pm (ET) and will be an in person presentation in room 316 of the Science Center. The speaker is Lucas Janson who is an Assistant Professor of Statistics at Harvard University.
Title: Controlled Discovery and Localization of Signals via Bayesian Linear Programming (BLiP)
Our upcoming event for the Statistics Department Colloquium Series is scheduled for this Monday, April 25th from 12:00 – 1:00pm (ET) and will be an in person presentation in room 316 of the Science Center. The speaker is Yury Polyanskiy who is an Associate Professor in the Department of Electrial Engineering and Computer Sciences at Massachusetts Institute of Technology.
Title: Empirical Bayes estimators for Poisson and normal means
Abstract : We consider the classical problems of estimating the mean of an n-dimensional normally (with...
Our upcoming event for the Statistics Department Colloquium Series is scheduled for this Monday, April 18th from 12:00 – 1:00pm (ET) and will be an in person presentation in room 316 of the Science Center. The speaker is Tselil Schramm who is an Assistant Professor of Statistics at Stanford University.
Title: Testing thresholds in high-dimensional random geometric graphs
Abstract: We study random geometric graphs in the high-dimensional setting, where the dimension grows to infinity with the number of...
Our upcoming event for the Statistics Department Colloquium Series is scheduled for this Monday, April 11th from 12:00 – 1:00pm (ET) and will be a virtual presentation over Zoom. The speaker is Deborah Marks who is an Associate Professor of Systems Biology at Harvard University.
Our next event for the Statistics Department Colloquium Series is scheduled for this Monday, March 28th from 12:00 – 1:00pm (ET) and will be an in person presentation in room 316 of the Science Center. The speaker is Andrew Nobel who is a Robert Paul Ziff Distinguished Professor in the Department of Statistics and Operations Research at University of North Carolina.
Title : Stationary Optimal Transport with Applications to Graph Alignment
Abstract : Optimal transport seeks to find couplings of two given distributions with minimum expected cost. This...
Our next event for the Statistics Department Colloquium Series is scheduled for this Monday, March 21st from 12:00 – 1:00pm (ET) and will be an in person presentation in room 316 of the Science Center. The speaker is Yuan Liao who is an Associate Professor of Economics at Rutgers University.
Title : Neural Network Inference on Nonparametric Conditional Moment Restrictions with Weakly Dependent Data
Abstract : Neural Networks (NNs) are nonlinear sieves that can approximate nonlinear functions...
Our next event for the Statistics Department Colloquium Series is scheduled for this Monday, March 7th from 12:00 – 1:00pm (ET).
The speakers are Xiao-Li Meng: the Whipple V. N. Jones Professor of Statistics at Harvard University, and the Founding Editor-in-Chief of Harvard Data Science Review and Sham Kakade: a Gordon McKay Professor of Computer Science & Statistics at Harvard University....
Zoom - please contact monestime@fas.harvard.edu for more information
Title : Non-parametric exploration in multi-armed bandits
Abstract : The multi-armed bandit model is useful for sequential resource allocation tasks in a stochastic environment, e.g., the design of a recommendation algorithm or an adaptive clinical trial. This simple model also captures the exploration/exploitation dilemma that is central in more structured reinforcement learning problems. The two most famous approaches to MABs, namely Upper Confidence Bounds and Thompson Sampling, share the need for some prior...
Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Likelihood theory for high-dimensional inference
Abstract:
In this talk we consider to what extent classical methods of inference based on the likelihood function can be extended to high-dimensional settings, and what...
Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression
Abstract:
Generalized linear regressions are linear combinations of many regressors that set a residual orthogonal to the regressors. These include objects that are nonparametrically estimated in generalized linear models. We give debiased machine learners of parameters of interest that depend on generalized linear...
Title:
Title:
Title:
Title:
Title:
Title: